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Changes in visual search patterns as an indication of attentional narrowing and distraction during a simulated high-speed driving task under increasing levels of anxiety

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Changes in visual search patterns as an indication of attentional narrowing and distraction during a simulated high-speed driving task under increasing levels of anxiety
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Janelle, Christopher M
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English
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x, 242 leaves : ill. ; 29 cm.

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Anxiety ( jstor )
Cognitive psychology ( jstor )
Control groups ( jstor )
Eyes ( jstor )
Information search ( jstor )
Mental stimulation ( jstor )
Paradigms ( jstor )
Saccades ( jstor )
Sports psychology ( jstor )
Visual perception ( jstor )
Dissertations, Academic -- Health and Human Performance -- UF ( lcsh )
Health and Human Performance thesis, Ph. D ( lcsh )
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bibliography ( marcgt )
non-fiction ( marcgt )

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Thesis:
Thesis (Ph. D.)--University of Florida, 1997.
Bibliography:
Includes bibliographical references (leaves 203-222).
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Typescript.
General Note:
Vita.
Statement of Responsibility:
by Christopher M. Janelle.

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CHANGES IN VISUAL SEARCH PATTERNS AS AN INDICATION OF ATTENTIONAL NARROWING AND DISTRACTION DURING A SIMULATED HIGH-SPEED DRIVING TASK UNDER
INCREASING LEVELS OF ANXIETY






By

CHRISTOPHER M. JANELLE

















A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

1997













ACKNOWLEDGMENTS

There are many people to whom I am indebted for their guidance and patience

throughout my doctoral education and especially during the dissertation process. I would like to begin by thanking my wife, Carol, and my little buddy, Matthew, for their inspiration and understanding over the past four years. Words cannot express the love and appreciation I have for you both. Similarly, I would like to express my gratitude to my parents, Jean and Fran Janelle, for their support and encouragement during my graduate education and throughout my life. The value system and work ethic they instilled in me are what made this possible.

A very special thank you goes to my mentor and dissertation chair, Dr. Robert N. Singer. His scholarly example and practical lessons have greatly enhanced my professional and personal development. He has truly embodied the term "mentor" by providing me with the tools and opportunities needed to develop into a young scholar while adding constructive criticism and a sincere pat on the back when needed. 11is influence will always be greatly appreciated.

I would like to express my sincere thanks to my committee members, Dr. James H. Cauraugh, Dr. Ira Fischler, Dr. Milledge Murphey, and Dr. L. Keith Tennant, for their support and helpful comments in the completion of this project. In addition to the dissertation experience, each has provided much in their own way to my development and



ii







for that I am grateful. The many experiences I have shared with each of you, both academically and otherwise, will not be forgotten.

This study would not have been possible without the willingness to participate and generosity of Dr. Mark Williams who allowed me to use his eye-tracking equipment and provided interesting ideas and conceptual contributions during the formative stage of this project. Furthermore, I would like to acknowledge the technical assistance of Mark Tillman, Luis Maseda, and Dr. Jeff Bauer who helped put everything in motion. Also, I am thankful to Beth Fallen, Wisug Ko, and Dr. Andrea Behrman for helping with data collection, reduction, and analysis.













TABLE OF CONTENTS


Fne
ACKNOWLEDGEMENTS ............................................................ ii

LIST O F TA B LE S ...................................................................... vii

LIST OF FIGURES ..................................................................... viii

A B ST R A C T .............................................................................. ix

CHAPTERS

I INTRODUCTION ..................................................... 1

Attentional Narrowing ................................................. 3
D istraction .............................................................. 12
Arousal and Anxiety ................................................... 15
A More Comprehensive Next Step .............................. ... 18
Statement of the Problem ............................................. 22
H ypotheses ............................ ................................. 23
Definitions of Terms ................................................... 29
A ssum ptions ............................................................ 32
Significance of the Study .............................................. 33

2 REVIEW OF LITERATURE ........................................ 37

Stress and Human Performance ...................................... 39
Anxiety, Arousal, and Attention ..................................... 63
V isual A ttention ........................................................ 79
Visual Attention and Driving ......................................... 95
Visual Attention and Sport ........................................... 112
Summary and Future Directions ...................................... 115
Visual Search as an Indicator of Distraction
and/or Narrowing .................................................... 118








iv








3 METHODS ............................................................. 121

P articipants ............................................................. 12 1
Instruments and Tests ................................................. 122
Measurement Recording Devices .................................... 126
P rocedure ................................................................ 130
D ata A nalysis ........................................................... 136

4 R E SU L T S ............................................................... 139

Anxiety and Arousal .................................................... 139
Performance Data ....................................................... 142
Visual Search Data ...................................................... 151
Multiple Regression Analyses ......................................... 157
Manipulation Checks ................................................... 160

5 DISCUSSION, SUMMARY, CONCLUSIONS,
AND IMPLICATIONS FOR FURTHER RESEARCH ......... 162

D iscussion ................................................................ 164
Visual Search Data ...................................................... 175
Findings Which Contradict and Augment
Previous Research ..................................................... 180
Sum m ary .................................................................. 193
C onclusions .............................................................. 195
Issues for Future Consideration ....................................... 196
A Final Comment ........................................................ 201

REFERENCES ............................................................................ 203

APPENDICES

A COWETITIVE STATE ANXIETY INVENTORY 2
(C SA I-2) ................................................................. 223

B INFORMED CONSENT FORM .................................... 225

C PRE-RACE INSTRUCTIONS ........................................ 227

D FAMILIARIZATION SESSION INSTRUCTIONS ............... 230

E PRACTICE SESSION INSTRUCTIONS ........................... 233

F CONTETITION SESSION INSTRUCTIONS ..................... 235

G POST-EXPERE"ENT CONPvIENTS ............................... 237

H PEARSON PRODUCT-MOMENT
CORRELATION COEFFICIENTS ............................... 239


v








BIOGRAPHICAL SKETCH ............................................................ 242
























































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LIST OF TABLES

Table Fne

3.1 Experim ental design ................................................................. 136

4.1 Cognitive Anxiety Levels for Each Group Across Sessions 1-3 ............. 140

4.2 Change from Baseline HR for Each Group Across Sessions 1-3 ............ 142

4.3 Driving Performance (Lap Speed) ................................................ 145

4.4 Number of Major Driving Errors ................................................. 147

4.5 Mean Response Time Across Sessions 1-3 ..................................... 149

4.6 Mean Number of Peripheral Light Misidentifications ....................... 151

4.7 Number of Saccades to Peripheral Stimuli ............ ........................ 153

4.8 Number of Fixations to peripheral Locations Across Sessions 1-3 ..................................................................... 155

4.9 Stepwise Multiple Regression Analysis Predicting Lap Speed with Activation Data Across Sessions 1-3 .................................. 158

4.10 Stepwise Multiple Regression Analysis Predicting Response Time with Activation Data Across Sessions 1-3 .................................. 159

4.11 Stepwise Multiple Regression Analysis Predicting Misidentifications of Peripheral Stimuli with Activation Data Across Sessions 1-3 ......... 159

4.12 Stepwise Multiple Regression Analysis Predicting Exogenous Saccades with Activation Data Across Sessions 1-3 ....................... 160


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LIST OF FIGURES

Figure Page

4.1 Changes in cognitive anxiety for each group across sessions 1-3 .......... 141

4.2 Change in HR from baseline rates for each group during sessions 1-3 ..................................................................... 143

4.3 Lap speed for each group across sessions 1-3 .................................. 146

4.4 Number of major driving errors for each group across sessions 1-3 ........ 148

4.5 Mean response time across sessions 1-3 ....................................... 150

4.6 Mean number of peripheral light misidentifications ........................... 152

4.7 Number of saccades to peripheral stimuli across sessions 1-3 ............... 154

4.8 Number of fixations to peripheral locations across sessions 1-3 ............. 156























viii














Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

CHANGES IN VISUAL SEARCH PATTERNS AS AN INDICATION OF
ATTENTIONAL NARROWING AND DISTRACTION DURING A
SIMULATED HIGH-SPEED DRIVING TASK UNDER INCREASING LEVELS OF ANXIETY By

Christopher M. Janelle

August, 1997

Chairperson: Robert N. Singer Major Department: Health and Human Performance

The purpose of this investigation was to examine the influence of distraction on the attentional narrowing construct in the context of a dual task driving simulation under varying levels of anxiety. Forty-eight women were randomly assigned to one of six experimental conditions: distraction control, distraction anxiety, relevant control, relevant anxiety, central control, and central anxiety. Those assigned to central conditions only performed a driving task while the other four groups were required to identify peripheral lights in addition to driving. Those in anxiety conditions were exposed to increasing levels of anxiety which was manipulated by instructional sets. All



ix








participants completed three sessions consisting of 20 trials each during which measures of cognitive anxiety, arousal, visual search patterns, and performance were taken.

Data indicated that as those in dual task conditions reached higher levels of anxiety, their ability to identify peripheral fights become slower and less accurate. Furthermore, the ability to drive for those in the distraction and central groups was impaired at high levels of anxiety. The decrease in driving proficiency for those in the distraction anxiety condition was highly associated with changes in visual search patterns which became more directed toward peripheral locations. In the central anxiety condition, driving proficiency was influenced by an increased tendency to make minor errors which could be attributed to a more cautious driving style when highly activated. Overall, performance on both central and peripheral tasks was worse for those in the distraction anxiety condition during the period of highest anxiety. Furthermore, visual search patterns were more eccentric during this session for this group.

Results suggest that drivers who are highly anxious and aroused experience an altered ability to process peripheral information at the perceptual level, leading to a decrease in attention resources available for the processing of central information. In addition, it appears that this effect is amplified when distractors as well as relevant cues are present in peripheral areas. Implicated in the study is the role of visual search patterns and distractors in the dual task context. Suggestions are made to revise the current notion of attention narrowing to include the role of distraction as a contributor to performance variability.



x












CHAPTER I

INTRODUCTION

Anyone associated with sport as either an athlete, coach, or spectator, can remember instances in which the pressure of competition transcended the typical commentary that it was "just a game". Sport is replete with occasions such as a crucial free throw, a clutch base hit, a game winning field goal, or a breathtaking lap at the finish fine, in which athletes either overcome the excessive demands of the moment and perform at their highest levels or choke under the extreme circumstances of the situation. More often than not, it is the ability to maintain concentration when faced with these stressors that determines the outcome of sport contests. However, even the greatest athletes occasionally succumb to these inordinate demands, causing sport psychologists to question why this occurs and what mechanisms contribute to diminished performance.

It has been suggested that the ability of athletes to execute effectively in

exceptionally stressful environments is related to the impact of arousal and anxiety on the capability to maintain concentration. Though a number of researchers have suggested that excessive stress influences information processing capabilities by overloading the limited attention. resources available, much evidence provided to support this claim is anecdotal or observational in nature (Moran, 1996). Ignoring the underlying mechanisms



I







2
responsible for general changes in performance renders it impossible to prescribe competent interventions that specifically address the mechanisms which are being affected.

The paradigm shift in the study of cognitive psychology that occurred in the late 1950s and early 1960s brought with it a greater understanding of the specific processes that are involved with attending to and processing information. However, the research has been criticized due to its reductionistic nature. Ignored have been other relevant factors, particularly emotions that influence attentional processes and subsequent achievement (Kremer & Scully, 1994; Moran, 1996). By not studying the interaction of emotions, attention, and performance, the generalizability of research on attention has been somewhat limited. Thus, much still needs to be understood about dynamic sport settings in which attentional flexibility is crucial under conditions of severe time constraints and the stress associated with the competitive drive to win.

Of interest here is the peripheral (or attentional) narrowing phenomenon which has been reported to occur under high stress levels (Easterbrook, 1959). Though intriguing, and attracting much research interest to the present day, the underlying mechanisms responsible for the narrowing (or tunnel vision effect) which presumably occurs in stressful situations remain a mystery. Questions are still unanswered regarding the specific components of the stress response (i.e., cognitive or somatic anxiety, and/or arousal) that influence performance. Specifically, does narrowing occur due to heightened levels of cognitive anxiety, somatic anxiety, mere arousal, or some combination of these factors?

Another factor that has contributed to the confusion is that sport psychology researchers have been reluctant to give up the notion that the Inverted-U hypothesis







3

(Yerkes-Dodson, 1908) is the one and only description of the stress/performance relationship. However, contemporary models have been proposed that address the specific components of stress and prescribe testable hypotheses that are quite different from the very general Inverted-U description of the relationship of stress with performance.

Furthermore, the specific aspects of performance (i.e., stimulus detection and discrimination, response time, response accuracy, and others) that are influenced by changes in affective states have received relatively little empirical investigation due to the favoring of more easily understood global performance measures. As mentioned, by failing to address the specific parameters that are impacted by stressful stimuli, it is impossible to understand more precisely what is happening; and therefore, what to do about it.

Finally, many of the performance changes in stressful environments that have been attributed to attentional narrowing could possibly be explained in the context of distraction. In spite of their obvious application to understanding sport performance, the study of attentional narrowing and distraction in the context of dynamic sports is nonexistent.

As may be evident, advancement beyond current understandings of the

stress/performance relationship is warranted for both theoretical and practical reasons. Thus, my intent was to investigate specific affective factors that influence attentional parameters and, ultimately, performance in an ecologically valid dual task situation under stressful circumstances. To provide further description of the specific issues to be






4

addressed and to Justify the intended experiment, background information on the topics of interest follows.

Attentional Narrowing

It has been suggested that the ability to attend to, select, and process the most critical cues in a situation is one of the most important skills required for high level performance in sport (e.g., Abernethy, 1993). In support of this idea, experts have consistently exhibited what has been called a "cognitive advantage" over less skilled participants, being able to process the same information in a more efficient and effective manner (Starkes & Allard, 1993). Though this is interesting and valuable information for both cognitive and sport psychology researchers, the ability to demonstrate this cognitive advantage has rarely been investigated under imposed stressful states in a realistic sport context or other meaningful situation. However, an early theory that directly addressed the ability to select cues and use them effectively under different emotional conditions is the cue-utilization hypothesis described by the concept of attentional narrowing.

Easterbrook (1959) produced the most influential article on the topic of cue utilization based on the findings of Bahrick, Fitts, and Rankin (1952) and others (e.g., Bruner, Matter, & Papanek, 1955; Callaway & Dembo, 1958; Callaway & Thompson, 1953; Eysenck, Granger, & Brengelman, 1957; Granger, 1953). Easterbrook's primary theoretical contribution was the notion that as level of arousal increased to a certain point, performance in a dual-task situation would be variable between the two tasks. Specifically, he suggested that with an increase in activation to moderate levels, central task achievement would be facilitated due to the blocking of irrelevant cues in the






5
periphery from being processed. Furthermore, he postulated that at this moderate level, performance in tasks requiring less of a central focus (i.e., a peripheral focus) would deteriorate due to a blocking of these cues. Finally, performance in central tasks would be expected to deteriorate if arousal level reached a heightened state in which the funneling effect prohibited attention to relevant cues that are integral to performance of the central task. In other words, Easterbrook (1959) suggested that the degree of facilitation or disruption caused by emotional arousal is dependent on the range of cues needed to perform a task effectively and how those cues are attenuated by emotional states.

Unfortunately, relatively few investigations have been undertaken in sport settings to examine the effects of peripheral narrowing, or if this phenomenon exists. This is surprising considering that typical sport situations, especially at higher levels of expertise, often occur in extremely stress-provoking environments. In one of the only studies done in the context of sport, Landers, Wang, and Courtet (1985) investigated peripheral narrowing with experienced and inexperienced rifle shooters. The central task was a target shooting task and the peripheral task was an auditory detection task. Although there were no differences found in secondary task performance between the experienced and inexperienced shooters, both groups shot worse under high stress conditions.

Also with relevance to sport, two studies were conducted by Williams, Tonymon, and Andersen (1990, 1991) that substantiated Andersen and Williarns'(1988) model of athletic injury. In the model, Andersen and Williams (1988) indicate that a possible predisposition to athletic injuries may be precipitated by elevated levels of life stress that result in an inability to attend to threatening peripheral stimuli. Support for this possibility






6

was provided by Williams et al. (1990, 199 1) who showed that decrements in the ability to detect peripheral cues were found to occur while individuals performed Stroop tasks under stressful conditions. Based on their conclusions, the researchers suggested that attention narrowing may be a dispositional factor that predicts athletic injuries because athletes are unable to notice potentially dangerous peripheral stimuli such as other players, dangerous terrain, and the like.

Though not directly sport-related, other perceptual-motor activities have been investigated with respect to the ability to attend to central and peripheral dual tasks. Of these, perhaps the most relevant to sport is driving an automobile (unfortunately, many highway drivers forget that it is not a sport!). While driving, there is a limited amount of attention resources that can be devoted to an almost infinite number of stimuli at any point of time. As the task of driving becomes more complex due to decreased visibility, bad weather, heavy traffic, mechanical malfunction, sudden unexpected obstacles, fatigue, and other factors, the automaticity of driving becomes less instinctive and demands more attention resources (Shinar, 1978). In these conditions, drivers may experience information overload and may be more likely to place themselves in possibly risky situations.

During normal driving, the driver tends to focus on the central task of keeping the vehicle "on the straight and narrow" so to speak, maintaining control of the vehicle based on the constraints of the driving environment (e.g., speed limits and lane markers). However, when confronted with an object or event that is not in the central (or foveal) field of vision, the eyes are normally moved from the central task to focus more directly on






7
the information that has been attended to in the periphery. Based on the information provided by the newly attended stimulus, a decision must be made regarding whether or not to change driving behavior, These alterations occur both in serial and in parallel depending on the specific situation presented (Schneider & Shiffiin, 1977; Shiffrin & Schneider, 1977). To make matters more complicated, all of these processes are often limited by extremely restrictive temporal constraints (Shinar, 1978).

Recent research has been directed toward understanding, more fully, the ability of drivers to extract meaningful information from signals along the roadway. In particular, many studies have been done on the demands of the external environment while driving, such as the perception and processing of road signs.

Hughes and Cole (1988) investigated the effect of attention demands on eye movement behavior during simulated road driving. They attempted to assess how a driver's performance was effected by purposely directing attention to particular features of the road environment under single and dual task conditions. Results showed that across groups, 25% of the fixations were located at the actual focus of expansion while 80% of the remaining fixations were centered within 6 degrees of the focus of expansion. Therefore, results suggest that if road signs are located beyond the 6' point in the display, they will probably not be perceived. Also, increasing task specificity resulted in more fixations to the left part of the display (the area where most signs were posted) with a corresponding decrease infixations to the center of the display. Furthermore, the addition of the dual task paradigm resulted in two predominant effects on eye movements. First,






8

eye fixations tended to move closer to the central region. Second, the distance of peripheral fixation also moved closer to the focus of expansion.

Therefore, it can be concluded that in the typical dual task condition which

requires increased attention resources, there is insufficient spare resources to perform the peripheral task without more fixation resources. The additional demand of the secondary task not only necessitates more fixations to the region of the task, but also reduces the extent to which the rest of the visual display is searched. Though not suggested by the researchers, these results could be accounted for in the context of attention narrowing and/or distraction.

A similar study was conducted by Luoma (1988) to examine the types of roadway landmarks that are perceived and remembered better than others. As may be evident from the results of Hughes and Cole (1988), drivers do not perceive nearly all of the traffic signs that they encounter, even in situations where they have been precued to look for the signs. In situations imposing increasing demands and challenges to the driving task, the perception of signs is even less than in "normal" driving conditions.

Luoma (198 8) tested the idea that the more casual the perception or the larger the target signs, peripheral vision is used to a greater extent. However, an important function of peripheral vision is to identify targets of importance to the driving task and, if the situations warrants, direct focal vision to the sign. To investigate these ideas, participants actually drove a 50 Ian route while outfitted in eye movement monitoring equipment. Results indicated that correct perception only occurred, for the most part, when the target was fixated foveally. Also, whether the sign was perceived or not depended heavily upon






9

the relevance of the sign to the driving task. For example, 100% of all speed limit targets were perceived foveally and were recalled while signs such as pedestrian crossings, roadside advertisements, and houses were perceived much less, if at all. in fact, no subjects recalled passing "pedestrian crossing" signs even though 25% of them fixated on it. It appears that the processing devoted toward identifying the signs was dependent upon the relevance of the sign to the actual driving task and its informativeness.

Perhaps the most relevant study reported to date to examine the processing of visual stimuli in both central and peripheral fields was conducted by Nfiura (1990). The primary purpose was to assess changes in the useful field of view (UFOV: the information gathering area of the display) under situations of varying task demands and to determine the corresponding variation in the acquisition of visual information that accompanied these changes. Mackworth (1976) has suggested that the UFOV will vary with changes in the situational characteristics or specific demands of the environment. The study was conducted under actual driving conditions in which the subject had to navigate along a roadway, in daylight conditions.

Results showed that RT to peripheral lights increased as the situational demands increased. Furthermore, response eccentricity became shorter, suggesting that fixations had to occur closer to the actual target location to acquire the necessary information. In general, this suggests that peripheral visual performance is impeded by an increase in situational demands. Specifically, it appears that the UFOV narrowed at each fixation point, and the latency of each fixation lengthened. Also, the detection of targets required a greater number of eye movements in more demanding driving situations.






10

To explain these findings, Mura (1990) postulated that the depth of processing of an object in focus increases as the situational demands increase. Specifically, the latency period of the eye movements following fixation on a target lengthens as the demands increase. In more demanding situations, when a narrower UFOV exists, information pickup at the fixation point appears to be slower, causing a delay in the attentional switching capabilities of the driver. Other evidence (Mura, 1985) indicates that with lower demands, the fixation points shift to the inner area of the UFOV while during highly demanding situations, fixations shift toward the outer part of the UFOV. Thus, as a result of the deeper processing that occurs at each fixation point, participants attempt to acquire information more efficiently in the periphery while using a smaller UFOV. Another hypothesis is that as demands increase, they develop a stronger tendency to search for information in the periphery, a phenomenon referred to as "cognitive momentum", and a possible adaptation of the system to utilize attentional resources in the most efficient manner to deal with the increase in demands (Miura, 1986).

Though interesting and conceptually valuable, Miura's (1985, 1986, 1987, 1990) work fails to take into account what might be a primary influence on the decrement in peripheral performance and the apparent narrowing of the UFOV. Though not mentioned in any of his papers, a possible explanation for these findings can be attributed to the increase in arousal and anxiety that accompanies tasks that increase in complexity and demands (Easterbrook, 1959). Although eye movements have been recorded in a variety of real world and simulated driving situations, researchers have not attempted to examine other affective inputs to the system that may account for differences in performance.






11

Furthermore, in Murals (1990) study, as well as others, performance in the central driving task was not recorded.

Like normal driving, the sport of auto racing demands the coordination of an

extensive repertoire of perceptual and motor skills. However, the performance difficulty of these skills is significantly compounded by the competitive nature of the sport. In addition to mastering typical driving skills, the shear speed of the car requires split-second decision-making and intense concentration on the most relevant cues for the entire duration of the race. An ill-advised momentary attention shift or distraction can be (and often is) catastrophic under these circumstances. Unfortunately, virtually no attempt has been made to empirically assess these factors in auto racing.

As may be evident from the sparse research that has been conducted related to

sport and driving, no study has addressed the issue of attention narrowing in the context of dynamic, reactive sport environments. However, perhaps the theoretical mechanisms that underlie results from laboratory tasks and the few sport situations that have been studied are common to all dynamic sports as well.

The reduction in the range of cue utilization was originally explained in the context of both Hull's (1943) Drive theory and the Yerkes-Dodson (1908) Inverted-U hypothesis. However, the cue utilization hypothesis can be more accurately accommodated with more recent attention capacity (or resource) theories (Kahneman, 1973: Wickens, 1984) which propose a limit in the resources available to attain optimal attention. Proponents of this view (e.g., Landers, 1980) suggest that one primary feature of high arousal levels is a narrowing of attention because the allocation policy is likely to shift away from the






12

periphery and toward the central area of a visual display. This notion has been supported by studies that indicate the probability of cues in central areas of a display to draw more attention resources 'creases under stressful situations (e.g., Hockey, 1970).

To summarize the attention narrowing point of view, stress (either arousal or anxiety produced) tends to overload the system, narrowing the range of stimuli that are perceived. When this occurs, information processing capabilities appear to operate in a dysfunctional manner. At the initial stages of perception, possibly various cues are ignored, never reaching later stages of processing. On the other hand, the actual informational value of the stimulus may not be utilized effectively due to an inability to distinguish the stimulus as relevant or irrelevant and respond accordingly, Thus, narrowing could be due to a dysfunction at the perceptual stage of processing and/or at the short term memory stage. Quite possibly, impairment occurs at both stages of information processing (Bacon, 1974; Hockey, 1970). However, the exact location of information processing dysfunction has not been substantiated. Furthermore, an alternative explanation for what happens to performance and attention allocation under stressful conditions is plausible.

Distraction

As described previously, the idea that consistently recurs as an explanation for performance changes in both central and peripheral tasks in stressful environments is a narrowing of the attention beam in which cues are somehow filtered from processing at either the perceptual or encoding stage of analysis. However, the influence of distractors in the context of peripheral narrowing has not been investigated, and the concept of







13

distraction has received very little attention from researchers. It seems logical, however, that the apparent narrowing of attention that occurs under stressful conditions could also be explained by the notion that anxious or aroused performers are more inclined to be distracted.

The lack of research directed toward understanding distraction is surprising

considering the need of people in many work entertainment, sport, and other situations to ignore distractors and focus only on the most critical cues in order to effectively perform the task. Examples of athletes and other performers who have been victimized by distraction are numerous (Moran, 1996), prompting Orlick (1990) to suggest that the need to avoid distraction is one of the most important mental skills required to be successful in sport.

Brown (1993) defines distraction as situations, events, and circumstances which divert one's mind from some intended train of thought or from some desired course of action. This definition is somewhat different from William James' (1890) original conceptualization of distraction which was directed toward the experience of distracting thoughts and being "scatter-brained". Each of these views of distraction can be more easily understood if categorized in the context of internal and external types of distractors (Moran, 1996). Internal distractors refer to mental processes that interfere with one's ability to maintain attention while external distractors are environmental or situational factors that divert attention from the task at hand. Wegner (1994) has postulated that because the mind tends to wander, an attempt is made to hold it in place by repeatedly checking to determine whether it has wandered or not. However, in this process, the mind






14

is inadvertently drawn to the exact thing that one is trying to ignore. He also suggests that when highly emotional, attentional resources are reduced, and the mind is inclined not only to wander away from where it should be attending, but is also diverted toward that which one is attempting to ignore.

The typical effect of distraction is a decrease in performance effectiveness. The most plausible explanation for this is that when one is distracted by either external or internal factors, there is a decrease in available attentional resources for the processing of relevant cues. Like attentional narrowing, this idea is consistent with the limited capacity models of attentional resources proposed in different forms by various attention theorists (e.g., Aliport, 1989; Kahneman, 1973; Shitfiin & Schneider, 1977). Because attentional capacity is limited, resources directed toward the processing of distractors reduce available resources for the processing of task-relevant information. This idea is supported by studies which have shown that distraction effects increase for complex rather than simple tasks and are greater as the simiAlarity of distractors to relevant cues increases (Graydon & Eysenck, 1989).

Though empirical evidence is scarce, many researchers have suggested that increases in emotionality (i.e., anxiety, worry, arousal) increase susceptibility to distraction. Numerous examples of evidence to support the notion that stress impedes performance due to distraction can be found in verbal accounts and behavioral observations of "choking" in competitive environments. Moran (1994, 1996) provides substantial anecdotal evidence that the impact of anxiety is the absorption of attentional resources which could otherwise be directed toward the relevant task. Similarly,






15

Baumeister and Showers (1986) suggest that increased worry causes attentional resources to be devoted to task-irrelevant cues. Furthermore, self-awareness theorists such as Masters (1992) suggest that under stress, not only is attention absorbed by irrelevant stimuli, but also the performance of normally automated skills becomes less automated as resources begin to be intentionally directed toward the process of the once-automated movement. Finally, Eysenck (1992) has provided empirical evidence that anxiety provokes people to detect stimuli which they fear, usually stimuli that diverts them from attending to relevant information. Unfortunately, the specific components of stress that influence attentional parameters have also been largely ignored.

Arousal and Anxiety

Due to increasing dissatisfaction with the Inverted-U hypothesis and other

theories, researchers attempted to analyze the stress response in greater detail as to its various components and to re-examine the stress/performance relationship. Perhaps the first scholars to approach the possibility of dissecting the general anxiety response were Liebert and Morris (1967) who identified two primary contributing factors to anxiety: worry and emotionality. In Liebert and Morris's view, worry consisted of cognitive concerns about one's performance while emotionality referred to the autonomic reactions to the performance environment. This concept strongly influenced Davidson and Schwartz's (1976) multidimensional model of anxiety. They were the first to use the terms "cognitive" and "somatic" anxiety and formulated their theory in the context of clinical applications. Thus, worry has become synonymous with cognitive anxiety and emotionality has become synonymous with somatic anxiety. These general characteristics






16
of the components of anxiety have held up under empirical investigation and appear to be manipulable independently (e.g., Schwartz, Davidson, & Goleman, 1978). Also, it is important to distinguish both components of anxiety from arousal. Though similar to somatic anxiety, arousal refers to the natural physiological indices of activation that are present within an organism at any time (Sage, 1984). In contrast, somatic anxiety refers to the perception of physiological arousal.

One problem with multidimensional anxiety theory is the two-dimensional

approach used to explain the effects of somatic and cognitive anxiety on performance. Specifically, the two-dimensional approach in analyzing results tends to neglect the interaction of the components of stress, treating them independently rather than in combination (Hardy & Fazey, 1987). According to the viewpoint of Hardy and his colleagues, any relatively comprehensive treatment of these components must treat them in an interacting, three dimensional manner. To improve the predictability and structure of the model, therefore, Hardy and Fazey (1987) developed a catastrophe model of anxiety and performance.

In an effort to advance understanding beyond the multidimensional approach to the study of the effects of anxiety and arousal on performance, Fazey and Hardy (1988) proposed a three-dimensional model of the relationship. Borrowing heavily from Thom (1975) and Zeeman (1976) who originally conceptualized the idea of catastrophes and then applied them to the behavioral sciences, respectively, Fazey and Hardy's (198 8) model is closest in form to the cusp catastrophe, one of the seven originally proposed






17

catastrophe models of Thom (1975). According to the cusp catastrophe model, changes in either cognitive anxiety or arousal, or both Will change performance in specific ways.

Hardy and Fazey (1987) state that of the two variables that determine behavior

(cognitive anxiety and arousal), cognitive anxiety is the "splitting factor", the variable that has the primary influence on performance level. The roles of cognitive anxiety and physiological arousal were chosen specifically to be able to evaluate testable hypotheses with respect to the anxiety/arousal/performance relationship. Specifically, when cognitive anxiety is low, the model predicts that physiological arousal will influence performance in an inverted-U fashion. However, when physiological arousal is high, high levels of cognitive anxiety will result in lower levels of performance. Finally, when physiological arousal is low, higher cognitive anxiety will lead to increases in performance.

Usually the manipulation of anxiety and arousal is carried out through a time-toevent paradigm in which assessments are taken at specified times leading up to a competition setting (Hardy, Parfitt, & Pates, 1994). For instance, assessments will be taken one week prior, two days prior, and then one hour prior to the competition. In this way, the time course of anxiety and arousal can be assessed. In other instances, levels of anxiety and arousal are manipulated through the use of both ego-threatening or other anxiety-producing instructional sets and through the use of exercise-induced arousal, respectively (Parfitt, Hardy, & Pates, 1995).

An obvious feature of the cusp catastrophe model of the anxiety/performance

relationship is the choice of physiological arousal rather than somatic anxiety as the normal factor. The primary reason for this choice is based on the notion that it is part of the






18

organisrds natural physiological response to anxiety-producing situations (Hardy, 1996). This belief is sufficiently well-established to be spoken of in the context of a generalized response within the competition setting. In other words, in competitive environments, performers usually show one or more signs of physiological arousal. Though the physiological response may be reflected in self-reports of somatic anxiety, the purely physiological index can encompass the individual task requirements, different situations, and other combinations of factors that override reports of somatic anxiety. Furthermore, physiological arousal changes tend to be reflected in changes of somatic anxiety while the converse is not the case (Fazey & Hardy, 1988; Hardy, 1996; Hardy & Fazey, 1987). Substantial support has been shown for the cusp catastrophe model of the anxiety performance relationship in seminal investigations of the model by Hardy and his colleagues (e.g., Hardy, Parfitt, & Pates, 1994).

One limitation, however, to the study of stress and performance in the context of any of the models described previously, is a lack of empirical explanation for the performance changes that are noticed in overly stressful situations. As mentioned, one specific cognitive mechanism that has been implicated, but has received limited empirical investigation in sport contexts, is the impact of anxiety and arousal on attention resources. Thus, a logical next step is to attempt to delineate these relationships in an effort to more thoroughly understand performance changes under stressful conditions.

A More Comprehensive Next Step

Though intriguing and receiving much anecdotal support in a variety of settings, the empirical interaction between the cognitive and emotional antecedents of the






19

stress/performance relationship remains largely unspecified. Furthermore, in light of recent dissatisfaction with the Inverted-U hypothesis of the anxiety/arousal/performance relationship, the underlying explanations originally forwarded by Easterbrook (195 9) may be somewhat obsolete. Specifically, although studies in which anxiety or arousal have been manipulated have shown support for the attentional narrowing phenomenon, none have examined the interactive effects of these emotional antecedents, nor have they designated one or the other as the primary contributor to the relationship. Furthermore, the role of distraction has received little or no investigation in this context, and an understanding of it could contribute greatly to the understanding of performance changes.

Paradoxically, it appears that perhaps there are two equally attractive explanations for the decrease in performance that occurs under high levels of stress. On one hand, proponents of the attentional narrowing argument would suggest that under high stress levels (either anxiety or arousal induced) the attentional field narrows to block out irrelevant cues, and then narrows further, blocking the processing of relevant information as stress continues to increase. On the other hand, proponents of the distraction argument would suggest that actually a widening of the attentional field occurs such that irrelevant or distracting cues receive more attention than when under lower stress levels. Evidently, a controversy exists unless in some way, both mechanisms could be working at the same time. Perhaps, an increase in anxiety and/or arousal results in a narrowing of the attentional field while at the same time, especially at higher levels of stress, it increases susceptibility to distraction. Many theories can account for how stress affects attention






20

and the eventual impact of attention variation on performance, but none address specifically why this phenomenon occurs.

As may be evident from the discussion of driving tasks, visual search has been used extensively to draw cognitive inferences regarding what information is being extracted and processed during eye fixations, a concept Viviani (1990) has termed the "central dogma" of visual search research. Though it is presently impossible to empirically prove the central dogma, most researchers agree that eye fixations do at least reflect cognitive processing. Assuming the dogma to be even partially true, if an attenuation of cues in the periphery is evident, the need to pick up crucial cues in the periphery during particular situations would necessitate an increase in scan path variability and fixation rate in order to compensate for peripheral narrowing. Furthermore, if distracting visual cues were actually introduced into the test environment, visual search strategies may be altered, resulting in increased fixation and processing of distracting stimuli and a reduction of attention resources available for central task performance.

Like normal driving, the sport of auto racing demands the coordination of an

extensive repertoire of perceptual and motor skills. However, the performance difficulty of these skills is significantly compounded by the competitive nature of the sport. In addition to mastering typical driving skills, the sheer speed of the car requires split-second decision-making and intense concentration on the most relevant cues. An ill-advised momentary attention shift or distraction can be (and often is) catastrophic under these circumstances. Thus the need to respond effectively in this type of a pressure-packed






21

activity is paramount. Unfortunately, no attempt has been made to empirically assess these factors in auto racing.

Viviani (1990) suggested that the central dogrna of visual search and cognitive inference would be valid if evidence for serial search is provided in particular tasks. According to Kahneman (1973), as arousal increases, task difficulty also increases. Under these circumstances, parallel (relatively automatic) processes tend to be modified by the organism, becoming more serial and attentive in nature (Duncan & Humphreys, 1989; Shiffiin & Schneider, 1977). As mentioned, the auto-racing environment is one is one in which drivers experience extremely high levels of arousal and anxiety. In this case, the ability to relate eye fixations to cognitive information processing is more valid than when parallel processing is dominant.

As mentioned, very limited research has been done to investigate any psychological phenomena with auto racing and none has been done to investigating driver's eye movements or other attention parameters that are critical to high performance in the fastest sport in the world. The selective and divided attention demands of race car driving render it an ideal task and environment to investigate attention mechanisms and the eyemovement parameters that underlie those mechanisms. Perhaps the first step that should be taken to better understand the attention capabilities necessary for effective race car operation is to evaluate the visual search patterns of drivers as they navigate the race course. By evaluating these parameters, it may be possible to assess whether the "software" advantages that appear to predispose athletes in other sports to reach higher levels of achievement are valid antecedents to high performance auto racing.






22

In light of these considerations, the primary objective of this study was to attempt to delineate the individual and interactive influence of arousal and cognitive anxiety on attention capabilities. In addition, it was anticipated that these attention alterations would result in behavioral changes that would, in turn, influence global performance indicators. Specifically, performance while undertaking (1) a central driving task and (2) a peripheral light identification task was investigated under various levels of cognitive anxiety. Furthermore, visual search patterns were assessed to ascertain whether perceptual factors (i.e., the search patterns themselves) contributed to the attention narrowing and/or distractibility phenomena.

In this manner, an attempt was made to isolate specific factors that might influence selective attention and the ability to divide attention between the central and peripheral tasks. Also, an attempt was made to determine whether visual search patterns were influenced by changes in both cognitive and physiological activation levels. By assessing specific dependent measures rather than simply global changes in affect, cognition, and performance, a clearer understanding of the interactive influence of these factors was acquired.

Statement of the Problem

In this experiment, a central driving task and a peripheral light detection task were used to assess the effects of anxiety (as manipulated by a time-to-event paradigm and anxiety-producing instructional sets) on performance over the course of familiarization, practice, and competition sessions. Performance-related variables included: (a) driving speed and accident propensity, (b) peripheral fight detection speed and accuracy, (c) visual







23

search patterns, and (d) physiological arousal. Determined was whether any anxietyinduced changes in performance were due to a narrowing of the attentional field, increased distractibility, or both.

Hypotheses and Pilot Study Results

The following hypotheses were tested in this investigation. The first set of

hypotheses was directed toward the manipulation of anxiety and the expected result of this manipulation on arousal levels. Rationale for the hypotheses is offered after all are proposed.

1. The use of the time-to-event paradigm and instructional sets will produce higher cognitive anxiety levels during the practice and competition sessions in the experimental groups (anxiety) than in the control groups (no anxiety) as measured by the CSAI-2 (Martens et al., 1990). The instructional sets used will be similar to those employed by Hardy et al. (1994) and will be used to manipulate levels of cognitive anxiety independent of somatic anxiety. These manipulations have been shown to be valid in both sport-specific (Hardy et aL, 1994) and other evaluative situations (e.g., Morris, Harris, & Rovins, 198 1). Furthermore, the time-to-event paradigm has been a reliable means of investigating temporal changes in anxiety associated with impending competitions (Hardy et al., 1994).

2. The increase in anxiety levels exhibited in the experimental groups will be

mirrored by an increase in physiological arousal (as measured by an increase in heart rate and pupil diameter size) in the practice and competition sessions. In addition, it is






24

hypothesized that cognitive anxiety and arousal levels will be highest immediately prior to the competition session due to the time-to-event and instructional set manipulations.

According to Lacey and Lacey's (195 8) autonomic response stereotype

hypothesis, the reaction to anxiety-producing thoughts and stimuli cannot be specified due to individual differences. However, if manifested in physiological changes, heart rate and pupil dilation measures are sensitive to increases in autonorrdc activity. In addition, heart rate has been used reliably in other tests of the catastrophe model of anxiety (e.g., Hardy et al., 1994). Furthermore, Abernethy (1993) has advocated the use of pupillometry as one of the most reliable measures of anxiety. Finally, because the test environment is static, such that the participant is not physically activated in any way, any changes in HR or pupil dilation across test conditions can be more readily attributable to emotional changes than if tested in a physically active situation.

The next set of hypotheses was directed toward the anticipated changes in performance that were expected to occur in the central and peripheral tasks.

1. For central task conditions (those in which only the central driving task is performed), driving performance (as measured by lap speed and the number of driving errors) will be similar for the control group and anxiety group in the familiarization session. However, during the second session, driving is hypothesized to be more proficient for the anxiety group than the control group. Finally, performance in the competition session will be better for those in the control group than those in the anxiety group.






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2. Those in the relevant groups, in which the central driving task will be

performed concurrently with peripheral fight identification of relevant stimuli, will exhibit similar proficiency on both tasks regardless of control or anxiety manipulations during the familiarization session. Driving skill during Session 2 (practice) is predicted to be facilitated for those in the anxiety group as opposed to the control group, but performance in the peripheral light detection task (as measured by reaction time and response accuracy) will be diminished due to a decrease in peripheral cue utilization. In the third test session (competition), those in the anxiety group will perform worse in both tasks due to a decrease in cue utilization.

3. For the dual task distraction conditions (those in which the central driving task will be completed concurrently with peripheral light detection of relevant stimuli while ignoring irrelevant peripheral lights), achievement in both tasks during the familiarization session will be similar for the anxiety and control groups. Central driving task proficiency during the second session will be facilitated for those in anxiety groups as opposed to control groups, but peripheral cue utilization changes will result in reduced performance on the peripheral light detection task during the same session for the anxiety group. In the third session, execution of both tasks will be worse for those in the anxiety condition as compared to control groups due to an increase in the narrowing of cue utilization as well as an increase in the distractibility of participants at high levels of anxiety.

4. Overall, achievement in the central driving task should be highest for the

central control group in the third test session due to no interference from anxiety or other attention-demanding stimuli (i.e. peripheral fights). The ability to detect peripheral lights







26
should be best for the relevant control group in the competition session due to the increased automation of the central task no interference from distractors, and no interference from anxiety changes. Furthermore, reaction time and detection accuracy for relevant peripheral lights in the distraction condition is expected to be similar in the familiarization session for anxiety and control groups. However, detection speed and accuracy will decrease for those in the anxiety group in the competition session due to an increase in distractibility.

These hypotheses were forwarded on the basis of previous conclusions from

studies of the attentional narrowing phenomenon (e.g., Bruner, Matter, & Papanek, 1955; Callaway & Dembo, 1958; Callaway & Thompson, 1953; Eysenck, Granger, & Brengelman, 1957; Granger, 1953), as well as a variety of anxiety models that indicate a moderate increase in activation to be beneficial to performance but a high level of activation to result in diminished achievement (e.g., Hardy & Fazey, 1987; YerkesDodson, 1908).

According to the attentional narrowing phenomenon, under moderate levels of

anxiety and arousal, the range of cues utilized will be decreased, blocking peripheral cues from being processed. Thus, central driving task proficiency will be facilitated by maintaining attentional focus on the most relevant cues while performance on the peripheral light detection task will be hindered (Easterbrook, 1959; Kahneman, 1973). However, as activation levels increase, a person is most likely susceptible to a further decrease in the range of cue utilization, blocking the processing of relevant cues (Easterbrook, 1959). Also, remaining attentional resources may be absorbed by the






27

increased propensity to be distracted by both internal factors (anxiety) and an increased propensity to process irrelevant external factors (distracting peripheral stimuli) (Moran, 1996; Wegner, 1994).

If activation levels reach extremes, this could eventually result in a catastrophic deterioration in effective execution (Hardy, 1996) of both central and peripheral tasks. Specifically, Hardy and Fazey's (1987) catastrophe model indicates that when a performer's cognitively anxiety and arousal reach high levels, performance will deteriorate in a dramatic fashion, not in a gradual manner as proposed by the Inverted-U hypothesis (Yerkes-Dodson, 1908).

The final set of hypotheses was directed toward the expected changes in visual search patterns that were expected to be exhibited by participants in response to changes in anxiety and arousal levels. Once again, at the completion of the proposed hypotheses, rationale will be presented.

1. Eye fixations for those in the central condition are expected to cluster closely around the point of expansion (within a 6' radius from the point of expansion) for both the control and anxiety groups.

2. In the relevant condition, fixations for the control groups should be focused more centrally (similar to the central condition) than for the anxiety group due to the ability of control participants to acquire peripheral stimuli information with peripheral vision. Correspondingly, those in the anxiety group will probably exhibit an increase in the number of fixations to the periphery in order to compensate for the reduction of peripheral vision due to anxiety.






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3. In the distraction condition, similar to the relevant condition, fixations for the control groups are expected to remain more centrally located in Sessions 2 and 3 due to the ability to discriminate relevant from irrelevant peripheral light stimuli with peripheral vision. However, the number of fixations to the periphery for those in the anxiety group will increase in Session 2 and then even more in Session 3 due to a narrowing of cue utilization and an inability to acquire peripheral information with peripheral vision, as well as the increased susceptibility to focus on distracting stimuli.

These hypotheses are based on findings from general studies of driver fixation tendencies as well as the previously mentioned hypotheses with respect to attention. narrowing and distraction. It has been repeatedly shown that 80-90% of drivers' fixations tend to cluster within 4-6' of the point of expansion in the visual display and that this tendency is enhanced under conditions of higher task complexity (Miura, 1985, 1990). These tendencies would be expected to hold for those in control groups that do not experience extremely high levels of anxiety and are not required to process peripheral input. However, under anxiety-producing conditions, the visual field is expected to narrow (Easterbrook, 1959), requiring an increased number of fixations to the periphery to acquire information that is normally acquired by peripheral vision.

Furthermore, it would appear that highly anxious and aroused participants will

increase the number of fixations to distracting stimuli. Mura (1986) has suggested that as driving demands increase, a stronger tendency to search for information in the periphery occurs. Accordingly, this is a possible adaptation of attention processing to deal with the increase in demands (Mura, 1987). In terms of distraction, resources (i.e., eye






29
fixations) directed toward the processing of distractors reduce available resources for the processing of task-relevant information. Graydon and Eysenck (1989) have shown that distraction effects increase for complex rather than simple tasks and are greater as the similarity of distractors to relevant cues increases. As the ability to distinguish relevant from irrelevant cues is diminished, the propensity to be distracted by irrelevant stimuli will likely increase along with the tendency to fixate on these stimuli.

Definitions of Terms

To standardize the terminology in this experiment, the following terms are defined:

Arousal is the process in the central nervous system that increases the activity in the brain from a lower level to a higher level, and maintains that higher level. The activation response is a general energy mobilizing response that provides the conditions for high performance, both physically and psychologically (Ursin, 1978).

Attention is "...the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought. Focalization concentration, of consciousness are of its essence. It implies withdrawal from some things in order to deal effectively with others" (James, 1890, pp. 403-404). Also, it has been described as a concentration of mental activity (Matlin, 1994; Moran, 1996).

Attentional. narrowing refers to the phenomenon in which, under increasing levels of stress, the range of cues utilized by an organism is reduced, resulting in an initial filtering of irrelevant or peripheral cues from processing, an increase in performance of central tasks, and a decrease in performance of peripheral tasks. As stress levels continue






30

to increase, both task- irrelevant as well as relevant cues begin to be attenuated from processing until performance in both peripheral as well as central tasks are disrupted (Easterbrook, 1959).

Cognitive anxiety is characterized by worry or the awareness of unpleasant

feelings, concerns about performance, and the inability to concentrate (Rotella & Lerner, 1993).

Cusp catastrophe model is a three-dimensional model that describes how one dependent variable can demonstrate both continuous and discontinuous changes in two other dependent variables. In the context of the catastrophe model of anxiety, the dependent variable is performance and the two independent variables are anxiety and arousal (Hardy, 1996).

Distraction refers to situations, events, thoughts, or circumstances that divert the mind from some intended train of thought and tend to disrupt performance (Brown, 1993; James, 1890; Moran, 1996).

Divided attention is characterized by the ability to attend to several simultaneously active messages or tasks, or to distribute attention effectively to simultaneous tasks that develops as a result of experience and practice (Eysenck & Keane, 1995; Hawkins & Presson, 1986).

Fixation refers to a pause in search during which the eye remains stationary for a period equal to or in excess of three video frames (120 ins) (Williams, Davids, Burwitz, & Williams, 1994).






31

Fixation location refers to the areas in the display in which the eye fixates during completion of a task (Williams, Davids, Burwitz, & Williams, 1994).

Point of exansion (POE) is the area where the two edge lines of the road appear to converge and the point at which the road appears to expand outward from the center (Rockwell, 1972).

Reaction time (RT) refers to the elapsed time between presentation of a particular stimulus and the initiation of a response to that stimulus (Schmidt, 1988).

Saccadic eye movements refer to movements of the eyes from one fixation point to another. A common saccade lasts for approximately 1I5Oth to 1/10 t of a second depending on how far it is to the next fixation (Andreassi, 1989).

Search Rate refers to a combination score representing the number of fixations and the duration of each fixation at particular locations (Williams, Davids, Burwitz, & Williams, 1994).

Selective attention refers to "the process of selecting part of simultaneous sources of information by enhancing aspects of some stimuli and suppressing information from others" (Theeuwes, 1994, p. 94).

Somatic anxiety refers to perceptions of physiological arousal such as shakiness, sweating, increased heart rate, rapid respiration, and "butterflies in the stomach"' (Martens et al., 1990).

Stress is characterized by a combination of stimuli or a situation that is perceived as threatening and which causes anxiety and/or arousal (Hackfort & Schwenkmezger, 1993).







32
Useful field of view (UFOV) refers to the information gathering area of the visual display (Mackworth, 1976).

Visual search refers to the two-stage process in which visual information from

sensory receptors is held in a rapidly decaying visual sensory store and then selected items in the iconic store are subjected to a more detailed analysis (Jonides, 1981; Theeuwes, 1994).

Assumptions

For the purposes of this investigation, the following assumptions were made:

1. Participants received course credit for participation and therefore should have been

equally motivated to participate in the study.

2. The time-to-event paradigm and specific instructional sets which include possible ego

threats, monetary gain, and other incentives, were appropriate methods to manipulate

cognitive anxiety (Hardy, Parfitt, & Pates, 1994).

3. The CSAI-2 (Martens et al., 1990) was an appropriate measure of cognitive anxiety.

4. Heart rate and pupil diameter measures were accurate and appropriate indices of

arousal (Abernethy, 1993; Hardy, 1996).

5. The dependent measures used to assess central driving task performance (lap speed

and number of errors) and the peripheral tasks (RT and number of errors) were

appropriate measures of performance.

6. The central dogma that the line of sight will coincide with the direction of attention

(Viviani, 1990) was at least partially true in this case, and therefore, visual search

orientation was reflective of the participant's actual allocation of attention.






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Significance of the Study

Most empirical research dealing with the interactive effects of arousal and/or anxiety with performance has been oriented in a in a very general fashion. This is exemplified by the global measures of both stress and performance that have been used (Jones, 1990). Therefore, very little is known regarding the specific components of the stress response (either cognitive anxiety, arousal, or both) that influence performance variables such as attention flexibility, speed of information processing, decision-making, and other cognitive factors. With this in mind, the primary intention of the study was to contribute to and expand upon the established bodies of knowledge regarding the ability of participants in competitive sports and other stress-inducing activities to attend to and process the most relevant cues and make decisions appropriately. Though a driving task was used in the study, the implications of this research are intended to be generalizable, to a certain extent, to other achievement situations in which the stress response occurs. The driving simulation provided an ecologically valid, natural dual task paradigm in which to ideally investigate the phenomena of interest due to the need to attend to and process cues from both central and peripheral locations while driving.

The investigation addressed five issues of theoretical importance. First, a greater understanding was provided of the decrement in performance that has been repeatedly shown while completing tasks under high levels of stress. Though the attention narrowing phenomenon has received much empirical support as the underlying reason for a diminished ability to execute various tasks, other factors were suggested as possible contributors to these debilitative effects. Specifically, proposed was that the influence of






34

distractors, and the tendency to be distracted when faced with increased activation levels may also contribute to performance decreases, but had not been addressed. Wegner (1994) and others have presented the notion that as stress levels increase, the propensity of the performer to be distracted is enhanced. Though empirical evidence does not exist to support this notion, anecdotal self-report from athletes and athletes and other performers warranted investigation into this area (Moran, 1996). No research done to date in the context of peripheral narrowing had been conducted in which distractors were presented to participants while performing central and peripheral tasks.

Another issue of interest was whether the performance changes that were anticipated to occur under elevated levels of activation were due to changes in psychological affect (e.g., cognitive anxiety), an increase in arousal level, or some combination of both. By examining these variables in the context of the cusp catastrophe model (Hardy & Fazey, 1987), a clearer understanding of them and their affect on attention processing was delineated.

Third, determined to a certain extent was whether performance changes under higher levels of activation were due to the perceptual alterations in visual selective attention (as indicated by changes in visual search patterns) or other non-perceptual factors (i.e., encoding, response selection) during the information processing of relevant and irrelevant stimuli. As mentioned, one of the areas of controversy regarding the peripheral narrowing phenomenon was with respect to the mechanisms responsible for the lack of effective cue utilization. Indirect support has been provided for both a diminished ability to perceive relevant cues as well as a decrease in the efficiency of later stages of






35

information processing. Before this study was undertaken, no researchers had used eye movement information to clarify these issues. However, shifts in visual attention from central areas of a display to the periphery, and vice-versa, were reflected in the visual search data obtained in this experiment. Furthermore, information gathered from the use of visual search monitoring equipment was used to shed some light on the question of distraction versus narrowing by indicating whether eye-movement patterns were altered to focus more on distractors while under high levels of stress. A fourth area of significance addressed in this experiment was the effect of elevated activation levels on specific performance variables. In particular, by evaluating performance in terms of a variety of accuracy, speed, and reaction time measures, a more complete understanding of the separate elements of proficiency that are impaired or facilitated was ascertained. As Jones and Hardy (1990) have suggested, the lack off attention to these specific performance variables rendered it difficult, if not impossible, to prescribe interventions to enhance them.

Finally, an attempt was made to surmise whether skill execution was affected in a gradual or more dramatic fashion at higher levels of activation. Although the view of an inverted-U relationship between activation levels and performance is still the most popular conception of the relationship, this investigation provided evidence that perhaps more recent models (such as the cusp catastrophe model) are more accurate in their predictions.

From a more applied point of view, the results of this investigation are expected to benefit both drivers and sports performers. Though merely a simulation, the findings from this investigation give an indication of the manner in which excessive driving demands (such as heavy traffic, being "cut off', or near accidents) which increase the level of







36
activation of drivers will affect their attentional abilities. Furthermore, the impact of attentional abilities on the central task of driving the car (accelerating, braking, and steering) as well as the ability to detect and effectively process peripheral information were elaborated.

It is anticipated that many of the results obtained from this study will be

generalizable to other dynamic and reactive sport activities that involve the coordination and flexibility of attentional. processing between central and peripheral sources of information. By developing a clearer understanding of information processing abilities in these types of environments, it may be possible to derive training simulations to help athletes to maintain focus on the most relevant cues in the performance situation. For instance, Singer, Cauraugh, Chen, Steinberg, Frehlich, and Wang (1994) have shown that it is possible to train attentional parameters to be more in line with expert strategies used in reactive tennis situations. Perhaps this will. be possible in tasks in which an anxietyproducing situation is present, such as the high speed driving context of interest in this study.













CHAPTER 2

REVEEW OF LITERATURE

When considering the ability to attend to, process, and react to specific cues in dynamic, highly reactive sport situations in the most efficient and correct manner, issues arise concerning the various attention and information processing components that either facilitate or impede performance. Specific questions include: How do performers know which cues to attend to? What are the properties of particular cues that make them salient and informative to the participant? What information is extracted from cues as they are attended? What are the separate influences of arousal and anxiety levels on the ability to perform effectively by selecting and processing the most relevant cues at the right time? Do eye movements and other behaviors associated with visual attention processing change under stressful situations? If so, do changes in attention shifts and eye movements reflect detrimental or facilitative effects of performance? Are these effects due to a narrowing of the visual field and/or to changes in the ability to mediate the distracting properties of irrelevant stimuli? These are questions that have received little attention in the context of sport and other performance areas and will therefore be investigated in this project.

The influence of an organism's general level of activation is integral to the ability to respond to particular stimuli in an effective and timely manner. The level of activation 37







38

is usually described in terms of the performer's state of arousal, which has been defined by Abernethy (1993) as "a physiological state that reflects the energy level or degree of activation of the performer at any particular instant" (p. 129). Since the publication of the Yerkes-Dodson (1908) Inverted-U theory, much research has been devoted to understanding the influence of arousal states on the ability to attend to, discriminate, and process information in tasks ranging from simple laboratory reaction time tasks to more applied areas in sport, the military, and industry. Research in which the effects of stress on performance have been investigated have ranged along a continuum from assumed low levels of arousal in vigilance tasks to very high levels of arousal in quickly changing, interactive, dynamic environments or situations in which the perception of threat has been induced.

A concept that received a great deal of attention during the early 1950's was the narrowing of the attention field as arousal and/or anxiety increased, culminating in the publication of Easterbrook's (1959) article describing the phenomenon. The peripheral narrowing idea has been used extensively to explain changes in performance in a variety of laboratory tasks and has been generalized to other real-world applications. However, in the sport domain, empirical investigation of the peripheral narrowing phenomenon has been sparse. Furthermore, other factors such as the influence of distraction on decision making and information processing capabilities of athletes have been virtually ignored by sport psychology researchers. Similarly, no research has been directed toward assessing these various attention parameters in the sport of auto racing. However, due to its reliance on speedy decision making and attention shifts under extreme time constraints







39

and life-threatening circumstances, auto racing provides the ideal environment in which to assess these factors. Differences to such situations in other contexts and with other tasks can be made, which is the intent in the present study.

Accordingly, the focus of the following literature review is to critically evaluate the literature that led up to and continued beyond the publication of Easterbrook's (1959) influential work. Also, the separate components of stress will be compared and contrasted, and the interactive influence of these components on attention will be summarized. Furthermore, a justification for examining attention processing in stressful environments with respect to eye movement parameters will be provided. Finally, an empirical framework will be proposed to evaluate the influence of physiological and cognitive stress on attention capabilities in a simulated race car driving task.

Stress and Human Performance

Anxiety, arousal, fear, and a variety of other terms that fall under the guise of

stress have been studied extensively in terms of their influence on performance, individual responses to stressors, and methods of regulating the stress levels of sport performers. The very nature of sport, with its increasing public exposure, the pressures placed on athletes to win from coaches, other athletes, and themselves, the rewards for great performance, and the disappointment from losing, is full of stressful performance situations (Murphy, 1995). Athletes who are able to regulate the stress response and perform in competitive situations in spite of the surrounding pressures inherent in sport are those who will inevitably excel.







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However, though the general topic of stress in sport has received much attention from sport psychology researchers, confusion has been proliferated by the fact that many researchers and practitioners use terms such as activation, stress, anxiety, and arousal interchangeably, treating a multidimensional construct in unidimensional ways. Accordingly, before addressing the specific issue of attentional narrowing as a result of stressful circumstances, a discussion of the similarities and differences of these terms must be addressed. Also, a discussion of popular theories developed to describe how performers deal with stress and the theoretical basis for the present investigation Will be provided in light of the recently proposed cusp catastrophe model of anxiety and arousal (Fazey & Hardy, 1987).

Stress

Stress is defined as a combination of stimuli or a situation that is perceived as threatening and which causes anxiety (Hackfort & Schwenkmezger, 1993)). Various stressors include external threats, deprivation of primary needs, and performance pressures that can be characterized as both general and sport specific. Selye (1956) described stress based on the principle of equilibrium in which self-regulation is of primary importance. He differentiated stress (a condition to which we are always prone) from the inability to cope with the stress.

A popular cognitive view of anxiety that was heavily influenced by Selye's ideas was forwarded by Lazarus and his colleagues (e.g., Lazarus, 1966; Lazarus & Averill, 1972). Basically, Lazarus viewed anxiety as an emotion with a specific pattern of arousal that corresponds to it and that is influenced by the cognitive appraisal and perception of an







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anxiety-producing event. According to this view, all facets of a situation tend to be classified with respect to its significance and the implications of that situation on the person's well-being. Therefore, it is the perception of the event and not the event itself that dictates emotions. Researchers have discovered that, contrary to the medical model of stress, many people view stress and anxiety as challenging, exciting, and beneficial (Lazarus & Folkman, 1984).

These findings prompted the formulation of Kobasa's (1989) Hardy Personality Theory which states that people who are psychologically hardy tend to view stressful situations in a positive way. The specific characteristics of psychologically hardy people are that they (1) are committed to the activity, (2) believe they can control or influence events, and (3) view demands or changes as exciting challenges. Similarly, Smith's (1980) mediational model suggests that the appraisal process creates the psychological reality based on what the individual tells himself or herself about the situation and the ability to cope with it.

Meichenbaum (1985) also suggests that the cognitive appraisal of the individual is what dictates the nature of the interaction with the environment. The meaning the person construes to the event is what shapes the emotional and behavioral response. Similarly, Mahoney and Meyers (1989) postulate that it is not stress that is central to performance but the athlete's expectations, efficacy beliefs, and use of arousal that will determine performance. Therefore, arousal, if perceived as natural is positive but negative anxiety (i.e., worry) is negative. Being aroused does not mean that one will become anxious. Rather, anxiety occurs due to (1) distrust of natural responses, (2) ineffective perceptions







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due to previous exposure to modeling of arousal, (3) directly being taught that arousal is bad, and (4) early failure experience while aroused. Support for the notion that it is the perception of the stressful situation that dictates performance is provided by findings that athletes enjoy the "nervousness" associated with competition. Rotella, Lerner, Allyson, and Bean (1990) have shown that precompetitive feelings of high activation are helpful to performance if they are perceived to be natural and provide a sense of readiness rather than concern.

Unfortunately, all athletes, even those perceived as being the best in stressful situations, occasionally "choke" under pressure. Thus, the question remains: How do external and internal stressors manifest themselves in the stress response and how does the stress response affect performance? The rest of the review will be directed toward describing situations in which the performer fails to regulate the stress response appropriately. A justification for continued research in this area will be provided. From a cognitive perspective, then, questions arise concerning how the stress response influences the ability of performers to process information and allocate processing resources to coping with stressful stimuli as well as dealing with task demands and constraints. Theories of the Stress Response

Controlling the stress response is critical to the ability to perform well. Whether or not cognitive appraisal reflects reality is not necessarily important in terms of the stress response for the simple reason that it only occurs in situations in which self-regulatory skills fail (Carver & Scheier, 198 1; Cherry, 1978; Jones, 1990; Lazarus, 1966).







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The analysis of stress has its roots in the psychoanalytic conceptualization of the construct. Specifically, Freud (1952) postulated that affect and neurosis are closely related to each other, with affect being related to exogenous arousal and neurosis being related to endogenous arousal. Though not the most popular view of stress today, this does provide a foundation for much of the work done in the psychoanalytic realm and provides the impetus for later cognitive and behavioral approaches to the study of stress.

Mower's (1960) learning theory approached stress from a behavioral learning viewpoint involving both classical conditioning and instrumental reinforcement. He suggested that in environments where specific stimuli result in stressful outcomes, the organism would eventually learn to associate the stimulus with the stressful outcome. For instance, if an athlete consistently performs poorly in a specific competition setting, eventually, the simple thought of that setting will elicit an anxious response.

With the cognitive revolution in the late 50's and early 60's, stress (in particular, anxiety) was viewed as an emotion that is triggered by a person's "communicative relationship" with the environment and arose from expectations and appraisals of these situations (Festinger, 1954). Festinger suggested that anxiety control is based on decisions that lead to either direct actions to remove the anxiety-producing stimulus or to avoid it (the approach/avoidance distinction). Three assumptions that formed the basis of Festinger's theory were that: (1) a person who cannot account for arousal will look for something to attribute it to, (2) previous explanations do not cause a need for appraisal, and (3) a person with arousing thoughts but no physiological arousal will not show emotional response and therefore will not be stressed. According to this view, an athlete







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that experiences physiological arousal will only choose to exert cognitive processes for interpretation of it if the arousal persists, and is unaccounted for (Hackfort & Schwenkmezger, 1993).

As will be described in depth later, the specific reactions to stress are individually determined. Stress can be manifested in the form of cognitive and somatic anxiety, physiological arousal, loss of self-confidence, panic, and a variety of other forms. Obviously, each of these different responses will have an influence on performance if not regulated appropriately.

The Stress/Performance Relationship

One of the more popular early conceptualizations of the stress/performance

relationship was the Hull/Spence Drive Theory (Hull, 1952; Spence & Spence, 1966). According to the theory, level of activation is considered a function of the sum of all of the energetic components affecting an individual at the time of a particular behavior. Furthermore, drive strength is dependent on the emotional reaction that is caused by an aversive stimulus. Thus, people with increased drive levels perform better due to their greater effort, emotion, and motivational need to remove the aversive stimulus. Though an attractive early attempt to explain the stress/performance relationship, empirical testing has suggested that the theory is not generalizable to many situations, especially those requiring fine motor control.

Other popular theories that have attempted to relate stress to performance are the 'optimal zone' theories. Of these, Hanin's (1980) concept of an arousal zone of optimal functioning (ZOF) has received the majority of empirical investigation. Though initially







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criticized as a reiteration of the Inverted-U hypothesis (Yerkes-Dodson, 1908) (which will be discussed at length in the next section), it is instead an interindividual account of how arousal affects performance. The attractiveness of the model rests in the fact that it accounts for individual differences, something the Inverted-U is unable to do. A similar theory is Martens' (1987) zone of optimal energy.

Csiksentmihalyi's (1975) concept of a less sport-specific optimal arousal state (or FLOW state) is another attempt to explain the activation of the organism at a level that is most conducive to performing well. The flow state is characterized by a variety of factors including (1) awareness, but not being aware of awareness, (2) focused attention, (3) loss of the ego and self-consciousness, (4) feeling of being in control, and (5) intrinsic reward from performing well. Often athletes refer to the flow state in discussing their best performances and continued research is being directed toward understanding the factors that allow athletes to enter this relaxed state of intense concentration and seemingly effortless ability to perform at the highest levels.

Another related theory to that of the 'optimal states' is Kerr's (1989) Reversal Theory. Based on Apter's (1982) phenomonological theory of motivation, emotion, personality, and psychopathology, Kerr's basic premise is that depending on the metamotivational state in which the athlete is currently involved, there is a combination of arousal and "hedonic tone" (feeling of pleasure) that dictates whether that state will be associated with anxiety, pleasurable excitement, boredom, or relaxation. A discussion of the intricacies of reversal theory is beyond the scope of the current review, but it does







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provide a unique way to view the arousal/anxiety/performance relationship and warrants further investigation.

Inverted-U Hypothesis

Of the theories that have been proposed to account for the relationship between stress and performance, perhaps the most influential and misunderstood is YerkesDodson's (1908) Inverted-U hypothesis. The basic premise of the Inverted-U hypothesis (which was generated based on work with animals) is that as arousal increases so does performance until an optimal level is reached. At this point, any increase in arousal level will lead to a gradual deterioration of performance until arousal level is reduced to the optimal level (Yerkes-Dodson, 1908). Unfortunately, sport psychology research has been reluctant to abandon the rather shallow notion of the Inverted-U hypothesis due to the simplistic nature of the theory and its almost universal application. The myths and realities surrounding this controversial theory and the research undertaken that both supports and refutes it will be briefly reviewed in the following section.

It has been postulated that one mediator of the stress/performance relationship is the characteristics of the task. In regard to the influence of task characteristics on the stress/performance relationship (and assuming the Inverted-U relationship of stress to performance), Oxendine (1970, 1984) and Oxendine and Temple (1970) suggested that different types of tasks require different levels of arousal. According to Oxendine, a moderately above resting level of arousal is required for the successful execution of all motor tasks. Also, a low level of arousal is best for tasks involving complex movements,







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very fine motor control, steadiness, and concentration. Finally, in gross movements requiring strength, endurance, and speed, a high level of arousal is most beneficial.

Though intuitively appealing, Oxendine's suggestions have been criticized due to their simplicity (Jones, 1990). Jones provides several examples of sport situations where Oxendine's hypotheses do not hold true and cites three primary reasons for their lack of value. First, only one of the three predictions has held up to empirical examination; that relatively lower levels of arousal are most advantageous for complex, highly specialized tasks. Also, his classification system is overly simplified in that entire sports such as basketball which requires extremely diverse arousal states during the course of the game can be categorized in one of the three levels. Finally, Jones (1990) suggests that Oxendine does not consider the cognitive requirements of the skills in favor of focusing on the movement parameters in particular.

Another one of the primary criticisms of the Inverted-U hypothesis is its global nature. It seemingly relies on the notion of a general stress response that influences performance (e.g., Neiss, 1988). Levi (1972) made an early attempt at separating the different components of the stress response by suggesting that both high and low levels of arousal could be experienced as stressful. In this vein, he proposed that an increase in stress would result from the further deviation of the arousal state from the optimal level. However, these ideas have also been criticized and basically dismissed by the newer concepts of the interactionist approach to stress in which individual differences in the perception of the stress response are accounted for, not simply the fact that being underaroused or overaroused causes stress.







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Another problem with the Inverted-U description of the stress/performance

relationship is that it is a description and nothing more (Jones, 1990). No explanation is offered for why performance is impaired when arousal deviates from the optimal level. Though factors such as attentional allocation of resources, attentional narrowing, and hyperdistractibility have been suggested and many have been investigated, the Inverted-U hypothesis specifies none of these as the primary contributor to the decline in performance as arousal deviates from optimal levels. More than likely, it is a combination of these factors that impacts on the ability of the performer to function efficiently and to process information effectively in the stressful environment.

Another criticism that has been levied against the Inverted-U hypothesis is that it does not address specifically how performance is influenced. Rather, the hypothesis merely states that overall capabilities, in a very general sense, are dependent on the level of stress. Obviously, this description is entirely too global and does not explain how such variables as speed of information processing, stimulus detection ability, and response accuracy are affected (Eysenck, 1984). Furthermore, as Will be addressed later, the actual shape of the Inverted-U hypothesis has been questioned by those who assert a more dramatic decrease in performance at high levels of anxiety/arousal with a more difficult recovery to high performance levels as anxiety/arousal decreases (Hardy & Fazey, 1987).

It has been suggested that there is virtually no sound evidence to support the

Inverted-U hypothesis (Hockey, Coles, & Gaillard, 1986; Naatanen, 1973; Neiss, 1988). Perhaps, of the critics of the Inverted-U, Neiss (1988) is the most rabid, calling the empirical evidence in favor if the Inverted-U "psychologically trivial". Other researchers







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have been equally adamant regarding its lack of applicability, validity, and credibility, calling it a "catastrophe" and a "myth" (Hardy & Fazey, 1987; King, Stanley, & Burrows, 1987). The criticisms and negative connotations associated with the Inverted-U hypothesis prompted Neiss (1988, 1990) to suggest that the study of arousal in the context of the Inverted-U should be abandoned for the following reasons: (1) it cannot be falsified, (2) it cannot function as a causal hypothesis, (3) it has trivial value if true, and

(4) it hinders understanding of individual differences in regard to the stress response.

Others suggest that it merely needs to be reformulated to account for individual differences and to address the underlying mechanisms that specify the facilitative and/or detrimental effects of stress (Anderson, 1990; Hanin, 1980; Martens, 1987). Researchers have addressed such areas as the nature of the task (e.g., Weinberg, Gould, & Jackson, 1985), skill level (e.g., Cox, 1990), and individual differences (e.g., Ebbeck & Weiss, 1988; Hamilton, 1986; Spielberger, 1989) with respect to the Inverted-U hypothesis. However, the understanding of these specific components is only beginning to be surmised.

Perhaps much of the confusion, equivalence of empirical results, and lack of consistency in research findings that has been associated with the Inverted-U can be attributed to the multitude of experimental methods that have been used to examine it and the lack of consistency in differentiating the various components that embody the term "stress". A discussion of the specific components that fall under the guise of "stress" will be presented in the following section.







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Stress. Arousal, and Anxiety

As mentioned earlier in the review, stress is characterized by a combination of

stimuli or a situation that comprises the circumstance of a person's subjective experiences as threatening and which causes anxiety (Hackfort & Schwenkmezger, 1993). According to this view, stress occurs when one is unable to cope with a particular situation, and it arises due to specific 'constellations' of threatening stimuli. Various stressors include internal and external threats, performance pressures, social threats, and sport-specific circumstances. One of the specific components of stress is anxiety.

Anxiety is an emotion characterized by uncertainty; a state of unoriented activation that is learned through the socialization process and direct exposure to anxiety-producing situations (Sage, 1984)). Fear, on the other hand, though similar to anxiety, is characterized by the perception of danger in response to a known threat, is a reflex-like defense, and is logical, self-protective, and adaptive (Hackfort & Schwenkmezger, 1993). According to Cattell. and Scheier (196 1), fear is a specific reaction while anxiety is caused by anticipatory and imaginative processes. Thus they are based on the degree of specificity and recognizability.

Spielberger (1966, 1972, 1983) defines stress as being closely related to state and trait anxiety. The trait component is exhibited as an acquired behavioral disposition, independent of time, causing the person to perceive a wide range of not very dangerous circumstances as threatening. Conversely, state anxiety refers to subjective, consciously perceived feelings of inadequacy and tension accompanied by an increase in arousal in the autonomic nervous system. These characteristics are influenced by both cognitive and







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emotional components in which the person is preoccupied with irrelevant thoughts and eventual subjective excitement when the ego is threatened. Spielberger's Anxiety Theory (1966, 1972) states that those with higher trait anxiety tend to respond to stressful situations with even higher state anxiety. In accordance with this view, studies (e.g., Hackfort & Schwenkmezger, 1989) have indicated that those who exhibited higher trait anxiety reported anxiety as debilitating while those who were not trait anxious reported it as facilitative to performance. Similarly, Martens (1971, 1974) determined that highly anxious persons perform better on some tasks while lower anxious do better on others and that the state anxiety level at the beginning of the learning process depends on the trait anxiety level of the person. Furthermore, there appears to be an unexplored interaction between anxiety level, situation-specific stress stimuli, task difficulty, and situation specific conditions of learning and performance.

Another important distinction must be made between cognitive and somatic anxiety. Cognitive anxiety is characterized by a state of worry, the awareness of unpleasant feelings, and concerns about ability to perform and concentrate in a particular environment. Worry is a cognitive process that takes place prior to, during, and after a task and is marked by decreases in faith in the performance, increased concern, social comparison, and fear of failure (Hackfort & Schwenkmezger, 1993). These characteristics of worry may represent cognitive, evaluative processes that are suitable for predicting performance, as high levels of worry tend to lead to lower levels of performance (Martens, Burton, Vealey, Bump, & Smith, 1990).







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Conversely, somatic anxiety refers to the perceptions of physiological arousal such as shakiness, sweating, M respiration, and "butterflies" in the stomach. A synonymous term used to describe somatic anxiety is "emotionality", characterized by affective physiological system changes caused by an increase in arousal level (nervousness, increased HR, etc.) (Zaichkowski & Takenaka, 1993). Furthermore, cognitive and somatic anxiety appear to have different antecedents. Somatic anxiety is elicited by a conditioned response to competitive stimuli while cognitive anxiety is characterized by worry or negative expectations about an impending performance or event. A handful of studies has suggested that there tends to be a negative link between worry and motor performance while there appears to be a positive link between somatic anxiety and performance (e.g., Gould, Weiss,& Weinberg, 198 1).

Due to the relevance of somatic anxiety to arousal, these terms are often used interchangeably. However, there is a clear distinction between the two terms. Somatic anxiety refers to perceptions of physiological states and is, therefore, a psychological characteristic. On the other hand, arousal reflects the natural activity of one's physiology and is therefore a purely physiological construct (Rotella & Lemer, 1993). In this respect, somatic anxiety is influenced by the subjective evaluation and interpretation of arousal. The specific physiological mechanisms that govern arousal level are thought to be regulated by the neurophysiology of the central nervous system, in particular. The four primary structures involved include the cerebral cortex, the reticular formation, the hypothalamus, and the limbic system. The cortex is responsible for cognitive appraisal of incoming stimuli, the reticular formation acts as an organizer with the other components,







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the limbic system provides emotional input in the regulation of arousal, and the hypothalamus regulates sympathetic nervous system activity along vAth the pit-uitafy gland (Zaichkowski & Takenaka, 1993). These upper level control systems exert their influence on the sympathetic nervous system which is primarily responsible for the psychophysiological changes in HR, pupil dilation, respiration rate, blood glucose levels, and other physiological responses.

Though the description of arousal appears straightforward, researchers have conceptualized it in various ways. For instance, Sage (1984) suggests that arousal is synonymous with activation level. Magill (1989) discusses it in a motivational context that serves as an energizing agent to direct behavior to a specific goal. Cox (1990) has defined arousal as alertness while Martens (1987) dislikes the term "arousal" altogether and prefers the term "psychic energy" which serves as the cornerstone of motivation. Based on these current views of arousal, collectively, anxiety appears to be a multidimensional construct that serves as an energizing function of the mind and body and varies along a continuum from sleep to extreme excitement. It contains a general physiological response in which several systems may be activated at once in including HR, sweat gland activity, pupil dilation, and electrical activity of the brain. It also includes behavioral responses (performance) and cognitive processes (appraisal of physiological arousal).

Therefore, in order to gain an accurate assessment of arousal, physiological,

behavioral, and cognitive components must be assessed (Borkovec, 1976). It should be emphasized that changes in physiological function are not necessarily indicative of arousal,







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and therefore must be accompanied by other measures because any of the physiological components can be altered without impacting the others (Lacey & Lacey, 1958). These issues will be addressed again later in the discussion of multidimensional anxiety theory. Assessment of the Stress Response

As mentioned, due to the multidimensional nature of the stress response, multilevel assessment is absolutely necessary to gather a better understanding of the influence of the various components of stress on performance. Assessment effectiveness can be maximized through the combination of physiological, behavioral, and cognitive (selfreport) measures. Physiological indices of arousal include such measures as skin resistance, pupil dilation, heart rate, electroencephalogram, electrocardiogram, electromyogram, and other biological measures. The advantages of physiological assessments are that they are not tied to verbal statements. Also, they can be used with all types of people and can assess changes in arousal continuously. However, the primary disadvantage is the fact that physiological measures lack high correlations among each other, a condition Lacey and Lacey (1958) referred to as autonomic response stereotype. Also, in most sport contexts, physiological measures will be confounded by other physiological changes due to exercise-induced responses.

Another level of assessment is behavioral. Observation of behavioral change (such as the presence of nervous twitches, vomiting, etc.) can provide an indication of the stress response. Unfortunately, often behavioral observations may be attributed to stress when the actual root of the behavior is not stress-produced. For instance, vomiting could be







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due to the flu rather than competitive stress. Therefore, often self-statements are needed to interpret behavioral observations.

Assessment at the cognitive level is usually done through self-report measures.

Some of the more popular measures of anxiety include the State-Trait Anxiety Inventory (STAL Spielberger, Gorsuch, & Luschene, 1970), the Sport Competition Anxiety Test (SCAT: Martens, 1977), and the Competitive State Anxiety Inventory 2 (CSAI-ll: Martens, Burton, Vealey, Bump, & Smith, 1990). It should be mentioned that most cognitive measures of arousal that have been used are those that measure anxiety, not arousal. Though much time and effort has been devoted to the development of these selfreport measures, Kleine (1990) conducted meta-analyses that indicated only a moderate relationship between various measures of anxiety and performance. Furthermore, his results suggested that the STAI (a non-sport-specific measurement tool) was as good as the SCAT (sport-specific) for predicting performance in sport. Further criticism has been directed toward the SCAT due to the unidimensionality of the instrument (assessing only the cognitive aspects of anxiety) and its bias toward assessment of the frequency of debilitating anxiety while ignoring possibly facilitative aspects.

As mentioned, one of the primary weaknesses of research on stress and more specifically, anxiety, is the lack of multidimensional assessment. The CSAI-2 is more multidimensional in nature as it separates measures of cognitive and somatic anxiety and has been used extensively in sport research. The reliability and validity of the instrument and its ability to measure the multidimensional nature of anxiety is laudable. The next section of the review addresses the multidimensional nature of anxiety and the importance







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of obtaining a better understanding of the influence of specific components of anxiety and their influence on performance from both a basic and applied point of view. Multi-Dimensional Anxigty Theor.y

Due to recent concern with the lack of usefulness of the Inverted-U model of anxiety and/or arousal, theorists began to search for a better explanation of the stress/performance relationship. Researchers began to attempt to break down the stress response into its various components. These concerns eventually lead to the formation of multidimensional anxiety theory which has also spurred the development of other theories such as Hardy and Fazey's (1987) catastrophe theory. The generation and a general summary of multidimensional anxiety theory follows.

Perhaps the first to attempt a defragmentation of the general stress response were Liebert and Morris (1967), who identified two primary contributing factors to anxiety: Worry and emotionality. In Liebert and Morris's view, worry consisted of cognitive concerns about one's performance while emotionality referred to the autonomic reactions to the performance environment. This initial identification heavily influenced Davidson and Schwartz's (1976) multidimensional model of anxiety. They were the earliest to use the terms "cognitive" and "somatic" anxiety and formulated their theory in the context of clinical applications. Thus, worry has become synonymous with cognitive anxiety and emotionality has become synonymous with somatic anxiety. Cognitive anxiety is typified by the awareness of unpleasant feelings and concerns about ability to perform and to concentrate. Conversely, somatic anxiety is characterized by perceptions of physiological arousal such as shakiness, sweating, HR, respiration, and "butterflies in the stomach".







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These general characteristics of the components of anxiety have held up under empirical investigation and appear to be manipulable independently (e.g., Schwartz, Davidson, & Goleman, 1978).

As mentioned, another characteristic of the multidimensional components of

anxiety is that they appear to have different antecedents. Somatic anxiety is elicited by a conditioned response to the competitive environment, while cognitive anxiety is characterized by worry or negative expectations. Researchers have consistently shown that somatic anxiety tends to build as the event (or competition) grows nearer and dissipates as performance begins, while cognitive anxiety continually fluctuates as the subjective probability of success varies (Jones & Hardy, 1990; Martens et al., 1990). Martens et al. (1990) found that cognitive anxiety remains stable and high during the period preceding an event while somatic anxiety peaks at the moment just before competition. Likewise, in an earlier study, Spiegler, Morris, and Liebert (1968) reported similar results in the context of test anxiety.

Another means in which cognitive and somatic anxiety differ is with respect to

their effects on performance. In accordance with differences in the time course of anxiety onset, somatic anxiety would be expected to have no influence on performance while cognitive anxiety would have a significant influence, due to the ever changing subjective probability of success. Consistent with this prediction, Martens et al. (1990) found that this was the case. However, other studies have shown an Inverted-U relationship of somatic anxiety to performance (Burton, 1988). Furthermore, studies using the time-toevent paradigm have found that cognitive anxiety actually has a positive effect on







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performance in the days leading up to a competition (Hardy, 1996). Thus, it appears that rather equivocal results exist on both sides of the argument. Jones and Hardy (1990) interpret the disparity and lack of consistency in findings to the multitude of different paradigms that have been devised and the abundance of analyses that have been applied to reduce the data.

Another problem that exists with respect to multidimensional anxiety theory is the two-dimensional approach used to explaining the effects of somatic and cognitive anxiety on competition. Specifically, the two-dimensional approach in analyzing results tends to neglect the interaction of the components of anxiety, treating them independently rather than in combination (Hardy & Fazey, 1987). According to the viewpoint of Hardy and his colleagues, any relatively comprehensive treatment of these components must consider them in an interacting, three-dimensional manner. In an attempt to improve the predictability and structure of the model, therefore, Hardy and Fazey (1987) developed a catastrophe model of anxiety and performance. A Catastrophe Model of Anxi!4y

In an effort to advance understanding beyond the multidimensional approach to the study of the effects of anxiety and arousal on performance, Hardy and Fazey (1987) formulated a three-dimensional model of the relationship. Borrowing heavily from Thom (1975) and Zeeman (1976) who originally devised the idea of catastrophes and then applied them to the behavioral sciences, respectively, Hardy and Fazey's (1987) model is closest in form to the cusp catastrophe, one of the seven originally proposed catastrophe models of Thom (1975). Zeeman (1976) borrowed Thom's original ideas and described







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the theory by developing a machine to model it. When describing human behavior, however, events are less mechanistic and absolute, requiring the model to be revised such that changes in one variable (i.e., anxiety or arousal) increases the likelihood that the dependent variable (i.e., behavior) will change in a predicted direction.

Of the two independent variables in Hardy and Fazey's (1987) model, anxiety is the "splitting factor", the variable that determines performance levels and ultimately, catastrophes. The roles of cognitive anxiety and physiological arousal were chosen specifically to be able to evaluate testable hypotheses with respect to the anxiety/arousal/performance relationship. Specifically, when cognitive anxiety is low, the model predicts that physiological arousal will influence performance in an inverted-U fashion. However, when physiological arousal is high, such as on the day of competition, high levels of cognitive anxiety will result in lower levels of performance. When physiological arousal is low, such as during the days leading up to competition, higher cognitive anxiety will lead to increases in performance. When cognitive anxiety is high, the effect of physiological arousal depends on how high cognitive anxiety is elevated. Usually the manipulation of anxiety and arousal is carried out through a time-to-event paradigm in which assessments are taken at specified times leading up to a competition setting. For instance, assessments will be taken one week prior, two days prior, and then one hour prior to the competition setting. In this way, the time course of anxiety and arousal can be assessed. In other instances, levels of anxiety and arousal are manipulated through the use of both ego-threatening or other anxiety producing instructional sets and through the use of exercise-induced arousal, respectively.







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Testable hypotheses have been generated from the conceptual framework of the

original catastrophe model (Fazey & Hardy, 1988). According to the model, physiological arousal changes are not necessarily detrimental or facilitative to performance. However, if physiological arousal is high, it can have catastrophic effects on performance in situations where cognitive anxiety is also high. Another prediction is the hysteresis effect. Due to the splitting effect of cognitive anxiety, under high cognitive anxiety conditions, physiological arousal will have a differential effect on performance when it is increasing as opposed to when it is decreasing. A third prediction is that intermediate levels of achievement are most likely to occur under conditions where cognitive anxiety is high. Finally, Fazey and Hardy (1988) suggest that it is possible to fit statistical models to cusp catastrophes.

One notion that may become obvious in the discussion of the differences in the

catastrophe model versus multidimensional anxiety theory is the suggestion that cognitive anxiety can facilitate performance at certain times, especially in the days leading up to competition. This is in direct contrast to most studies of cognitive anxiety that have demonstrated a negative relationship between it and skill execution. With further thought, however, it is obvious that the motivating effects of cognitive anxiety in the days leading up to competition could eventually facilitate achievement capabilities. Also, it should be emphasized that in many of those studies in which a negative relationship has been identified between cognitive anxiety and performance, assessment was made on the day of competition, when physiological arousal can be assumed to be relatively high (Hardy, 1996).







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Another obvious feature of the cusp catastrophe model of the anxiety-performance relationship is the choice of physiological arousal rather than somatic anxiety as the normal factor. The primary reason for this choice was based on the notion that it is part of the organism's natural physiological response to anxiety producing situations (Hardy, 1996). The physiological response to performance anxiety is sufficiently well-established to be considered in the context of a generalized response within the competition setting. However, though the physiological response may be reflected in self-reports of the presence of somatic anxiety, the purely physiological index can encompass the individual task requirements, different situations, and other combinations of factors that override reports of somatic anxiety. Furthermore, physiological arousal changes tend to be mirrored by changes of somatic anxiety while the converse is not the case (Fazey & Hardy, 1988; Hardy, 1996; Hardy &Fazey, 1988).

Substantial support has been shown for the cusp catastrophe model of the anxiety performance relationship in seminal investigations of the model by Hardy and his colleagues. Hardy, Parfitt, and Pates (1994) and Parfitt, Hardy, and Pates (1995) conducted two studies to examine the relationship. In the first of their studies, the timeto-event paradigm was implemented to manipulate anxiety independently of physiological arousal in female basketball players and was primarily directed toward examining the hysteresis hypothesis. Physiological arousal was measured by a Polar heart rate monitor

(HM) and cognitive and somatic anxiety were measured with the CSAI-2. The task was a basketball free throw that was performed after completing physiologically arousing exercise. Findings indicated that both cognitive and somatic anxiety were elevated on the







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day before the tournament. This was a somewhat different finding as compared with previous studies in which somatic anxiety increases usually only occurred on the day of the significant event. The data with regard to the hysteresis hypothesis were supportive. In general, performance followed a different pathway with respect to heart rate when increasing as opposed to when it was decreasing in conditions of high cognitive anxiety but not in conditions of low cognitive anxiety.

In the second experiment, Parfitt, Hardy, and Pates (1995) examined the

generalizability of these findings with women basketball players to male crown green bowlers. The exception in this study was that cognitive anxiety was manipulated through the use of instructional sets rather than through the use of the time-to-event paradigm. The results of the first experiment were replicated in that the three-way interaction between cognitive anxiety, HR, and direction of heart rate change influenced performance in predictable directions.

Another interesting finding that provides support for the cusp catastrophe is a subprediction that performance will be most variable under the high and low cognitive anxiety conditions (Hardy, 1996). Specifically, according to the surface of the performance curve, it would be predicted that the highest levels of performance achieved in the high anxiety condition would be higher than the highest levels achieved in the low anxiety condition. Similarly, the lowest levels of performance in the high anxiety condition would be lower than the lowest levels of performance in the low anxiety condition. In fact, these hypotheses were supported in the second study; thereby providing evidence to support the cognitive anxiety component as the splitting factor on the performance surface (Parfitt,







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Hardy, & Pates, 1995). Though relatively little work has been done to examine the validity of the cusp catastrophe model, initial results provide evidence to support it and many fruitful areas of research in this area are warranted.

One limitation, however, to the study of anxiety in the context of both catastrophe theory and the other models mentioned above, is a lack of explanation for the performance changes that are noticed in overly stressful situations. One specific cognitive mechanism that has been implicated, but has received limited empirical investigation is the impact of anxiety and arousal on attentional resources. The following section will outline some of the research that has been directed toward examining this relationship.

Anxiety. Arousal, and Attention

One of the critical factors that could contribute to performance changes under

anxiety or arousal producing situations is the ability to allocate attentional resources in the appropriate areas and to process information gathered in these areas effectively (Kahneman, 1973; Landers, 1978; Nideffer, 1976, 1989). Evidence seems to suggest an arousal! performance relationship that is mediated by attentional factors. Support has been found for this notion in both anecdotal and empirical evidence (Nideffer, 1988).

Perhaps the most compelling evidence that favors the notion of a mediating role of attentional processes in the anxiety/arousallperformance relationship is the substantial support provided for the idea of attentional (or peripheral) narrowing. Research has indicated consistent changes in the peripheral acuity of subjects assessed in arousal and/or anxiety producing situations. Various studies have indicated a narrowing of attention that occurs in highly stressful environments, resulting in a tunneling effect where peripheral







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cues are selectively attenuated from further processing. Using dual task paradigms, results have shown a facilitative effect in the performance of central tasks with a concomitant decrease in performance of peripheral tasks when performed under a state of increased arousal or anxiety. Literature relevant to the attention narrowing idea will be reviewed in the following section.

Peripheral Narrowing

The first researchers to address the idea of peripheral narrowing in terms of cue utilization were Bahrick, Fitts, and Rankin (1952). Based on the assumptions that anything to which an organism responds is relevant to performance, and that continuously variable information is more important to interpreting a stimulus than are relatively constant sources, Bahrick et al. (1952) hypothesized that perceptual selectivity would be highly dependent upon cues available. They postulated that objects in the peripheral visual field (as well as those aspects of the central task that are relatively unimportant) would tend to be interpreted as less important than those in the central part of the field. Using a tracking task and several intermittent peripheral tasks, they found that when subjects were offered incentives, performance on the central task was superior to performance on peripheral ones. These results were interpreted as suggesting that performance was influenced by the degree of motivation manipulated by the incentives provided.

Easterbrook (1959) produced the most influential article on the topic of cue

utilization based on the findings of Bahrick et al. (1952), and others (e.g., Bruner, Matter, & Papanek, 1955; Callaway & Dernbo, 1958; Callaway & Thompson, 1953). Easterbrook indicated that as the level of arousal increased to a certain point, performance on the







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central task was facilitated due to the blocking of irrelevant cues in the periphery from being processed. In contrast, as arousal increased, he suggested that performance on tasks requiring less of a central focus deteriorated due to the blocking of relevant cues. Furthermore, performance on central tasks deteriorated if arousal level reached a state in which the funneling effect prohibited attention to relevant cues that were integral to performance of the central task.

Easterbrook (1959) suggested that the degree of facilitation or disruption caused by emotional arousal is dependent on the range of cues required to perform a task effectively. These ideas were consistent with Woodworth's (1938) concept of a 49recepto-effector spatf', an index of the range of cue utilization. The size of the receptoeffector span is related to the number of possible responses permitted following a stimulus, and the influence of warning time on the ability to prepare responses. Based on the work of Bartlett (1950) and Poulton (1957), Easterbrook suggested that in serial task performance, "the effect of increased foreknowledge is that responses can be made in larger units so that inter-response delay times become covert, inter-response junctions are smoothed, net speed increases, precision improves, and the performance may be better described as better integrated" (p. 186). Therefore, in tasks that require a large range of cue utilization (larger receptor-effector spans), performance win be facilitated with an increase in the amount of advanced preparation allowed. In relatively simpler tasks, however, requiring reduced cue utilization and attention, a surplus in capacity to attend to and process information exists, permitting the processing of (and distraction due to) irrelevant cues (e.g., Porteus, 1956). In accordance with this view, effective execution on







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a variety of serial tasks including paced problem solving, mirror drawing, and tracking, has been shown to decrease in anxious subjects as compared to control groups.

Easterbrook (195 9) was insistent on the interdependence of perception and

response, based on the premise that a response cannot be made without some type of perception. Similarly, in the absence of a response, it is virtually impossible to determine whether perception occurred. Through this conceptualization, he defined the meaning of a Cccue" as occurring when a singular related response has been made to a percept. Likewise, in highly variable situations, containing many cues, a response to a particular cue can be said to have occurred when the response takes the form of the normal response in the absence of other cues. In light of this operationalization of cue meaning, several researchers during the 1950's found that a funneling or reduction of the perceptual field resulted from induced psychosomatic stress (e.g., Callaway & Thompson, 1953; Combs & Taylor, 1952). In most perceptual tasks administered, manipulations causing the range of cue utilization to fall below that required to complete the task resulted in relative decrements in achievement (Eysenck, Granger, & Brengelman, 1957; Granger, 1953). However, it is important to note that the degree of skill deterioration on tasks is highly relevant to task complexity. As Easterbrook wrote,

For any task, then, provided that initially a certain portion of the cues in use

are irrelevant cues (that the task demands something less than the total capacity of the organism), the reduction in range will reduce the proportion of irrelevant cues

employed and so improve performance ...... When all relevant cues have been

excluded, however, (so that now the task demands the total capacity of the







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subject), further reduction in the number of cues employed can only effect relevant

cues, and proficiency will fall. (p. 193)

One may question the effect of learning on the ability to select and process only the most relevant cues in a display. Support for the idea that overlearning improves the ability to select appropriate cues has been provided by Bruner, Matter, and Papanek (1955). In regard to learning, the question of "What makes a cue relevant or irrelevant?" must be answered. In other words, how does a person know what cues to select and what information will be gained from selection of particular cues? It appears that cue relevance is specified by the amount of information obtained from a cue and the task requirements at hand. Thus, cue utilization is not merely a perceptual idea, but one that is mediated by the "cerebral competence of the subject" (Easterbrook, 1959, p. 196). Consequently, the ability to select and incorporate the most relevant cues while ignoring irrelevant cues is intricately tied to the intellectual competence of the person who must competently complete various tasks.

Easterbrook's (195 9) conceptual contribution spurred much work to investigate the mediating factors that influence the degree of peripheral narrowing, and the related facilitation and inhibition in skill level resulting from this condition. The methodologies used and factors investigated are quite varied. As a result, this review, though somewhat comprehensive, cannot account for all studies that have been related to the concept of peripheral narrowing.

Studies concerning the cue utilization theory were prevalent in the 1960's and

1970's and lent credence to Easterbrook's (1959) ideas. Because much of Easterbrook's







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theory is related to the conceptualization of arousal as a driving force which directs behavior in a way to reduce the desire for something, many early researchers investigated the cue utilization theory with this underlying theoretical backdrop. For instance, Eysenck and Willett (1962) classified subjects into high and low drive categories based on whether or not they had passed their entrance examination into a training school. Those who had passed were classified as high-drive subjects, while those who had not were classified as low drive subjects. Findings indicated that performance on a Tsal-Partington Numbers test was significantly greater for subjects characterized by high drive rather than those categorized in the low-drive condition. Though not a direct test of the cue utilization hypothesis, the results do suggest limited performance on this highly visually dependent task by those who were at presumably lower drive levels.

A direct examination of the cue utilization theory was conducted by Agnew and Agnew (1963) who used two different tasks, the Porteus maze and the Stroop ColorWord Interference test. Investigated was whether tasks which demand differing levels of attentional span would be effected differentially by increasing and decreasing stress levels as manipulate through electric shock. Success in the Porteus maze task, one that requires a wide range of cue utilization, was detrimentally influenced by electric shock. However, proficiency in the Stroop color word test, requiring a more narrow range of cue utilization, was facilitated by increased levels of arousal. These results provided substantial evidence for the validity of the cue utilization hypothesis.

A similar study was conducted by Tecce and Happ (1964) in which performance on a card sorting task and the Stroop Color-Word Interference test were assessed while







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stress levels were manipulated through electric shock. In this way, both relevant and irrelevant stimuli were presented that would be thought to impede performance of the central sorting task. Similar findings to those of Agnew and Agnew (1963) were obtained in which the shocked subjects performed better on the card sorting task than did a no-shock control group.

Another early study in which the cue utilization hypothesis was examined

incorporated a state measure of anxiety as assessed by the Taylor Manifest Anxiety Scale (TMAS: Zaffy & Bruning, 1966). Those participants who scored in the upper and lower 20% of the distribution of TMAS scores were selected for the study. The task consisted of learning 19 multiple choice items with 5 zeros for each 19 choice set. With the presentation of the zeros which had to be identified, either a relevant cue, an irrelevant cue, or no cue was presented. Findings showed that the low anxiety subjects performed worse than the high anxiety subjects, responding to both relevant and irrelevant cues while the high anxiety subjects responded to only the relevant cues, ignoring the irrelevant ones. Follow up experiments using the same task as Zaffy and Bruning (1966) but reducing the items from 19 to 15 and increasing the choices from 5 to 7 provided similar results (Bruning, Capage, Kozuh, Young, & Young, 1968).

In their first experiment, Bruning et al. (1968) manipulated anxiety through the

presence or absence of the test administrator, while in the second experiment, anxiety was manipulated by feedback regarding the subject's success and failure. Results in the first experiment replicated the findings of Zaffy and Bruning (1966). However, in Experiment 2, it was determined that the high drive subjects were superior in the irrelevant condition







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while the low drive subjects were superior in the relevant cue condition. Because subjects responded in different manners based on their anxiety disposition, results do provide some support for the attention narrowing idea.

Wachtel (1968) also conducted a study to examine the cue utilization hypothesis. However, his goal was to determine whether the cue utilization tendencies could be altered through offering participants a means of coping with the anxiety. The tasks consisted of a central continuous tracking task while identifying a random presentation of peripheral lights. Performance was based on a combined score of accuracy on the pursuit rotor task as well as reaction time to the peripheral fights. Three groups were tested in which one was a control group, the second group was told that it would receive random shocks that were independent of performance, and the third group was told that the longer it went without a shock, the stronger the shock would be. However, this group was also told that it would not be shocked as long as sufficient achievement was demonstrated, Results indicated that groups I and 3 reacted slower to the peripheral stimulus, suggesting that proficiency was impaired under the threat of electric shock, but not if the subjects had a means of escaping it. Thus, once again, it appears that stress affected peripheral task performance while facilitating the central task performance.

Hockey (1970) tested Easterbrook's ideas based on the notion that the differential selectivity effect observed between central and peripheral tasks is based not on the actual location of the stimuli but rather the allocation of priorities to the two tasks. He postulated that the high subjective probability of relevant signals to occur in the central field predisposes subjects to focus attention scanning of signals to the primary task.







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Using the manipulation of noise levels on central and peripheral tasks in which he ensured that all signals were detected (making objective and subjective probabilities identical), Hockey (1970) hypothesized that if a probability mechanism (priority allocation) was working, a greater facilitation of central detections in noise would occur only when the signal distribution was biased toward the center of the display. Attentional changes due to noise were inferred by the latency of response to central and peripheral locations. Support for the probability hypothesis was found. The response latency was faster when signals were biased toward the center of the visual field, but not when probability was equal of the signal being presented in the central or periphery. This explains, in part, that the fimneling which occurs is a function of the higher probability of relevant cues occurring in the central area, rather than as a function of the spatial location of the signal.

Bacon (1974), using a signal detection approach (Green & Swets, 1966), assessed the nature of stimulus loss by hypothesizing that there is not necessarily a loss of perceptual sensitivity to peripheral or irrelevant stimuli, rather a shift in the subjective decision criterion to respond to peripheral cues occurs. Due to the inconsistencies reported regarding whether performance on central tasks is enhanced or diminished, Bacon suggested that cues that initially attract less attention will show even less attention devoted to them while those that occupy the primary focus of attention attract an even higher degree of attention processing.

Using a dual task paradigm, Bacon's (1974) results supported Easterbrook's

(1959) hypothesis in that the increase in arousal (induced through electric shock) caused a funneling of attention toward central areas and away from the periphery. More pertinent







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to the hypotheses tested, however, it was determined that the decrease in attention devoted to the periphery was, in contrast to the expected result, due to a decrease in sensitivity rather than a shift in the subject's criterion for responding. Furthermore, due to the lack of ability to attend to both tasks as well in the aroused condition, the capacity limitation ideas of Easterbrook (1959) were also supported.

Though obviously laboratory-based and basic in nature, these early studies

established significant support for the attention narrowing idea. Eventually, these ideas were tested in more applied arenas. The less controlled studies and observations which will be summarized in the next section provided practical evidence for the viability of the attention narrowing idea in actual stress-producing environments. Applications of Peripheral Narrowing Research

Baddeley (1972) reviewed both anecdotal and empirical evidence of peripheral narrowing in "dangerous environments". Citing such examples as those from military combat observations, Baddeley (1972) provided substantial evidence of the impact of perceptual narrowing on real world situations. For example, he found that in the heat of battle soldiers will use their rifles much less efficiently than in training, the ratio of error to hits in combat increases, and tonnage of bombs needed to destroy a target increases. These are each examples of anecdotal reports that indict the deterioration of ability to use the most relevant cues in dangerous or stressful environments.

Weltman and Egstrom. (1966) and Wellman, Smith, and Egstrom. (197 1) applied the idea of peripheral narrowing to a deep sea diving environment under differing conditions of stress. The experimental conditions consisted of surface testing, shallow







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diving in an enclosed tank, diving at ocean depths of 20-25 feet (6-8 m), and simulated decompression dives in a pressure chamber. In general, they found that across conditions, performance was maintained on a centrally located monitoring task, but as stress level increased (i.e., the divers descended to more dangerous depths), attention to peripherally located light stimuli deteriorated. Though intriguing, these results may be contaminated by other extraneous factors such as the increase of nitrogen levels in the blood stream.

Surprisingly, relatively few investigations have been undertaken in sport settings to examine the effects of peripheral narrowing. Landers, Wang, and Courtet (1985) investigated peripheral narrowing with experienced and inexperienced rifle shooters. The central task was a target shooting task while the peripheral task was an auditory detection task. Although there were no differences found in secondary task performance between the experienced and inexperienced shooters, they did find that under high stress conditions, both groups shot worse.

As to other sport situations, two studies were conducted by Williams, Tonymon, and Andersen (1990, 1991) to help substantiate Andersen and Williams' (1988) model of athletic injury. In the model, Andersen and Williams (1988) indicate that a possible predisposition to athletic injuries may be precipitated by elevated levels of life stress, resulting in an inability to attend to peripheral stimuli. Support for this possibility was found in the two studies designed to test the model in which Williams, Tonymon, and Andersen (1990, 1991) found significant decrements in detection of peripheral cues while performing Stroop tasks under stressful conditions.







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The work done with regard to attentional narrowing in the sport context was

reviewed by Landers (1980). In the review, the Inverted-U in sports was explained using Easterbrook's cue utilization hypothesis. In sport, as with other domains, Landers suggested that performance is proportional to the number of cues utilized. At low arousal levels, there is a surplus of cues, including irrelevant cues that must be dealt with. With increasing anxiety levels, irrelevant stimuli are eliminated before relevant ones. Therefore, according to Landers, perhaps there is a bi-directional, reciprocal causality between arousal and performance in sport. Other theoretical proposals have been forwarded to account for the narrowing phenomenon. These will be reviewed in the next section. Theoretical Explanations for Peripheral Narrowing

Many theories have been forwarded to explain the consistent reduction in cue utilization during performance of tasks in stressful environments. Easterbrook (1959) proposes that if intensity cannot be discriminated between stimuli, a reduction in the employment of cues results. The reduction in the range of cue utilization can also be explained in the context of both Hull's (1943) Drive theory and the Yerkes-Dodson (1908) Inverted-U theory. In the Hullian sense, an increase in arousal (or drive) increases the stimulus generalization of a particular stimulus, resulting in the application of a trained response to stimuli other than the one of interest. In the Yerkes-Dodson argument, as arousal increases, some cues lose their ability to evoke the proper response, hence increased arousal, to a point, will be beneficial, after which decrements will result.

Easterbrook (1959) also implies that the cue utilization hypothesis fits nicely into Broadbent's (1957) idea of the single channel hypothesis of attentional capacity. Though







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less popular than current theories of attention, Broadbent's notion that there exists a single cue channel that will affect processing capabilities elsewhere in the system accommodated the cue utilization hypothesis effectively.

However, the idea can also be supported in the context of more recent

capacity/resource models such as proposed by Kahneman (1973) or Wickens (1984). These theories, though opposed with regard to the number of resource pools available, suggest a limit in the resources accessible to attain optimal attention as determined by priorities. In line with this view, one primary feature of high arousal levels is a narrowing of attention because the allocation policy is likely to shift away from the periphery and toward the central area. Thus, the allocation policy is also consistent with the probability results obtained by Hockey (1970).

In summary, it appears as though arousal tends to overload the system, narrowing the range of stimuli that are processed by impairing the memory traces of the stimuli of lesser importance, such that processing can continue to be devoted to the more central cues. It seems that narrowing could be due to both an impairment at the perceptual stage of processing and at the short term memory stage. However, the exact location of impairment has not been clearly identified.

Distraction

An idea that consistently recurs as an explanation for performance changes in both central and peripheral tasks is a narrowing of the attention beam in which irrelevant cues are somehow filtered from processing, either in the perceptual or encoding stage of analysis. However, virtually no one has assessed the impact of distractors, in this context







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and the concept of distraction has received very little attention from sport psychology and cognitive psychology researchers, in general. It seems logical, however, that the central task proficiency decrements that eventually occur as stress levels increase could also be explained in the context of distraction.

The lack of research directed toward understanding distraction is surprising

considering the imperative need to ignore distractors and focus only on the most critical cues in any performance situation. It is also surprising considering that the concept of distraction was actually addressed by William James as early as 1890. Though many of the ideas of James are being empirically investigated even at the end of the 20'h century, distraction continues to be a virtually untapped area of research on attention. Meanwhile, examples of athletes and other performers who have been victimized by distraction are numerous (Moran, 1996). The need to avoid distraction has prompted leading sport psychologists such as Orlick (1990) to suggest that it is one of the most important mental skills required to be successful in sport. Perhaps this is why virtually all mental training skills programs developed by sport psychologists are directed toward maintaining concentration on the task and appropriate cues. Interfering thoughts need to be regulated and irrelevant stimuli ignored.

Brown (1993; as cited by Moran, 1996) defines distraction as situations, events, and circumstances which divert attention from some intended train of thought or from some desired course of action. This definition is somewhat different from James' (1890) original conceptualization of distraction which was more directed toward the description of distracting thoughts and being "scatter-brained". Each of these views of distraction can








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be more easily understood if categorized in the context of internal and external types of distractors (Moran, 1996). Internal distractors refer to mental processes that interfere with the ability to maintain attention while external distractors are environmental or situational factors that divert attention from the task at hand. Each of the two types of distraction lead to a wandering of attention which Wegner (1994) has suggested is "not just the weakness of the will in the face of absorbing environmental stimulation ... but rather it is compelled somehow, even required, by the architecture of the mind" (p.3). Wegner (1994) has postulated that because the mind tends to wander, there is an attempt to hold it in place by repeatedly checking in to see whether it has wandered or not. Unfortunately, this results in a Catch-22 because by evaluating, attentional focus is inadvertently drawn to the exact thing that one is trying to ignore. He also suggests that when highly emotional, attentional resources are reduced and the mind is inclined not only to wander away from where it should be attending, but also wanders toward that which we are attempting to ignore.

Effects of distraction. Obviously, the typical effect of distraction is a decrease in performance effectiveness. The most plausible explanation for the decrease in performance when distracted by either external or internal factors is the decrease in available attentional resources for processing relevant cues. This idea is consistent with the limited capacity models of attentional resources proposed in different forms by various attention theorists (e.g., Allport, 1989; Kahneman, 1973; Shifflin & Schneider, 1977). Because attentional capacity is limited, resources directed toward the processing of distractors, reduces available resources for the processing of task-relevant information.







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This idea is supported by studies which have shown that distraction effects are greater for complex rather than simple tasks, and that distraction effects are greater as the similarity of distractors to relevant cues increases (Graydon & Eysenck, 1989). As tasks become more complex and distractor similarity increases, the attentional resources needed also increases due to a reduction in the automaticity of cue discrimination. Thus, any increase in distractibility will inevitably reduce the attentional capacity available for the primary task.

Distraction and stress. Though empirical evidence is scarce, many researchers have suggested that increases in emotionality as embodied by stress and the various components that make up stress (i.e., anxiety, worry, arousal) increase susceptibility to distraction. Emotional stress would be classified as an internal distractor as it does not exist except in the mind of the performer; but often internal distraction is caused by the erroneous perception of an external distractor (Anshel, 1995). Numerous examples to support the notion that stress impedes performance due to distraction can be found in verbal accounts and behavioral observations of "choking" in competitive environments. Moran (1994, 1996) provides substantial anecdotal evidence that the impact of anxiety is the absorption of attentional resources which could otherwise be directed toward the relevant task. Baumeister and Showers (1986) indicate that increased worry causes attentional resources to be devoted to task irrelevant cues while self-awareness theorists such as Masters (1992) suggest that under stress, not only is attention absorbed by irrelevant stimuli, but also the performance of normally automated skills becomes less automated as resources begin to be intentionally directed toward the process of the once-







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automated movement. Self-awareness, then, interrupts the normally fluid mechanics of the movement and inevitably decreases performance. Finally, Eysenck (1992) has provided empirical evidence that anxiety provokes people to detect stimuli which they fear, usually those that diverts them from attending to relevant information.

Paradoxically, it appears that there are two equally attractive explanations for the decrease in performance that occurs under high levels of stress. On one hand, proponents of the attention narrowing argument would suggest that under high stress levels (either anxiety or arousal induced), the visual field narrows to block out irrelevant information, and subsequently relevant information as stress continues to increase. On the other hand, proponents of the distraction argument would suggest that actually a widening of the attention field occurs such that irrelevant or distracting cues receive more attention then when under lower stress levels. Evidently, a controversy exists unless in some way, both mechanisms could be working at the same time. Perhaps, an increase in anxiety and/or arousal results in a narrowing of the attention field while at the same time, especially at higher levels of stress, increases susceptibility to distraction. Thus, many theories can account for how stress affects attention and the eventual impact of attention variation on performance, but none address specifically why this phenomenon occurs. By briefly examining research in visual attention, perhaps some clues as to what exactly is happening in these contexts may be surmised.

Visual Attention

It has long been known that there is a direct relationship between human

performance capabilities and the informational load as well as the response demands







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associated with a particular task (Fitts & Posner, 1967; Hick, 1952; Hyman, 1953). That is, as the level of response uncertainty (informational load) increases, so too does reaction time (RT). More importantly, laboratory research tends to indicate that RT to a single unanticipated visual stimulus is in the order of 180-220 ms, with this delay composed of latencies associated with stimulus detection, response preparation, and neural and muscular activity associated with a simple key press (e.g., Wood, 1983). Given these latencies, there is an apparent discrepancy between the obvious time constraints imposed by complex situations (those dominated by heightened levels of response uncertainty) and the ability of elite performers to routinely select and execute the most appropriate motor response.

Hardware vs. Software Approaches

In an attempt to understand this paradox, researchers have forwarded two

competing explanations. The first approach posits that expert performers differ from novices in that they possess advanced psychophysical and mechanical properties of the central nervous system (Abernethy, 1991; Burke, 1972). That is, proponents of this theory believe that experts have much faster overall RT's (simple, choice, and correction times) than do novices, and also possess greater optometric (static, dynamic, and mesopic acuity) and perimetric (horizontal and peripheral vertical range) attributes. In accord with the notion that humans are somewhat genetically programmed to possess these qualities, this perspective has been termed the "hardware" approach of expertise.

Support for the hardware approach, however, has been very limited. Studies by Helsen (1994), McLeod (1987), Starkes (1987), and Starkes and Deakin (1984), in which








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expert and novice athletes were compared on a number of laboratory tasks involving visual mechanisms (depth perception, static visual acuity) and processing abilities (simple and choice reaction time tasks) demonstrated no significant differences between the two groups. Thus, it appears as though expertise cannot be explained by a CNS advantage on the part of the expert.

In contrast to the hardware theory of expertise, proponents of the "software" approach argue that experts have a much greater knowledge base of information pertaining to their particular area of expertise. Differences in expert performance as compared to novices is thought to be the result of a cognitive advantage, rather than a physical advantage. For example, it is believed that expert athletes make faster and more appropriate decisions based on acquiring selective attention, anticipation, and pattern recognition strategies associated with their sport (Abernethy, 1991). That is, experts learn to know which cues to focus their attention on in their sport environment, and develop an understanding of the importance of these cues in predicting the nature of future sport related stimuli.

Support for the software approach to expertise has been repeatedly demonstrated in studies assessing decision time and accuracy responses for sport-specific situations (Bard & Fleury, 1976; Starkes, 1987). The same is true for the recognition and recall of structured elements of game situations in sports such as baseball (Hyllegard, 1991; Shank & Haywood, 1987), basketball (Allard & Burnett, 1985; Bard & Fleury, 1981), field hockey (Starkes, 1987), and volleyball (Borgeaud & Abernethy, 1987). Given the vast support for the software approach, the rest of this section will describe the cognitive







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elements of visual search that provide a better understanding of visual selective attention capabilities.

Visual Selective Attention

Theeuwes (1994) has defined selective attention as "the process of selecting part of simultaneous sources of information by enhancing aspects of some stimuli and suppressing information from others" (p. 94). Visual selective attention theorists are in agreement that there is primarily a two-stage process of selection: A preattentive stage and an attentive stage. The preattentive stage is thought to be unlimited in capacity and occurs in parallel across the visual display. Conversely, the attentive stage is capacity limited and is serial in nature. Preattentive parallel search has been supported by the notion that in simple search tasks, a flat function exists relating RT to the number of nontarget items that are varied (e.g., Egeth, Jonides, & Wall, 1972; Neisser, Novick, & Lazar, 1963). This flat function has been regarded as a pop-out effect (i.e., the non-target items pop-out of the display) and gives support to the notion that operations are carried out in a spatially parallel manner. Thus the three properties of preattentive search are unlimited capacity, independence of strategic control (exogenous, stimulus driven,), and spatial parallelism at various locations. Attentive search is characterized by functions that show a linear increase in RT as the number of non-target items increases. It is serial in nature, usually found in tasks with specific arrangements and in conjunctive search, and is probably capacity limited.

The specific nature of the attentive stage of visual search has been hotly debated by theorists who favor the concept of a late selection approach versus those who favor early







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selection. In regard to the notion that the attentive stage is limited in capacity, disagreement exists in regard to where the capacity is limited. Early selection theorists (e.g., Theeuwes, 1994; Treisman, 1988; Treisman & Gelade, 1980; Treisman & Sato, 1990) suggest that perceptual operations can be performed during the attentional stage that cannot be handled by the preattentive stage. Conversely, late selection theorists (e.g., Ailport, 1980; Duncan, 1980; Duncan & Humphreys, 1989) say that during the attentive stage, no perceptual operations are completed. Rather they propose that during the attentive stage, selection of one of the competing response tendencies elicited by the multiple stimuli occurs.

The idea of a limited spatial location property to attentive search has also been of issue. Specifically, early selection theorists have suggested that there is serial inspection of each item; a notion that is in line with several metaphors that have been forwarded to describe visual selective attention such as the spotlight (Posner, 1980; Treisman, 1988) and the zoom lens (Treisman & Gormican, 1988) which will be described later. The late selection theorists, on the other hand, do not allocate a special role to spatial attention.

Different types of search tasks have been used in an attempt to better understand the covert processes that distinguish the two stages and elements of the stages. The most popular of these tasks have been those characterized as primitive features and conjunctive features. In searches involving primitive features, Treisman and her colleagues (e.g., Treisman, 1988; Treisman & Gelade, 1980) have provided an abundance of evidence that these tasks can be carried out preattentively, exhibiting flat search functions which are the result of the popping-out of the most significant features. In these types of tasks,







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information does not need to be passed to the second stage because it is automatically selected and there are no attention limitations.

As mentioned, a special role for spatial attention has been advocated by those in

the early selection camp (e.g., Broadbent, 1982; Hoffman, 1986) to account for findings in which items with unique attributes have not been shown to pop-out when they were irrelevant to the task. This notion also contradicts the idea that top-down control maintains gaze until it comes close to a conspicuous object, and then bottom-up control takes over (e.g., Engel, 1977). Thus, spatial attention may not strictly adhere to the constraints of other types of primitive search tasks. These concepts support for the zoom lens metaphor of spatial attention in that people may intentionally vary the distribution of attention in the visual field (Eriksen & Yeh, 1985). In this case, search for the target proceeds serially, omitting the need for the preattentive stage period. This is in line with a series of studies by Eriksen and his colleagues in which it was shown that non-target items may have a detrimental effect if they are spatially close to the target but have no effect when they are further away. As will be seen later, the idea that attentive search is serial is also an important factor in being able to infer that the fine of sight coincides with attention.

Conjunctive feature search tends to show a linearly increasing relationship between the number of different features in the task, and whether the target is absent or present in the display. According to the early selection account, the reason this occurs is due to the need for serial search rather than parallel operations only. However, under certain circumstances such as relatively large displays or search for some particular attributes (depth, movement), search functions become relatively flat (Pashler, 1987; Wolfe, Cave, &







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Franzel, 1989). These results can all be accounted for however, by the revised FIT which incorporates some top-down mechanisms in conjunctive search so that non-targets (even though conjunctive) which are very dissimilar to the target do not have the same probability of entering into the attentive stage as do those that are similar.

Stage-s of visual search. As mentioned, the visual search process consists of two distinct stages (Jonides, 1981). The first of these, the preattentive stage, involves unlimited capacity in which visual information from sensory receptors is held in a rapidly decaying visual sensory store. The literal representation of this briefly held information is labeled "the icon" (Neisser, 1967). This stage of visual search is thought to be automatic, with parallel processing of information, and demonstrates crude feature analysis or detection.

The second stage of visual search, termed the focal or attention demanding stage, refers to the process through which selected items in the iconic store are subjected to a more detailed analysis (Jonides, 198 1: Remington & Pierce, 1984; Yarbus, 1967). The concept of selective attention in this context focuses on the determination and passage of specific icons from the preattentive stage to the focal stage. It is in this focal stage that only those cues (icons) in the sport environment that are deemed pertinent will be attended to and used by the athlete.

The process of selecting and processing information from only specific aspects of an entire visual display entails both overt visual orienting and covert mechanisms that occur during eye fixations. Overt visual orienting includes the movement of the eyes and head to focus on a particular spatial location. Both top-down (cognitively driven) and







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bottom-up (stimulus driven) processes control the 'macrostructure' of the scanpath (LevySchoen, 198 1), or where the visual receptors are focused. Covert orienting mechanisms are unseen processors that occur within the attention allocation resources of the brain and are also influenced by both top-down and bottom-up control (e.g., Posner & Cohen, 1984).

Temporal aV. cts. Though covert orienting mechanisms are, by definition hidden, studies of the covert measures of visual orienting have been reported for the past 20 years based on the cost-benefit paradigm developed by Posner and Snyder (1975) and Posner (1978, 1980) to investigate mental chronometry; the time course of information processing. Much work in this area led to the conclusion that reaction time decreases give the perceiver a head start in shifting attention to the target's location. However, questions arose regarding the effect of location cueing as being related to perceptual sensitivity changes or changes in the observer's response criterion. Using SDT paradigms, results have indicated that the benefit occurs mainly through a change in the perceptual sensitivity (e.g., Downing, 1988). These results have further been substantiated by overt measures of mental chronometry.

Specifically, Saitoh and Okazaki (1990) examined the temporal structure of visual processing while performing a digit string search and matching task in an effort to decompose the stages of reaction time. The time used to encode and memorize the standard digit string increased linearly with each addition to the digit string. Also, it was found that the entire visual search time and RT was associated more with the number of eye fixations rather than the duration of the fixations. This provides support for the idea







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that each shift of eye fixation provides a shift in visual attention as well and that the ability to measure the chronometry of information processing can be accomplished through the study of eye movements.

Though the results obtained by Saitoh and Okazaki (1990) are encouraging, many questions have been raised regarding the ability to infer visual attention shifts from eye movements (Klien, 1994; Viviani, 1990). Attempts to clarify this issue have typically involved determining whether saccadic eye-movements can be made without concomitant eye-movements to the location. As mentioned, when highly salient aspects of the display exist, stimulus driven (bottom-up) control takes over (Engel, 1971, 1974, 1977). Cognitive control (top-down) of the scanpath is most evident when a particular aspect of the display is of interest. Goal driven visual search strategies are produced on the basis of cognitive control while stimulus driven responses appear to be elicited by the stimuli themselves and take on the properties of reflexive shifts to the visual field (Yantis & Jonides, 1984). Most research has indicated that while there appears to be a close relationship between stimulus driven saccades and attention shifts, less convincing evidence exists for the validity of inferring attention shifts from goal driven initiation.

Research indicates that in the case of stimulus driven saccades, the shift of

attention occurs before the initiation of the saccade (Wright & Ward, 1994). In their work looking at express saccades, Fischer and Weber (1993) have shown that attention must first be disengaged from the fixation point at the origin prior to target onset. Posner accounted for these criticisms through an elaborative account of the disengage, shift, reengage sequence that is probably mediated by activity in the posterior parietal cortex, the







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superior colliculus, and the pulvinar region of the thalamus (Posner, Peterson, Fox, & Raichle, 1988). However, even in this description, most data were gathered from stimulus driven rather than goal driven attentional shifts. An in-depth discussion of the benefits and criticisms directed toward inferring attentional processing from eye-movement recording devices will be provided in the following sections of the review. Metaphors of Visual Attention

Though overt mechanisms of visual selective attention are relatively simple to

observe, covert attentional shifts are much more difficult to ascertain. As a result, much debate surrounds the ability to infer cognitive processing from overt observations. Due to the inability to precisely describe the association between line of fixation and attentional processing, several different models have been posed to account for the psychological mechanisms underlying attentional shifts. First, movement models suggest that the focus of attention is shifted from one location to another in an analog or discrete manner (the spotlight metaphor, e.g., Posner, 1980). Another popular metaphor is focusing models which suggest that attentional focus can change from a broader, more diffuse state, then back to a finer, more concentrated state at the destination of the shift (the zoom lens idea, Eriksen & St. James, 1986). Finally, resource distribution models postulate an attentional alignment process that does not involve a movement or a focusing component (Laberge & Brown, 1989). Investigations of each of these models have provided data to support them. However, as will be addressed later, Wright and Ward (1994) suggest that the reason for many discrepancies is the use of a variety of experimental paradigms, tasks, and cueing mechanisms.







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The primary question that arises from the debate is whether or not the line of sight is independent of selective attention shifts. The evidence described so far in reference to the stages of processing has been gleaned primarily from studies in which line of sight is inferred from RT and other indirect measures of fixation location. However, much research has been completed with eye-movement recording devices to determine precisely when and where attention shifts during information processing of visual stimuli. Eye-Movement Recording

The ability to infer attention shifts from eye movements was first investigated by Helmholtz in the 19th century when he discovered that he could shift his point of gaze to illuminated letters before his actual attention shifted there (the latency of a normal saccade is approximately 220 ms (Fischer & Weber, 1993). James (1890) described attention shifts as being under involuntary or voluntary control which was the genesis for the study of exogenous (bottom-up) versus endogenous (top-down) processing. However, much research in the area was not possible until the 1970's with the advent of sophisticated eye monitoring equipment. Even with the additional data acquired through eye movement recording devices, researchers have been unable to provide indisputable evidence for the notion that the line of sight coincides with the line of attention.

While the visual search paradigm would appear to be a fluitful means of assessing selective attention strategies, it is not without criticism. Before concluding this section on visual search, it is necessary to discuss some of the limitations and potential problems that currently exist in eye movement recording research. These concerns are reflected in both







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the assumptions of selective visual attention theory, and in the eye movement recording techniques themselves (Abernethy, 1988; Vviani, 1990).

According to Abernethy (1988), the first major limitation of eye movement recording lies in the assumption that visual search orientation is reflective of actual allocation of attention. That is, visual fixation and attention are one in the same (where one looks is where one attends). This notion, however, has been refuted by Remington (1980) and Remington and Pierce (1984), who demonstrated that attention can be allocated to areas other than the foveal fixation point. Indeed, attention can be allocated to areas in peripheral vision, a mode that cannot be measured with current visual search equipment (Buckholz, Martinelli, & Hewey, 1993; Davids, 1987).

A second limitation of current visual search recording involves the high trial-totrial variability that is evident in the literature (Abernethy, 1988). These variable patterns make reliable conclusions about the relevance of specific visual cues difficult. Related to this limitation is the fact that the majority of studies include relatively low sample sizes (often n = 6 or 8), thus causing internal and external validity concerns.

A third, and perhaps most important, limitation of eye movement recording

focuses on the issue of visual orientation and information pick-up. As Abernethy (1988) notes, merely "looking" at visual information does not necessarily equate with "seeing" (or comprehending) this information. Thus, a person may fixate upon pertinent cues in the visual array, but there is no guarantee that he or she is actually attending to or utilizing these cues. In order to empirically determine whether one is actually "picking-up" and using the cues available in the visual field, the technique of cue occlusion has been used.




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CHANGES IN VISUAL SEARCH PATTERNS AS AN INDICATION OF
ATTENTIONAL NARROWING AND DISTRACTION DURING A
SIMULATED HIGH-SPEED DRIVING TASK UNDER
INCREASING LEVELS OF ANXIETY
By
CHRISTOPHER M. JANELLE
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1997

ACKNOWLEDGMENTS
There are many people to whom I am indebted for their guidance and patience
throughout my doctoral education and especially during the dissertation process. I would
like to begin by thanking my wife, Carol, and my little buddy, Matthew, for their
inspiration and understanding over the past four years. Words cannot express the love and
appreciation I have for you both. Similarly, I would like to express my gratitude to my
parents, Jean and Fran Janelle, for their support and encouragement during my graduate
education and throughout my life. The value system and work ethic they instilled in me
are what made this possible.
A very special thank you goes to my mentor and dissertation chair, Dr. Robert N.
Singer. His scholarly example and practical lessons have greatly enhanced my professional
and personal development. He has truly embodied the term “mentor” by providing me
with the tools and opportunities needed to develop into a young scholar while adding
constructive criticism and a sincere pat on the back when needed. His influence will
always be greatly appreciated.
I would like to express my sincere thanks to my committee members, Dr. James H.
Cauraugh, Dr. Ira Fischler, Dr. Milledge Murphey, and Dr. L. Keith Tennant, for their
support and helpful comments in the completion of this project. In addition to the
dissertation experience, each has provided much in their own way to my development and
11

for that I am grateful. The many experiences I have shared with each of you, both
academically aind otherwise, will not be forgotten.
This study would not have been possible without the willingness to participate and
generosity of Dr. Mark Williams who allowed me to use his eye-tracking equipment and
provided interesting ideas and conceptual contributions during the formative stage of this
project. Furthermore, I would like to acknowledge the technical assistance of Mark
Tillman, Luis Maseda, and Dr. Jeff Bauer who helped put everything in motion. Also, I
am thankful to Beth Fallen, Wisug Ko, and Dr. Andrea Behrman for helping with data
collection, reduction, and analysis.

TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS ii
LIST OF TABLES vii
LIST OF FIGURES viii
ABSTRACT ix
CHAPTERS
1 INTRODUCTION 1
Attentional Narrowing 3
Distraction 12
Arousal and Anxiety 15
A More Comprehensive Next Step 18
Statement of the Problem 22
Hypotheses 23
Definitions of Terms 29
Assumptions 32
Significance of the Study 33
2 REVIEW OF LITERATURE 37
Stress and Human Performance 39
Anxiety, Arousal, and Attention 63
Visual Attention 79
Visual Attention and Driving 95
Visual Attention and Sport 112
Summary and Future Directions 115
Visual Search as an Indicator of Distraction
and/or Narrowing 118
iv

3 METHODS 121
Participants 121
Instruments and Tests 122
Measurement Recording Devices 126
Procedure 130
Data Analysis 136
4 RESULTS 139
Anxiety and Arousal 139
Performance Data 142
Visual Search Data 151
Multiple Regression Analyses 157
Manipulation Checks 160
5 DISCUSSION, SUMMARY, CONCLUSIONS,
AND IMPLICATIONS FOR FURTHER RESEARCH 162
Discussion 164
Visual Search Data 175
Findings Which Contradict and Augment
Previous Research 180
Summary 193
Conclusions 195
Issues for Future Consideration 196
A Final Comment 201
REFERENCES 203
APPENDICES
A COMPETITIVE STATE ANXIETY INVENTORY - 2
(CSAI-2) 223
B INFORMED CONSENT FORM 225
C PRE-RACE INSTRUCTIONS 227
D FAMILIARIZATION SESSION INSTRUCTIONS 230
E PRACTICE SESSION INSTRUCTIONS 233
F COMPETITION SESSION INSTRUCTIONS 235
G POST-EXPERIMENT COMMENTS 237
H PEARSON PRODUCT-MOMENT
CORRELATION COEFFICIENTS 239
v

BIOGRAPHICAL SKETCH
242
vi

LIST OF TABLES
Table Page
3.1 Experimental design 136
4.1 Cognitive Anxiety Levels for Each Group Across Sessions 1-3 140
4.2 Change from Baseline HR for Each Group Across Sessions 1-3 142
4.3 Driving Performance (Lap Speed) 145
4.4 Number of Major Driving Errors 147
4.5 Mean Response Time Across Sessions 1-3 149
4.6 Mean Number of Peripheral Light Misidentifications 151
4.7 Number of Saccades to Peripheral Stimuli 153
4.8 Number of Fixations to peripheral Locations Across
Sessions 1-3 155
4.9 Stepwise Multiple Regression Analysis Predicting Lap Speed
with Activation Data Across Sessions 1-3 158
4.10 Stepwise Multiple Regression Analysis Predicting Response Time
with Activation Data Across Sessions 1-3 159
4.11 Stepwise Multiple Regression Analysis Predicting Misidentifications
of Peripheral Stimuli with Activation Data Across Sessions 1-3 159
4.12 Stepwise Multiple Regression Analysis Predicting Exogenous
Saccades with Activation Data Across Sessions 1-3 160
Vll

LIST OF FIGURES
Figure Page
4.1 Changes in cognitive anxiety for each group across sessions 1-3 141
4.2 Change in HR from baseline rates for each group during
sessions 1-3 143
4.3 Lap speed for each group across sessions 1-3 146
4.4 Number of major driving errors for each group across sessions 1-3 148
4.5 Mean response time across sessions 1-3 150
4.6 Mean number of peripheral light misidentifxcations 152
4.7 Number of saccades to peripheral stimuli across sessions 1-3 154
4.8 Number of fixations to peripheral locations across sessions 1-3 156
vm

Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
CHANGES IN VISUAL SEARCH PATTERNS AS AN INDICATION OF
ATTENTIONAL NARROWING AND DISTRACTION DURING A
SIMULATED HIGH-SPEED DRIVING TASK UNDER
INCREASING LEVELS OF ANXIETY
By
Christopher M. Janelle
August, 1997
Chairperson: Robert N. Singer
Major Department: Health and Human Performance
The purpose of this investigation was to examine the influence of distraction on the
attentional narrowing construct in the context of a dual task driving simulation under
varying levels of anxiety. Forty-eight women were randomly assigned to one of six
experimental conditions: distraction control, distraction anxiety, relevant control,
relevant anxiety, central control, and central anxiety. Those assigned to central
conditions only performed a driving task while the other four groups were required to
identify peripheral lights in addition to driving. Those in anxiety conditions were exposed
to increasing levels of anxiety which was manipulated by instructional sets. All
IX

participants completed three sessions consisting of 20 trials each during which measures of
cognitive anxiety, arousal, visual search patterns, and performance were taken.
Data indicated that as those in dual task conditions reached higher levels of
anxiety, their ability to identify peripheral lights become slower and less accurate.
Furthermore, the ability to drive for those in the distraction and central groups was
impaired at high levels of anxiety. The decrease in driving proficiency for those in the
distraction anxiety condition was highly associated with changes in visual search patterns
which became more directed toward peripheral locations. In the central anxiety
condition, driving proficiency was influenced by an increased tendency to make minor
errors which could be attributed to a more cautious driving style when highly activated.
Overall, performance on both central and peripheral tasks was worse for those in the
distraction anxiety condition during the period of highest anxiety. Furthermore, visual
search patterns were more eccentric during this session for this group.
Results suggest that drivers who are highly anxious and aroused experience an
altered ability to process peripheral information at the perceptual level, leading to a
decrease in attentional resources available for the processing of central information. In
addition, it appears that this effect is amplified when distractors as well as relevant cues
are present in peripheral areas. Implicated in the study is the role of visual search patterns
and distractors in the dual task context. Suggestions are made to revise the current notion
of attentional narrowing to include the role of distraction as a contributor to performance
variability.
x

CHAPTER 1
INTRODUCTION
Anyone associated with sport as either an athlete, coach, or spectator, can
remember instances in which the pressure of competition transcended the typical
commentary that it was "just a game". Sport is replete with occasions such as a crucial
free throw, a clutch base hit, a game winning field goal, or a breathtaking lap at the finish
line, in which athletes either overcome the excessive demands of the moment and perform
at their highest levels or choke under the extreme circumstances of the situation. More
often than not, it is the ability to maintain concentration when faced with these stressors
that determines the outcome of sport contests. However, even the greatest athletes
occasionally succumb to these inordinate demands, causing sport psychologists to
question why this occurs and what mechanisms contribute to diminished performance.
It has been suggested that the ability of athletes to execute effectively in
exceptionally stressful environments is related to the impact of arousal and anxiety on the
capability to maintain concentration. Though a number of researchers have suggested that
excessive stress influences information processing capabilities by overloading the limited
attentional resources available, much evidence provided to support this claim is anecdotal
or observational in nature (Moran, 1996). Ignoring the underlying mechanisms
1

2
responsible for general changes in performance renders it impossible to prescribe
competent interventions that specifically address the mechanisms which are being affected.
The paradigm shift in the study of cognitive psychology that occurred in the late
1950s and early 1960s brought with it a greater understanding of the specific processes
that are involved with attending to and processing information. However, the research has
been criticized due to its reductionistic nature. Ignored have been other relevant factors,
particularly emotions that influence attentional processes and subsequent achievement
(Kremer & Scully, 1994; Moran, 1996). By not studying the interaction of emotions,
attention, and performance, the generalizability of research on attention has been
somewhat limited. Thus, much still needs to be understood about dynamic sport settings
in which attentional flexibility is crucial under conditions of severe time constraints and the
stress associated with the competitive drive to win.
Of interest here is the peripheral (or attentional) narrowing phenomenon which has
been reported to occur under high stress levels (Easterbrook, 1959). Though intriguing,
and attracting much research interest to the present day, the underlying mechanisms
responsible for the narrowing (or tunnel vision effect) which presumably occurs in
stressful situations remain a mystery. Questions are still unanswered regarding the specific
components of the stress response (i.e., cognitive or somatic anxiety, and/or arousal) that
influence performance. Specifically, does narrowing occur due to heightened levels of
cognitive anxiety, somatic anxiety, mere arousal, or some combination of these factors?
Another factor that has contributed to the confusion is that sport psychology
researchers have been reluctant to give up the notion that the Inverted-U hypothesis

3
(Yerkes-Dodson, 1908) is the one and only description of the stress/performance
relationship. However, contemporary models have been proposed that address the
specific components of stress and prescribe testable hypotheses that are quite different
from the very general Inverted-U description of the relationship of stress with
performance.
Furthermore, the specific aspects of performance (i.e., stimulus detection and
discrimination, response time, response accuracy, and others) that are influenced by
changes in affective states have received relatively little empirical investigation due to the
favoring of more easily understood global performance measures. As mentioned, by
failing to address the specific parameters that are impacted by stressful stimuli, it is
impossible to understand more precisely what is happening; and therefore, what to do
about it.
Finally, many of the performance changes in stressful environments that have been
attributed to attentional narrowing could possibly be explained in the context of
distraction. In spite of their obvious application to understanding sport performance, the
study of attentional narrowing and distraction in the context of dynamic sports is
nonexistent.
As may be evident, advancement beyond current understandings of the
stress/performance relationship is warranted for both theoretical and practical reasons.
Thus, my intent was to investigate specific affective factors that influence attentional
parameters and, ultimately, performance in an ecologically valid dual task situation under
stressful circumstances. To provide further description of the specific issues to be

4
addressed and to justify the intended experiment, background information on the topics of
interest follows.
Attentional Narrowing
It has been suggested that the ability to attend to, select, and process the most
critical cues in a situation is one of the most important skills required for high level
performance in sport (e.g., Abemethy, 1993). In support of this idea, experts have
consistently exhibited what has been called a "cognitive advantage" over less skilled
participants, being able to process the same information in a more efficient and effective
manner (Starkes & Allard, 1993). Though this is interesting and valuable information for
both cognitive and sport psychology researchers, the ability to demonstrate this cognitive
advantage has rarely been investigated under imposed stressful states in a realistic sport
context or other meaningful situation. However, an early theory that directly addressed
the ability to select cues and use them effectively under different emotional conditions is
the cue-utilization hypothesis described by the concept of attentional narrowing.
Easterbrook (1959) produced the most influential article on the topic of cue
utilization based on the findings of Bahrick, Fitts, and Rankin (1952) and others (e.g.,
Bruner, Matter, & Papanek, 1955; Callaway & Dembo, 1958; Callaway & Thompson,
1953; Eysenck, Granger, & Brengelman, 1957; Granger, 1953). Easterbrook’s primary
theoretical contribution was the notion that as level of arousal increased to a certain point,
performance in a dual-task situation would be variable between the two tasks.
Specifically, he suggested that with an increase in activation to moderate levels, central
task achievement would be facilitated due to the blocking of irrelevant cues in the

5
periphery from being processed. Furthermore, he postulated that at this moderate level,
performance in tasks requiring less of a central focus (i.e., a peripheral focus) would
deteriorate due to a blocking of these cues. Finally, performance in central tasks would be
expected to deteriorate if arousal level reached a heightened state in which the funneling
effect prohibited attention to relevant cues that are integral to performance of the central
task. In other words, Easterbrook (1959) suggested that the degree of facilitation or
disruption caused by emotional arousal is dependent on the range of cues needed to
perform a task effectively and how those cues are attenuated by emotional states.
Unfortunately, relatively few investigations have been undertaken in sport settings
to examine the effects of peripheral narrowing, or if this phenomenon exists. This is
surprising considering that typical sport situations, especially at higher levels of expertise,
often occur in extremely stress-provoking environments. In one of the only studies done
in the context of sport, Landers, Wang, and Courtet (1985) investigated peripheral
narrowing with experienced and inexperienced rifle shooters. The central task was a
target shooting task and the peripheral task was an auditory detection task. Although
there were no differences found in secondary task performance between the experienced
and inexperienced shooters, both groups shot worse under high stress conditions.
Also with relevance to sport, two studies were conducted by Williams, Tonymon,
and Andersen (1990, 1991) that substantiated Andersen and Williams' (1988) model of
athletic injury. In the model, Andersen and Williams (1988) indicate that a possible
predisposition to athletic injuries may be precipitated by elevated levels of life stress that
result in an inability to attend to threatening peripheral stimuli. Support for this possibility

6
was provided by Williams et al. (1990, 1991) who showed that decrements in the ability to
detect peripheral cues were found to occur while individuals performed Stroop tasks
under stressful conditions. Based on their conclusions, the researchers suggested that
attentional narrowing may be a dispositional factor that predicts athletic injuries because
athletes are unable to notice potentially dangerous peripheral stimuli such as other players,
dangerous terrain, and the like.
Though not directly sport-related, other perceptual-motor activities have been
investigated with respect to the ability to attend to central and peripheral dual tasks. Of
these, perhaps the most relevant to sport is driving an automobile (unfortunately, many
highway drivers forget that it is not a sport!). While driving, there is a limited amount of
attentional resources that can be devoted to an almost infinite number of stimuli at any
point of time. As the task of driving becomes more complex due to decreased visibility,
bad weather, heavy traffic, mechanical malfunction, sudden unexpected obstacles, fatigue,
and other factors, the automaticity of driving becomes less instinctive and demands more
attentional resources (Shinar, 1978). In these conditions, drivers may experience
information overload and may be more likely to place themselves in possibly risky
situations.
During normal driving, the driver tends to focus on the central task of keeping the
vehicle "on the straight and narrow" so to speak, maintaining control of the vehicle based
on the constraints of the driving environment (e.g., speed limits and lane markers).
However, when confronted with an object or event that is not in the central (or foveal)
field of vision, the eyes are normally moved from the central task to focus more directly on

7
the information that has been attended to in the periphery. Based on the information
provided by the newly attended stimulus, a decision must be made regarding whether or
not to change driving behavior. These alterations occur both in serial and in parallel
depending on the specific situation presented (Schneider & Shiffrin, 1977; Shiffrin &
Schneider, 1977). To make matters more complicated, all of these processes are often
limited by extremely restrictive temporal constraints (Shinar, 1978).
Recent research has been directed toward understanding, more fully, the ability of
drivers to extract meaningful information from signals along the roadway. In particular,
many studies have been done on the demands of the external environment while driving,
such as the perception and processing of road signs.
Hughes and Cole (1988) investigated the effect of attentional demands on eye
movement behavior during simulated road driving. They attempted to assess how a
driver's performance was effected by purposely directing attention to particular features of
the road environment under single and dual task conditions. Results showed that across
groups, 25% of the fixations were located at the actual focus of expansion while 80% of
the remaining fixations were centered within 6 degrees of the focus of expansion.
Therefore, results suggest that if road signs are located beyond the 6° point in the display,
they will probably not be perceived. Also, increasing task specificity resulted in more
fixations to the left part of the display (the area where most signs were posted) with a
corresponding decrease in fixations to the center of the display. Furthermore, the addition
of the dual task paradigm resulted in two predominant effects on eye movements. First,

8
eye fixations tended to move closer to the central region. Second, the distance of
peripheral fixation also moved closer to the focus of expansion.
Therefore, it can be concluded that in the typical dual task condition which
requires increased attentional resources, there is insufficient spare resources to perform
the peripheral task without more fixation resources. The additional demand of the
secondary t ask not only necessitates more fixations to the region of the task, but also
reduces the extent to which the rest of the visual display is searched. Though not
suggested by the researchers, these results could be accounted for in the context of
attentional narrowing and/or distraction.
A similar study was conducted by Luoma (1988) to examine the types of roadway
landmarks that are perceived and remembered better than others. As may be evident from
the results of Hughes and Cole (1988), drivers do not perceive nearly all of the traffic
signs that they encounter, even in situations where they have been precued to look for the
signs. In situations imposing increasing demands and challenges to the driving task, the
perception of signs is even less than in "normal" driving conditions.
Luoma (1988) tested the idea that the more casual the perception or the larger the
target signs, peripheral vision is used to a greater extent. However, an important function
of peripheral vision is to identify targets of importance to the driving task and, if the
situations warrants, direct focal vision to the sign. To investigate these ideas, participants
actually drove a 50 km route while outfitted in eye movement monitoring equipment.
Results indicated that correct perception only occurred, for the most part, when the target
was fixated foveally. Also, whether the sign was perceived or not depended heavily upon

9
the relevance of the sign to the driving task. For example, 100% of all speed limit targets
were perceived foveally and were recalled while signs such as pedestrian crossings,
roadside advertisements, and houses were perceived much less, if at all. In fact, no
subjects recalled passing "pedestrian crossing" signs even though 25% of them fixated on
it. It appears that the processing devoted toward identifying the signs was dependent
upon the relevance of the sign to the actual driving task and its informativeness.
Perhaps the most relevant study reported to date to examine the processing of
visual stimuli in both central and peripheral fields was conducted by Miura (1990). The
primary purpose was to assess changes in the useful field of view (UFOV: the information
gathering area of the display) under situations of varying task demands and to determine
the corresponding variation in the acquisition of visual information that accompanied these
changes. Mackworth (1976) has suggested that the UFOV will vary with changes in the
situational characteristics or specific demands of the environment. The study was
conducted under actual driving conditions in which the subject had to navigate along a
roadway, in daylight conditions.
Results showed that RT to peripheral lights increased as the situational demands
increased. Furthermore, response eccentricity became shorter, suggesting that fixations
had to occur closer to the actual target location to acquire the necessary information. In
general, this suggests that peripheral visual performance is impeded by an increase in
situational demands. Specifically, it appears that the UFOV narrowed at each fixation
point, and the latency of each fixation lengthened. Also, the detection of targets required
a greater number of eye movements in more demanding driving situations.

10
To explain these findings, Miura (1990) postulated that the depth of processing of
an object in focus increases as the situational demands increase. Specifically, the latency
period of the eye movements following fixation on a target lengthens as the demands
increase. In more demanding situations, when a narrower UFOV exists, information
pickup at the fixation point appears to be slower, causing a delay in the attentional
switching capabilities of the driver. Other evidence (Miura, 1985) indicates that with
lower demands, the fixation points shift to the inner area of the UFOV while during highly
demanding situations, fixations shift toward the outer part of the UFOV. Thus, as a result
of the deeper processing that occurs at each fixation point, participants attempt to acquire
information more efficiently in the periphery while using a smaller UFOV. Another
hypothesis is that as demands increase, they develop a stronger tendency to search for
information in the periphery, a phenomenon referred to as "cognitive momentum", and a
possible adaptation of the system to utilize attentional resources in the most efficient
manner to deal with the increase in demands (Miura, 1986).
Though interesting and conceptually valuable, Miura's (1985, 1986, 1987, 1990)
work fails to take into account what might be a primary influence on the decrement in
peripheral performance and the apparent narrowing of the UFOV. Though not mentioned
in any of his papers, a possible explanation for these findings can be attributed to the
increase in arousal and anxiety that accompanies tasks that increase in complexity and
demands (Easterbrook, 1959). Although eye movements have been recorded in a variety
of real world and simulated driving situations, researchers have not attempted to examine
other affective inputs to the system that may account for differences in performance.

11
Furthermore, in Miura's (1990) study, as well as others, performance in the central driving
task was not recorded.
Like normal driving, the sport of auto racing demands the coordination of an
extensive repertoire of perceptual and motor skills. However, the performance difficulty
of these skills is significantly compounded by the competitive nature of the sport. In
addition to mastering typical driving skills, the shear speed of the car requires split-second
decision-making and intense concentration on the most relevant cues for the entire
duration of the race. An ill-advised momentary attentional shift or distraction can be (and
often is) catastrophic under these circumstances. Unfortunately, virtually no attempt has
been made to empirically assess these factors in auto racing.
As may be evident from the sparse research that has been conducted related to
sport and driving, no study has addressed the issue of attentional narrowing in the context
of dynamic, reactive sport environments. However, perhaps the theoretical mechanisms
that underlie results from laboratory tasks and the few sport situations that have been
studied are common to all dynamic sports as well.
The reduction in the range of cue utilization was originally explained in the context
of both Hull's (1943) Drive theory and the Yerkes-Dodson (1908) Inverted-U hypothesis.
However, the cue utilization hypothesis can be more accurately accommodated with more
recent attentional capacity (or resource) theories (Kahneman, 1973: Wickens, 1984) which
propose a limit in the resources available to attain optimal attention. Proponents of this
view (e.g., Landers, 1980) suggest that one primary feature of high arousal levels is a
narrowing of attention because the allocation policy is likely to shift away from the

12
periphery and toward the central area of a visual display. This notion has been supported
by studies that indicate the probability of cues in central areas of a display to draw more
attentional resources increases under stressful situations (e.g., Hockey, 1970).
To summarize the attentional narrowing point of view, stress (either arousal or
anxiety produced) tends to overload the system, narrowing the range of stimuli that are
perceived. When this occurs, information processing capabilities appear to operate in a
dysfunctional manner. At the initial stages of perception, possibly various cues are
ignored, never reaching later stages of processing. On the other hand, the actual
informational value of the stimulus may not be utilized effectively due to an inability to
distinguish the stimulus as relevant or irrelevant and respond accordingly. Thus,
narrowing could be due to a dysfunction at the perceptual stage of processing and/or at
the short term memory stage. Quite possibly, impairment occurs at both stages of
information processing (Bacon, 1974; Hockey, 1970). However, the exact location of
information processing dysfunction has not been substantiated. Furthermore, an alternative
explanation for what happens to performance and attentional allocation under stressful
conditions is plausible.
Distraction
As described previously, the idea that consistently recurs as an explanation for
performance changes in both central and peripheral tasks in stressful environments is a
narrowing of the attentional beam in which cues are somehow filtered from processing at
either the perceptual or encoding stage of analysis. However, the influence of distractors
in the context of peripheral narrowing has not been investigated, and the concept of

13
distraction has received very little attention from researchers. It seems logical, however,
that the apparent narrowing of attention that occurs under stressful conditions could also
be explained by the notion that anxious or aroused performers are more inclined to be
distracted.
The lack of research directed toward understanding distraction is surprising
considering the need of people in many work, entertainment, sport, and other situations to
ignore distractors and focus only on the most critical cues in order to effectively perform
the task. Examples of athletes and other performers who have been victimized by
distraction are numerous (Moran, 1996), prompting Orlick (1990) to suggest that the need
to avoid distraction is one of the most important mental skills required to be successful in
sport.
Brown (1993) defines distraction as situations, events, and circumstances which
divert one’s mind from some intended train of thought or from some desired course of
action. This definition is somewhat different from William James' (1890) original
conceptualization of distraction which was directed toward the experience of distracting
thoughts and being "scatter-brained". Each of these views of distraction can be more
easily understood if categorized in the context of internal and external types of distractors
(Moran, 1996). Internal distractors refer to mental processes that interfere with one’s
ability to maintain attention while external distractors are environmental or situational
factors that divert attention from the task at hand. Wegner (1994) has postulated that
because the mind tends to wander, an attempt is made to hold it in place by repeatedly
checking to determine whether it has wandered or not. However, in this process, the mind

14
is inadvertently drawn to the exact thing that one is trying to ignore. He also suggests that
when highly emotional, attentional resources are reduced, and the mind is inclined not only
to wander away from where it should be attending, but is also diverted toward that which
one is attempting to ignore.
The typical effect of distraction is a decrease in performance effectiveness. The
most plausible explanation for this is that when one is distracted by either external or
internal factors, there is a decrease in available attentional resources for the processing of
relevant cues. Like attentional narrowing, this idea is consistent with the limited capacity
models of attentional resources proposed in different forms by various attention theorists
(e.g., Allport, 1989; Kahneman, 1973; Shiffrin & Schneider, 1977). Because attentional
capacity is limited, resources directed toward the processing of distractors reduce
available resources for the processing of task-relevant information. This idea is supported
by studies which have shown that distraction effects increase for complex rather than
simple tasks and are greater as the similarity of distractors to relevant cues increases
(Graydon & Eysenck, 1989).
Though empirical evidence is scarce, many researchers have suggested that
increases in emotionality (i.e., anxiety, worry, arousal) increase susceptibility to
distraction. Numerous examples of evidence to support the notion that stress impedes
performance due to distraction can be found in verbal accounts and behavioral
observations of "choking" in competitive environments. Moran (1994, 1996) provides
substantial anecdotal evidence that the impact of anxiety is the absorption of attentional
resources which could otherwise be directed toward the relevant task. Similarly,

15
Baumeister and Showers (1986) suggest that increased worry causes attentional resources
to be devoted to task-irrelevant cues. Furthermore, self-awareness theorists such as
Masters (1992) suggest that under stress, not only is attention absorbed by irrelevant
stimuli, but also the performance of normally automated skills becomes less automated as
resources begin to be intentionally directed toward the process of the once-automated
movement. Finally, Eysenck (1992) has provided empirical evidence that anxiety provokes
people to detect stimuli which they fear, usually stimuli that diverts them from attending to
relevant information. Unfortunately, the specific components of stress that influence
attentional parameters have also been largely ignored.
Arousal and Anxiety
Due to increasing dissatisfaction with the Inverted-U hypothesis and other
theories, researchers attempted to analyze the stress response in greater detail as to its
various components and to re-examine the stress/performance relationship. Perhaps the
first scholars to approach the possibility of dissecting the general anxiety response were
Liebert and Morris (1967) who identified two primary contributing factors to anxiety:
worry and emotionality. In Liebert and Morris's view, worry consisted of cognitive
concerns about one’s performance while emotionality referred to the autonomic reactions
to the performance environment. This concept strongly influenced Davidson and
Schwartz's (1976) multidimensional model of anxiety. They were the first to use the
terms "cognitive" and "somatic" anxiety and formulated their theory in the context of
clinical applications. Thus, worry has become synonymous with cognitive anxiety and
emotionality has become synonymous with somatic anxiety. These general characteristics

16
of the components of anxiety have held up under empirical investigation and appear to be
manipulable independently (e.g., Schwartz, Davidson, & Goleman, 1978). Also, it is
important to distinguish both components of anxiety from arousal. Though similar to
somatic anxiety, arousal refers to the natural physiological indices of activation that are
present within an organism at any time (Sage, 1984). In contrast, somatic anxiety refers
to the perception of physiological arousal.
One problem with multidimensional anxiety theory is the two-dimensional
approach used to explain the effects of somatic and cognitive anxiety on performance.
Specifically, the two-dimensional approach in analyzing results tends to neglect the
interaction of the components of stress, treating them independently rather than in
combination (Hardy & Fazey, 1987). According to the viewpoint of Hardy and his
colleagues, any relatively comprehensive treatment of these components must treat them
in an interacting, three dimensional manner. To improve the predictability and structure of
the model, therefore, Hardy and Fazey (1987) developed a catastrophe model of anxiety
and performance.
In an effort to advance understanding beyond the multidimensional approach to the
study of the effects of anxiety and arousal on performance, Fazey and Hardy (1988)
proposed a three-dimensional model of the relationship. Borrowing heavily from Thom
(1975) and Zeeman (1976) who originally conceptualized the idea of catastrophes and
then applied them to the behavioral sciences, respectively, Fazey and Hardy's (1988)
model is closest in form to the cusp catastrophe, one of the seven originally proposed

17
catastrophe models of Thom (1975). According to the cusp catastrophe model, changes in
either cognitive anxiety or arousal, or both will change performance in specific ways.
Hardy and Fazey (1987) state that of the two variables that determine behavior
(cognitive anxiety and arousal), cognitive anxiety is the "splitting factor", the variable that
has the primary influence on performance level. The roles of cognitive anxiety and
physiological arousal were chosen specifically to be able to evaluate testable hypotheses
with respect to the anxiety/arousal/performance relationship. Specifically, when cognitive
anxiety is low, the model predicts that physiological arousal will influence performance in
an inverted-U fashion. However, when physiological arousal is high, high levels of
cognitive anxiety will result in lower levels of performance. Finally, when physiological
arousal is low, higher cognitive anxiety will lead to increases in performance.
Usually the manipulation of anxiety and arousal is carried out through a time-to-
event paradigm in which assessments are taken at specified times leading up to a
competition setting (Hardy, Parfitt, & Pates, 1994). For instance, assessments will be
taken one week prior, two days prior, and then one hour prior to the competition. In this
way, the time course of anxiety and arousal can be assessed. In other instances, levels of
anxiety and arousal are manipulated through the use of both ego-threatening or other
anxiety-producing instructional sets and through the use of exercise-induced arousal,
respectively (Parfitt, Hardy, & Pates, 1995).
An obvious feature of the cusp catastrophe model of the anxiety/performance
relationship is the choice of physiological arousal rather than somatic anxiety as the normal
factor. The primary reason for this choice is based on the notion that it is part of the

18
organism's natural physiological response to anxiety-producing situations (Hardy, 1996).
This belief is sufficiently well-established to be spoken of in the context of a generalized
response within the competition setting. In other words, in competitive environments,
performers usually show one or more signs of physiological arousal. Though the
physiological response may be reflected in self-reports of somatic anxiety, the purely
physiological index can encompass the individual task requirements, different situations,
and other combinations of factors that override reports of somatic anxiety. Furthermore,
physiological arousal changes tend to be reflected in changes of somatic anxiety while the
converse is not the case (Fazey & Hardy, 1988; Hardy, 1996; Hardy & Fazey, 1987).
Substantial support has been shown for the cusp catastrophe model of the anxiety
performance relationship in seminal investigations of the model by Hardy and his
colleagues (e.g., Hardy, Parfitt, & Pates, 1994).
One limitation, however, to the study of stress and performance in the context of
any of the models described previously, is a lack of empirical explanation for the
performance changes that are noticed in overly stressful situations. As mentioned, one
specific cognitive mechanism that has been implicated, but has received limited empirical
investigation in sport contexts, is the impact of anxiety and arousal on attentional
resources. Thus, a logical next step is to attempt to delineate these relationships in an
effort to more thoroughly understand performance changes under stressful conditions.
A More Comprehensive Next Step
Though intriguing and receiving much anecdotal support in a variety of settings,
the empirical interaction between the cognitive and emotional antecedents of the

19
stress/performance relationship remains largely unspecified. Furthermore, in light of
recent dissatisfaction with the Inverted-U hypothesis of the anxiety/arousal/performance
relationship, the underlying explanations originally forwarded by Easterbrook (1959) may
be somewhat obsolete. Specifically, although studies in which anxiety or arousal have
been manipulated have shown support for the attentional narrowing phenomenon, none
have examined the interactive effects of these emotional antecedents, nor have they
designated one or the other as the primary contributor to the relationship. Furthermore,
the role of distraction has received little or no investigation in this context, and an
understanding of it could contribute greatly to the understanding of performance changes.
Paradoxically, it appears that perhaps there are two equally attractive explanations
for the decrease in performance that occurs under high levels of stress. On one hand,
proponents of the attentional narrowing argument would suggest that under high stress
levels (either anxiety or arousal induced) the attentional field narrows to block out
irrelevant cues, and then narrows further, blocking the processing of relevant information
as stress continues to increase. On the other hand, proponents of the distraction argument
would suggest that actually a widening of the attentional field occurs such that irrelevant
or distracting cues receive more attention than when under lower stress levels. Evidently,
a controversy exists unless in some way, both mechanisms could be working at the same
time. Perhaps, an increase in anxiety and/or arousal results in a narrowing of the
attentional field while at the same time, especially at higher levels of stress, it increases
susceptibility to distraction. Many theories can account for how stress affects attention

20
and the eventual impact of attentional variation on performance, but none address
specifically why this phenomenon occurs.
As may be evident from the discussion of driving tasks, visual search has been used
extensively to draw cognitive inferences regarding what information is being extracted and
processed during eye fixations, a concept Viviani (1990) has termed the "central dogma"
of visual search research. Though it is presently impossible to empirically prove the
central dogma, most researchers agree that eye fixations do at least reflect cognitive
processing. Assuming the dogma to be even partially true, if an attenuation of cues in the
periphery is evident, the need to pick up crucial cues in the periphery during particular
situations would necessitate an increase in scan path variability and fixation rate in order to
compensate for peripheral narrowing. Furthermore, if distracting visual cues were actually
introduced into the test environment, visual search strategies may be altered, resulting in
increased fixation and processing of distracting stimuli and a reduction of attentional
resources available for central task performance.
Like normal driving, the sport of auto racing demands the coordination of an
extensive repertoire of perceptual and motor skills. However, the performance difficulty
of these skills is significantly compounded by the competitive nature of the sport. In
addition to mastering typical driving skills, the sheer speed of the car requires split-second
decision-making and intense concentration on the most relevant cues. An ill-advised
momentary attentional shift or distraction can be (and often is) catastrophic under these
circumstances. Thus the need to respond effectively in this type of a pressure-packed

21
activity is paramount. Unfortunately, no attempt has been made to empirically assess
these factors in auto racing.
Viviani (1990) suggested that the central dogma of visual search and cognitive
inference would be valid if evidence for serial search is provided in particular tasks.
According to Kahneman (1973), as arousal increases, task difficulty also increases. Under
these circumstances, parallel (relatively automatic) processes tend to be modified by the
organism, becoming more serial and attentive in nature (Duncan & Humphreys, 1989;
Shiflfin & Schneider, 1977). As mentioned, the auto-racing environment is one is one in
which drivers experience extremely high levels of arousal and anxiety. In this case, the
ability to relate eye fixations to cognitive information processing is more valid than when
parallel processing is dominant.
As mentioned, very limited research has been done to investigate any psychological
phenomena with auto racing and none has been done to investigating driver's eye
movements or other attentional parameters that are critical to high performance in the
fastest sport in the world. The selective and divided attention demands of race car driving
render it an ideal task and environment to investigate attentional mechanisms and the eye-
movement parameters that underlie those mechanisms. Perhaps the first step that should
be taken to better understand the attentional capabilities necessary for effective race car
operation is to evaluate the visual search patterns of drivers as they navigate the race
course. By evaluating these parameters, it may be possible to assess whether the
"software" advantages that appear to predispose athletes in other sports to reach higher
levels of achievement are valid antecedents to high performance auto racing.

22
In light of these considerations, the primary objective of this study was to attempt
to delineate the individual and interactive influence of arousal and cognitive anxiety on
attentional capabilities. In addition, it was anticipated that these attentional alterations
would result in behavioral changes that would, in turn, influence global performance
indicators. Specifically, performance while undertaking (1) a central driving task and (2) a
peripheral light identification task was investigated under various levels of cognitive
anxiety. Furthermore, visual search patterns were assessed to ascertain whether
perceptual factors (i.e., the search patterns themselves) contributed to the attentional
narrowing and/or distractibility phenomena.
In this manner, an attempt was made to isolate specific factors that might influence
selective attention and the ability to divide attention between the central and peripheral
tasks. Also, an attempt was made to determine whether visual search patterns were
influenced by changes in both cognitive and physiological activation levels. By assessing
specific dependent measures rather than simply global changes in affect, cognition, and
performance, a clearer understanding of the interactive influence of these factors was
acquired.
Statement of the Problem
In this experiment, a central driving task and a peripheral light detection task were
used to assess the effects of anxiety (as manipulated by a time-to-event paradigm and
anxiety-producing instructional sets) on performance over the course of familiarization,
practice, and competition sessions. Performance-related variables included: (a) driving
speed and accident propensity, (b) peripheral light detection speed and accuracy, (c) visual

23
search patterns, and (d) physiological arousal. Determined was whether any anxiety-
induced changes in performance were due to a narrowing of the attentional field, increased
distractibility, or both.
Hypotheses and Pilot Study Results
The following hypotheses were tested in this investigation. The first set of
hypotheses was directed toward the manipulation of anxiety and the expected result of this
manipulation on arousal levels. Rationale for the hypotheses is offered after all are
proposed.
1. The use of the time-to-event paradigm and instructional sets will produce
higher cognitive anxiety levels during the practice and competition sessions in the
experimental groups (anxiety) than in the control groups (no anxiety) as measured by the
CSAI-2 (Martens et al., 1990). The instructional sets used will be similar to those
employed by Hardy et al. (1994) and will be used to manipulate levels of cognitive anxiety
independent of somatic anxiety. These manipulations have been shown to be valid in both
sport-specific (Hardy et al., 1994) and other evaluative situations (e.g., Morris, Harris, &
Rovins, 1981). Furthermore, the time-to-event paradigm has been a reliable means of
investigating temporal changes in anxiety associated with impending competitions (Hardy
et al., 1994).
2. The increase in anxiety levels exhibited in the experimental groups will be
mirrored by an increase in physiological arousal (as measured by an increase in heart rate
and pupil diameter size) in the practice and competition sessions. In addition, it is

24
hypothesized that cognitive anxiety and arousal levels will be highest immediately prior to
the competition session due to the time-to-event and instructional set manipulations.
According to Lacey and Lacey’s (1958) autonomic response stereotype
hypothesis, the reaction to anxiety-producing thoughts and stimuli cannot be specified due
to individual differences. However, if manifested in physiological changes, heart rate and
pupil dilation measures are sensitive to increases in autonomic activity. In addition, heart
rate has been used reliably in other tests of the catastrophe model of anxiety (e.g., Hardy
et al., 1994). Furthermore, Abemethy (1993) has advocated the use of pupillometry as
one of the most reliable measures of anxiety. Finally, because the test environment is
static, such that the participant is not physically activated in any way, any changes in HR
or pupil dilation across test conditions can be more readily attributable to emotional
changes than if tested in a physically active situation.
The next set of hypotheses was directed toward the anticipated changes in
performance that were expected to occur in the central and peripheral tasks.
1. For central task conditions (those in which only the central driving task is
performed), driving performance (as measured by lap speed and the number of driving
errors) will be similar for the control group and anxiety group in the familiarization
session. However, during the second session, driving is hypothesized to be more
proficient for the anxiety group than the control group. Finally, performance in the
competition session will be better for those in the control group than those in the anxiety
group.

25
2. Those in the relevant groups, in which the central driving task will be
performed concurrently with peripheral light identification of relevant stimuli, will exhibit
similar proficiency on both tasks regardless of control or anxiety manipulations during the
familiarization session. Driving skill during Session 2 (practice) is predicted to be
facilitated for those in the anxiety group as opposed to the control group, but performance
in the peripheral light detection task (as measured by reaction time and response accuracy)
will be diminished due to a decrease in peripheral cue utilization. In the third test session
(competition), those in the anxiety group will perform worse in both tasks due to a
decrease in cue utilization.
3. For the dual task distraction conditions (those in which the central driving task
will be completed concurrently with peripheral light detection of relevant stimuli while
ignoring irrelevant peripheral lights), achievement in both tasks during the familiarization
session will be similar for the anxiety and control groups. Central driving task proficiency
during the second session will be facilitated for those in anxiety groups as opposed to
control groups, but peripheral cue utilization changes will result in reduced performance
on the peripheral light detection task during the same session for the anxiety group. In the
third session, execution of both tasks will be worse for those in the anxiety condition as
compared to control groups due to an increase in the narrowing of cue utilization as well
as an increase in the distractibility of participants at high levels of anxiety.
4. Overall, achievement in the central driving task should be highest for the
central control group in the third test session due to no interference from anxiety or other
attention-demanding stimuli (i.e. peripheral lights). The ability to detect peripheral lights

26
should be best for the relevant control group in the competition session due to the
increased automation of the central task, no interference from distractors, and no
interference from anxiety changes. Furthermore, reaction time and detection accuracy for
relevant peripheral lights in the distraction condition is expected to be similar in the
familiarization session for anxiety and control groups. However, detection speed and
accuracy will decrease for those in the anxiety group in the competition session due to an
increase in distractibility.
These hypotheses were forwarded on the basis of previous conclusions from
studies of the attentional narrowing phenomenon (e.g., Bruner, Matter, & Papanek, 1955;
Callaway & Dembo, 1958; Callaway & Thompson, 1953; Eysenck, Granger, &
Brengelman, 1957; Granger, 1953), as well as a variety of anxiety models that indicate a
moderate increase in activation to be beneficial to performance but a high level of
activation to result in diminished achievement (e.g., Hardy & Fazey, 1987; Yerkes-
Dodson, 1908).
According to the attentional narrowing phenomenon, under moderate levels of
anxiety and arousal, the range of cues utilized will be decreased, blocking peripheral cues
from being processed. Thus, central driving task proficiency will be facilitated by
maintaining attentional focus on the most relevant cues while performance on the
peripheral light detection task will be hindered (Easterbrook, 1959; Kahneman, 1973).
However, as activation levels increase, a person is most likely susceptible to a further
decrease in the range of cue utilization, blocking the processing of relevant cues
(Easterbrook, 1959). Also, remaining attentional resources may be absorbed by the

27
increased propensity to be distracted by both internal factors (anxiety) and an increased
propensity to process irrelevant external factors (distracting peripheral stimuli) (Moran,
1996; Wegner, 1994).
If activation levels reach extremes, this could eventually result in a catastrophic
deterioration in effective execution (Hardy, 1996) of both central and peripheral tasks.
Specifically, Hardy and Fazey’s (1987) catastrophe model indicates that when a
performer’s cognitively anxiety and arousal reach high levels, performance will
deteriorate in a dramatic fashion, not in a gradual manner as proposed by the Inverted-U
hypothesis (Yerkes-Dodson, 1908).
The final set of hypotheses was directed toward the expected changes in visual
search patterns that were expected to be exhibited by participants in response to changes
in anxiety and arousal levels. Once again, at the completion of the proposed hypotheses,
rationale will be presented.
1. Eye fixations for those in the central condition are expected to cluster closely
around the point of expansion (within a 6° radius from the point of expansion) for both the
control and anxiety groups.
2. In the relevant condition, fixations for the control groups should be focused
more centrally (similar to the central condition) than for the anxiety group due to the
ability of control participants to acquire peripheral stimuli information with peripheral
vision. Correspondingly, those in the anxiety group will probably exhibit an increase in
the number of fixations to the periphery in order to compensate for the reduction of
peripheral vision due to anxiety.

28
3. In the distraction condition, similar to the relevant condition, fixations for the
control groups are expected to remain more centrally located in Sessions 2 and 3 due to
the ability to discriminate relevant from irrelevant peripheral light stimuli with peripheral
vision. However, the number of fixations to the periphery for those in the anxiety group
will increase in Session 2 and then even more in Session 3 due to a narrowing of cue
utilization and an inability to acquire peripheral information with peripheral vision, as well
as the increased susceptibility to focus on distracting stimuli.
These hypotheses are based on findings from general studies of driver fixation
tendencies as well as the previously mentioned hypotheses with respect to attentional
narrowing and distraction. It has been repeatedly shown that 80-90% of drivers’ fixations
tend to cluster within 4-6° of the point of expansion in the visual display and that this
tendency is enhanced under conditions of higher task complexity (Miura, 1985, 1990).
These tendencies would be expected to hold for those in control groups that do not
experience extremely high levels of anxiety and are not required to process peripheral
input. However, under anxiety-producing conditions, the visual field is expected to
narrow (Easterbrook, 1959), requiring an increased number of fixations to the periphery
to acquire information that is normally acquired by peripheral vision.
Furthermore, it would appear that highly anxious and aroused participants will
increase the number of fixations to distracting stimuli. Miura (1986) has suggested that as
driving demands increase, a stronger tendency to search for information in the periphery
occurs. Accordingly, this is a possible adaptation of attentional processing to deal with
the increase in demands (Miura, 1987). In terms of distraction, resources (i.e., eye

29
fixations) directed toward the processing of distractors reduce available resources for the
processing of task-relevant information. Graydon and Eysenck (1989) have shown that
distraction effects increase for complex rather than simple tasks and are greater as the
similarity of distractors to relevant cues increases. As the ability to distinguish relevant
from irrelevant cues is diminished, the propensity to be distracted by irrelevant stimuli will
likely increase along with the tendency to fixate on these stimuli.
Definitions of Terms
To standardize the terminology in this experiment, the following terms are defined:
Arousal is the process in the central nervous system that increases the activity in
the brain from a lower level to a higher level, and maintains that higher level. The
activation response is a general energy mobilizing response that provides the conditions
for high performance, both physically and psychologically (Ursin, 1978).
Attention is . .the taking possession by the mind, in clear and vivid form, of one
out of what seem several simultaneously possible objects or trains of thought.
Focalization , concentration, of consciousness are of its essence. It implies withdrawal
from some things in order to deal effectively with others” (James, 1890, pp. 403-404).
Also, it has been described as a concentration of mental activity (Matlin, 1994; Moran,
1996).
Attentional narrowing refers to the phenomenon in which, under increasing levels
of stress, the range of cues utilized by an organism is reduced, resulting in an initial
filtering of irrelevant or peripheral cues from processing, an increase in performance of
central tasks, and a decrease in performance of peripheral tasks. As stress levels continue

30
to increase, both task- irrelevant as well as relevant cues begin to be attenuated from
processing until performance in both peripheral as well as central tasks are disrupted
(Easterbrook, 1959).
Cognitive anxiety is characterized by worry or the awareness of unpleasant
feelings, concerns about performance, and the inability to concentrate (Rotella & Lemer,
1993).
Cusp catastrophe model is a three-dimensional model that describes how one
dependent variable can demonstrate both continuous and discontinuous changes in two
other dependent variables. In the context of the catastrophe model of anxiety, the
dependent variable is performance and the two independent variables are anxiety and
arousal (Hardy, 1996).
Distraction refers to situations, events, thoughts, or circumstances that divert the
mind from some intended train of thought and tend to disrupt performance (Brown, 1993;
James, 1890; Moran, 1996).
Divided attention is characterized by the ability to attend to several simultaneously
active messages or tasks, or to distribute attention effectively to simultaneous tasks that
develops as a result of experience and practice (Eysenck & Keane, 1995; Hawkins &
Presson, 1986).
Fixation refers to a pause in search during which the eye remains stationary for a
period equal to or in excess of three video frames (120 ms) (Williams, Davids, Burwitz, &
Williams, 1994).

31
Fixation location refers to the areas in the display in which the eye fixates during
completion of a task (Williams, Davids, Burwitz, & Williams, 1994).
Point of expansion (POE) is the area where the two edge lines of the road appear
to converge and the point at which the road appears to expand outward from the center
(Rockwell, 1972).
Reaction time (RT) refers to the elapsed time between presentation of a particular
stimulus and the initiation of a response to that stimulus (Schmidt, 1988).
Saccadic eve movements refer to movements of the eyes from one fixation point to
another. A common saccade lasts for approximately 1/50°* to l/lO411 of a second
depending on how far it is to the next fixation (Andreassi, 1989).
Search Rate refers to a combination score representing the number of fixations
and the duration of each fixation at particular locations (Williams, Davids, Burwitz, &
Williams, 1994).
Selective attention refers to “the process of selecting part of simultaneous sources
of information by enhancing aspects of some stimuli and suppressing information from
others” (Theeuwes, 1994, p. 94).
Somatic anxiety refers to perceptions of physiological arousal such as shakiness,
sweating, increased heart rate, rapid respiration, and “butterflies in the stomach” (Martens
et al., 1990).
Stress is characterized by a combination of stimuli or a situation that is perceived
as threatening and which causes anxiety and/or arousal (Hackfort & Schwenkmezger,
1993).

32
Useful field of view (UFO V) refers to the information gathering area of the visual
display (Mackworth, 1976).
Visual search refers to the two-stage process in which visual information from
sensory receptors is held in a rapidly decaying visual sensory store and then selected items
in the iconic store are subjected to a more detailed analysis (Jonides, 1981; Theeuwes,
1994).
Assumptions
For the purposes of this investigation, the following assumptions were made:
1. Participants received course credit for participation and therefore should have been
equally motivated to participate in the study.
2. The time-to-event paradigm and specific instructional sets which include possible ego
threats, monetary gain, and other incentives, were appropriate methods to manipulate
cognitive anxiety (Hardy, Parfitt, & Pates, 1994).
3. The CSAI-2 (Martens et al., 1990) was an appropriate measure of cognitive anxiety.
4. Heart rate and pupil diameter measures were accurate and appropriate indices of
arousal (Abemethy, 1993; Hardy, 1996).
5. The dependent measures used to assess central driving task performance (lap speed
and number of errors) and the peripheral tasks (RT and number of errors) were
appropriate measures of performance.
6. The central dogma that the line of sight will coincide with the direction of attention
(Viviani, 1990) was at least partially true in this case, and therefore, visual search
orientation was reflective of the participant's actual allocation of attention.

33
Significance of the Study
Most empirical research dealing with the interactive effects of arousal and/or
anxiety with performance has been oriented in a in a very general fashion. This is
exemplified by the global measures of both stress and performance that have been used
(Jones, 1990). Therefore, very little is known regarding the specific components of the
stress response (either cognitive anxiety, arousal, or both) that influence performance
variables such as attentional flexibility, speed of information processing, decision-making,
and other cognitive factors. With this in mind, the primary intention of the study was to
contribute to and expand upon the established bodies of knowledge regarding the ability of
participants in competitive sports and other stress-inducing activities to attend to and
process the most relevant cues and make decisions appropriately. Though a driving task
was used in the study, the implications of this research are intended to be generalizable, to
a certain extent, to other achievement situations in which the stress response occurs. The
driving simulation provided an ecologically valid, natural dual task paradigm in which to
ideally investigate the phenomena of interest due to the need to attend to and process cues
from both central and peripheral locations while driving.
The investigation addressed five issues of theoretical importance. First, a greater
understanding was provided of the decrement in performance that has been repeatedly
shown while completing tasks under high levels of stress. Though the attentional
narrowing phenomenon has received much empirical support as the underlying reason for
a diminished ability to execute various tasks, other factors were suggested as possible
contributors to these debilitative effects. Specifically, proposed was that the influence of

34
distractors, and the tendency to be distracted when faced with increased activation levels
may also contribute to performance decreases, but had not been addressed. Wegner
(1994) and others have presented the notion that as stress levels increase, the propensity
of the performer to be distracted is enhanced. Though empirical evidence does not exist
to support this notion, anecdotal self-report from athletes and athletes and other
performers warranted investigation into this area (Moran, 1996). No research done to
date in the context of peripheral narrowing had been conducted in which distractors were
presented to participants while performing central and peripheral tasks.
Another issue of interest was whether the performance changes that were
anticipated to occur under elevated levels of activation were due to changes in
psychological affect (e.g., cognitive anxiety), an increase in arousal level, or some
combination of both. By examining these variables in the context of the cusp catastrophe
model (Hardy & Fazey, 1987), a clearer understanding of them and their affect on
attentional processing was delineated.
Third, determined to a certain extent was whether performance changes under
higher levels of activation were due to the perceptual alterations in visual selective
attention (as indicated by changes in visual search patterns) or other non-perceptual
factors (i.e., encoding, response selection) during the information processing of relevant
and irrelevant stimuli. As mentioned, one of the areas of controversy regarding the
peripheral narrowing phenomenon was with respect to the mechanisms responsible for the
lack of effective cue utilization. Indirect support has been provided for both a diminished
ability to perceive relevant cues as well as a decrease in the efficiency of later stages of

35
information processing. Before this study was undertaken, no researchers had used eye
movement information to clarify these issues. However, shifts in visual attention from
central areas of a display to the periphery, and vice-versa, were reflected in the visual
search data obtained in this experiment. Furthermore, information gathered from the use
of visual search monitoring equipment was used to shed some light on the question of
distraction versus narrowing by indicating whether eye-movement patterns were altered to
focus more on distractors while under high levels of stress. A fourth area of significance
addressed in this experiment was the effect of elevated activation levels on specific
performance variables. In particular, by evaluating performance in terms of a variety of
accuracy, speed, and reaction time measures, a more complete understanding of the
separate elements of proficiency that are impaired or facilitated was ascertained. As Jones
and Hardy (1990) have suggested, the lack off attention to these specific performance
variables rendered it difficult, if not impossible, to prescribe interventions to enhance them.
Finally, an attempt was made to surmise whether skill execution was affected in a
gradual or more dramatic fashion at higher levels of activation. Although the view of an
inverted-U relationship between activation levels and performance is still the most popular
conception of the relationship, this investigation provided evidence that perhaps more
recent models (such as the cusp catastrophe model) are more accurate in their predictions.
From a more applied point of view, the results of this investigation are expected to
benefit both drivers and sports performers. Though merely a simulation, the findings from
this investigation give an indication of the manner in which excessive driving demands
(such as heavy traffic, being “cut off”, or near accidents) which increase the level of

36
activation of drivers will affect their attentional abilities. Furthermore, the impact of
attentional abilities on the central task of driving the car (accelerating, braking, and
steering) as well as the ability to detect and effectively process peripheral information were
elaborated.
It is anticipated that many of the results obtained from this study will be
generalizable to other dynamic and reactive sport activities that involve the coordination
and flexibility of attentional processing between central and peripheral sources of
information. By developing a clearer understanding of information processing abilities in
these types of environments, it may be possible to derive training simulations to help
athletes to maintain focus on the most relevant cues in the performance situation. For
instance, Singer, Cauraugh, Chen, Steinberg, Frehlich, and Wang (1994) have shown that
it is possible to train attentional parameters to be more in line with expert strategies used
in reactive tennis situations. Perhaps this will be possible in tasks in which an anxiety-
producing situation is present, such as the high speed driving context of interest in this
study.

CHAPTER 2
REVIEW OF LITERATURE
When considering the ability to attend to, process, and react to specific cues in
dynamic, highly reactive sport situations in the most efficient and correct manner, issues
arise concerning the various attentional and information processing components that either
facilitate or impede performance. Specific questions include: How do performers know
which cues to attend to? What are the properties of particular cues that make them salient
and informative to the participant? What information is extracted from cues as they are
attended? What are the separate influences of arousal and anxiety levels on the ability to
perform effectively by selecting and processing the most relevant cues at the right time?
Do eye movements and other behaviors associated with visual attentional processing
change under stressful situations? If so, do changes in attentional shifts and eye
movements reflect detrimental or facilitative effects of performance? Are these effects
due to a narrowing of the visual field and/or to changes in the ability to mediate the
distracting properties of irrelevant stimuli? These are questions that have received little
attention in the context of sport and other performance areas and will therefore be
investigated in this project.
The influence of an organism’s general level of activation is integral to the ability
to respond to particular stimuli in an effective and timely manner. The level of activation
37

38
is usually described in terms of the performer’s state of arousal, which has been defined by
Abemethy (1993) as “a physiological state that reflects the energy level or degree of
activation of the performer at any particular instant” (p. 129). Since the publication of the
Yerkes-Dodson (1908) Inverted-U theory, much research has been devoted to
understanding the influence of arousal states on the ability to attend to, discriminate, and
process information in tasks ranging from simple laboratory reaction time tasks to more
applied areas in sport, the military, and industry. Research in which the effects of stress
on performance have been investigated have ranged along a continuum from assumed low
levels of arousal in vigilance tasks to very high levels of arousal in quickly changing,
interactive, dynamic environments or situations in which the perception of threat has been
induced.
A concept that received a great deal of attention during the early 1950’s was the
narrowing of the attentional field as arousal and/or anxiety increased, culminating in the
publication of Easterbrook’s (1959) article describing the phenomenon. The peripheral
narrowing idea has been used extensively to explain changes in performance in a variety of
laboratory tasks and has been generalized to other real-world applications. However, in
the sport domain, empirical investigation of the peripheral narrowing phenomenon has
been sparse. Furthermore, other factors such as the influence of distraction on decision
making and information processing capabilities of athletes have been virtually ignored by
sport psychology researchers. Similarly, no research has been directed toward assessing
these various attentional parameters in the sport of auto racing. However, due to its
reliance on speedy decision making and attentional shifts under extreme time constraints

39
and life-threatening circumstances, auto racing provides the ideal environment in which to
assess these factors. Inferences to such situations in other contexts and with other tasks
can be made, which is the intent in the present study.
Accordingly, the focus of the following literature review is to critically evaluate the
literature that led up to and continued beyond the publication of Easterbrook’s (1959)
influential work. Also, the separate components of stress will be compared and
contrasted, and the interactive influence of these components on attention will be
summarized. Furthermore, a justification for examining attentional processing in stressful
environments with respect to eye movement parameters will be provided. Finally, an
empirical framework will be proposed to evaluate the influence of physiological and
cognitive stress on attentional capabilities in a simulated race car driving task.
Stress and Human Performance
Anxiety, arousal, fear, and a variety of other terms that fall under the guise of
stress have been studied extensively in terms of their influence on performance, individual
responses to stressors, and methods of regulating the stress levels of sport performers.
The very nature of sport, with its increasing public exposure, the pressures placed on
athletes to win from coaches, other athletes, and themselves, the rewards for great
performance, and the disappointment from losing, is full of stressful performance
situations (Murphy, 1995). Athletes who are able to regulate the stress response and
perform in competitive situations in spite of the surrounding pressures inherent in sport are
those who will inevitably excel.

40
However, though the general topic of stress in sport has received much attention
from sport psychology researchers, confusion has been proliferated by the fact that many
researchers and practitioners use terms such as activation, stress, anxiety, and arousal
interchangeably, treating a multidimensional construct in unidimensional ways.
Accordingly, before addressing the specific issue of attentional narrowing as a result of
stressful circumstances, a discussion of the similarities and differences of these terms must
be addressed . Also, a discussion of popular theories developed to describe how
performers deal with stress and the theoretical basis for the present investigation will be
provided in light of the recently proposed cusp catastrophe model of anxiety and arousal
(Fazey & Hardy, 1987).
Stress
Stress is defined as a combination of stimuli or a situation that is perceived as
threatening and which causes anxiety (Hackfort & Schwenkmezger, 1993)). Various
stressors include external threats, deprivation of primary needs, and performance pressures
that can be characterized as both general and sport specific. Selye (1956) described stress
based on the principle of equilibrium in which self-regulation is of primary importance. He
differentiated stress (a condition to which we are always prone) from the inability to cope
with the stress.
A popular cognitive view of anxiety that was heavily influenced by Selye’s ideas
was forwarded by Lazarus and his colleagues (e.g., Lazarus, 1966; Lazarus & Averill,
1972). Basically, Lazarus viewed anxiety as an emotion with a specific pattern of arousal
that corresponds to it and that is influenced by the cognitive appraisal and perception of an

41
anxiety-producing event. According to this view, all facets of a situation tend to be
classified with respect to its significance and the implications of that situation on the
person’s well-being. Therefore, it is the perception of the event and not the event itself
that dictates emotions. Researchers have discovered that, contrary to the medical model
of stress, many people view stress and anxiety as challenging, exciting, and beneficial
(Lazarus & Folkman, 1984).
These findings prompted the formulation of Kobasa’s (1989) Hardy Personality
Theory which states that people who are psychologically hardy tend to view stressful
situations in a positive way. The specific characteristics of psychologically hardy people
are that they (1) are committed to the activity, (2) believe they can control or influence
events, and (3) view demands or changes as exciting challenges. Similarly, Smith’s (1980)
mediational model suggests that the appraisal process creates the psychological reality
based on what the individual tells himself or herself about the situation and the ability to
cope with it.
Meichenbaum (1985) also suggests that the cognitive appraisal of the individual is
what dictates the nature of the interaction with the environment. The meaning the person
construes to the event is what shapes the emotional and behavioral response. Similarly,
Mahoney and Meyers (1989) postulate that it is not stress that is central to performance
but the athlete’s expectations, efficacy beliefs, and use of arousal that will determine
performance. Therefore, arousal, if perceived as natural is positive but negative anxiety
(i.e., worry) is negative. Being aroused does not mean that one will become anxious.
Rather, anxiety occurs due to (1) distrust of natural responses, (2) ineffective perceptions

42
due to previous exposure to modeling of arousal, (3) directly being taught that arousal is
bad, and (4) early failure experience while aroused. Support for the notion that it is the
perception of the stressful situation that dictates performance is provided by findings that
athletes enjoy the “nervousness” associated with competition. Rotella, Lemer, Allyson,
and Bean (1990) have shown that precompetitive feelings of high activation are helpful to
performance if they are perceived to be natural and provide a sense of readiness rather
than concern.
Unfortunately, all athletes, even those perceived as being the best in stressful
situations, occasionally “choke” under pressure. Thus, the question remains: How do
external and internal stressors manifest themselves in the stress response and how does the
stress response affect performance? The rest of the review will be directed toward
describing situations in which the performer fails to regulate the stress response
appropriately. A justification for continued research in this area will be provided. From a
cognitive perspective, then, questions arise concerning how the stress response influences
the ability of performers to process information and allocate processing resources to
coping with stressful stimuli as well as dealing with task demands and constraints.
Theories of the Stress Response
Controlling the stress response is critical to the ability to perform well. Whether or
not cognitive appraisal reflects reality is not necessarily important in terms of the stress
response for the simple reason that it only occurs in situations in which self-regulatory
skills fail (Carver & Scheier, 1981; Cherry, 1978; Jones, 1990; Lazarus, 1966).

43
The analysis of stress has its roots in the psychoanalytic conceptualization of the
construct. Specifically, Freud (1952) postulated that affect and neurosis are closely
related to each other, with affect being related to exogenous arousal and neurosis being
related to endogenous arousal. Though not the most popular view of stress today, this
does provide a foundation for much of the work done in the psychoanalytic realm and
provides the impetus for later cognitive and behavioral approaches to the study of stress.
Mowrer’s (1960) learning theory approached stress from a behavioral learning
viewpoint involving both classical conditioning and instrumental reinforcement. He
suggested that in environments where specific stimuli result in stressful outcomes, the
organism would eventually learn to associate the stimulus with the stressful outcome. For
instance, if an athlete consistently performs poorly in a specific competition setting,
eventually, the simple thought of that setting will elicit an anxious response.
With the cognitive revolution in the late 50’s and early 60’s, stress (in particular,
anxiety) was viewed as an emotion that is triggered by a person’s “communicative
relationship” with the environment and arose from expectations and appraisals of these
situations (Festinger, 1954). Festinger suggested that anxiety control is based on
decisions that lead to either direct actions to remove the anxiety-producing stimulus or to
avoid it (the approach/avoidance distinction). Three assumptions that formed the basis of
Festinger’s theory were that: (1) a person who cannot account for arousal will look for
something to attribute it to, (2) previous explanations do not cause a need for appraisal,
and (3) a person with arousing thoughts but no physiological arousal will not show
emotional response and therefore will not be stressed. According to this view, an athlete

44
that experiences physiological arousal will only choose to exert cognitive processes for
interpretation of it if the arousal persists, and is unaccounted for (Hackfort &
Schwenkmezger, 1993).
As will be described in depth later, the specific reactions to stress are individually
determined. Stress can be manifested in the form of cognitive and somatic anxiety,
physiological arousal, loss of self-confidence, panic, and a variety of other forms.
Obviously, each of these different responses will have an influence on performance if not
regulated appropriately.
The Stress/Performance Relationship
One of the more popular early conceptualizations of the stress/performance
relationship was the Hull/Spence Drive Theory (Hull, 1952; Spence & Spence, 1966).
According to the theory, level of activation is considered a function of the sum of all of the
energetic components affecting an individual at the time of a particular behavior.
Furthermore, drive strength is dependent on the emotional reaction that is caused by an
aversive stimulus. Thus, people with increased drive levels perform better due to their
greater effort, emotion, and motivational need to remove the aversive stimulus. Though
an attractive early attempt to explain the stress/performance relationship, empirical testing
has suggested that the theory is not generalizable to many situations, especially those
requiring fine motor control.
Other popular theories that have attempted to relate stress to performance are the
‘optimal zone’ theories. Of these, Hanin’s (1980) concept of an arousal zone of optimal
functioning (ZOF) has received the majority of empirical investigation. Though initially

45
criticized as a reiteration of the Inverted-U hypothesis (Yerkes-Dodson, 1908) (which
will be discussed at length in the next section), it is instead an interindividual account of
how arousal affects performance. The attractiveness of the model rests in the fact that it
accounts for individual differences, something the Inverted-U is unable to do. A similar
theory is Martens’ (1987) zone of optimal energy.
Csiksentmihalyi’s (1975) concept of a less sport-specific optimal arousal state (or
FLOW state) is another attempt to explain the activation of the organism at a level that is
most conducive to performing well. The flow state is characterized by a variety of factors
including (1) awareness, but not being aware of awareness, (2) focused attention, (3) loss
of the ego and self-consciousness, (4) feeling of being in control, and (5) intrinsic reward
from performing well. Often athletes refer to the flow state in discussing their best
performances and continued research is being directed toward understanding the factors
that allow athletes to enter this relaxed state of intense concentration and seemingly
effortless ability to perform at the highest levels.
Another related theory to that of the ‘optimal states’ is Kerr’s (1989) Reversal
Theory. Based on Apter’s (1982) phenomonological theory of motivation, emotion,
personality, and psychopathology, Kerr’s basic premise is that depending on the
metamotivational state in which the athlete is currently involved, there is a combination of
arousal and “hedonic tone” (feeling of pleasure) that dictates whether that state will be
associated with anxiety, pleasurable excitement, boredom, or relaxation. A discussion of
the intricacies of reversal theory is beyond the scope of the current review, but it does

46
provide a unique way to view the arousal/anxiety/performance relationship and warrants
further investigation.
Inverted-U Hypothesis
Of the theories that have been proposed to account for the relationship between
stress and performance, perhaps the most influential and misunderstood is Yerkes-
Dodson’s (1908) Inverted-U hypothesis. The basic premise of the Inverted-U hypothesis
(which was generated based on work with animals) is that as arousal increases so does
performance until an optimal level is reached. At this point, any increase in arousal level
will lead to a gradual deterioration of performance until arousal level is reduced to the
optimal level (Yerkes-Dodson, 1908). Unfortunately, sport psychology research has been
reluctant to abandon the rather shallow notion of the Inverted-U hypothesis due to the
simplistic nature of the theory and its almost universal application. The myths and realities
surrounding this controversial theory and the research undertaken that both supports and
refutes it will be briefly reviewed in the following section.
It has been postulated that one mediator of the stress/performance relationship is
the characteristics of the task. In regard to the influence of task characteristics on the
stress/performance relationship (and assuming the Inverted-U relationship of stress to
performance), Oxendine (1970, 1984) and Oxendine and Temple (1970) suggested that
different types of tasks require different levels of arousal. According to Oxendine, a
moderately above resting level of arousal is required for the successful execution of all
motor tasks. Also, a low level of arousal is best for tasks involving complex movements,

47
very fine motor control, steadiness, and concentration. Finally, in gross movements
requiring strength, endurance, and speed, a high level of arousal is most beneficial.
Though intuitively appealing, Oxendine’s suggestions have been criticized due to
their simplicity (Jones, 1990). Jones provides several examples of sport situations where
Oxendine’s hypotheses do not hold true and cites three primary reasons for their lack of
value. First, only one of the three predictions has held up to empirical examination; that
relatively lower levels of arousal are most advantageous for complex, highly specialized
tasks. Also, his classification system is overly simplified in that entire sports such as
basketball which requires extremely diverse arousal states during the course of the game
can be categorized in one of the three levels. Finally, Jones (1990) suggests that Oxendine
does not consider the cognitive requirements of the skills in favor of focusing on the
movement parameters in particular.
Another one of the primary criticisms of the Inverted-U hypothesis is its global
nature. It seemingly relies on the notion of a general stress response that influences
performance (e.g., Neiss, 1988). Levi (1972) made an early attempt at separating the
different components of the stress response by suggesting that both high and low levels of
arousal could be experienced as stressful. In this vein, he proposed that an increase in
stress would result from the further deviation of the arousal state from the optimal level.
However, these ideas have also been criticized and basically dismissed by the newer
concepts of the interactionist approach to stress in which individual differences in the
perception of the stress response are accounted for, not simply the fact that being
underaroused or overaroused causes stress.

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Another problem with the Inverted-U description of the stress/performance
relationship is that it is a description and nothing more (Jones, 1990). No explanation is
offered for why performance is impaired when arousal deviates from the optimal level.
Though factors such as attentional allocation of resources, attentional narrowing, and
hyperdistractibility have been suggested and many have been investigated, the Inverted-U
hypothesis specifies none of these as the primary contributor to the decline in performance
as arousal deviates from optimal levels. More than likely, it is a combination of these
factors that impacts on the ability of the performer to function efficiently and to process
information effectively in the stressful environment.
Another criticism that has been levied against the Inverted-U hypothesis is that it
does not address specifically how performance is influenced. Rather, the hypothesis
merely states that overall capabilities, in a very general sense, are dependent on the level of
stress. Obviously, this description is entirely too global and does not explain how such
variables as speed of information processing, stimulus detection ability, and response
accuracy are affected (Eysenck, 1984). Furthermore, as will be addressed later, the actual
shape of the Inverted-U hypothesis has been questioned by those who assert a more
dramatic decrease in performance at high levels of anxiety/arousal with a more difficult
recovery to high performance levels as anxiety/arousal decreases (Hardy & Fazey, 1987).
It has been suggested that there is virtually no sound evidence to support the
Inverted-U hypothesis (Hockey, Coles, & Gaillard, 1986; Naatanen, 1973; Neiss, 1988).
Perhaps, of the critics of the Inverted-U, Neiss (1988) is the most rabid, calling the
empirical evidence in favor if the Inverted-U “psychologically trivial”. Other researchers

49
have been equally adamant regarding its lack of applicability, validity, and credibility,
calling it a “catastrophe” and a “myth” (Hardy & Fazey, 1987; King, Stanley, & Burrows,
1987). The criticisms and negative connotations associated with the Inverted-U
hypothesis prompted Neiss (1988, 1990) to suggest that the study of arousal in the
context of the Inverted-U should be abandoned for the following reasons: (1) it cannot be
falsified, (2) it cannot function as a causal hypothesis, (3) it has trivial value if true, and
(4) it hinders understanding of individual differences in regard to the stress response.
Others suggest that it merely needs to be reformulated to account for individual
differences and to address the underlying mechanisms that specify the facilitative and/or
detrimental effects of stress (Anderson, 1990; Hanin, 1980; Martens, 1987). Researchers
have addressed such areas as the nature of the task (e.g., Weinberg, Gould, & Jackson,
1985), skill level (e.g., Cox, 1990), and individual differences (e.g., Ebbeck & Weiss,
1988; Hamilton, 1986; Spielberger, 1989) with respect to the Inverted-U hypothesis.
However, the understanding of these specific components is only beginning to be
surmised.
Perhaps much of the confusion, equivalence of empirical results, and lack of
consistency in research findings that has been associated with the Inverted-U can be
attributed to the multitude of experimental methods that have been used to examine it and
the lack of consistency in differentiating the various components that embody the term
“stress”. A discussion of the specific components that fall under the guise of “stress” will
be presented in the following section.

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Stress. Arousal, and Anxiety
As mentioned earlier in the review, stress is characterized by a combination of
stimuli or a situation that comprises the circumstance of a person’s subjective experiences
as threatening and which causes anxiety (Hackfort & Schwenkmezger, 1993). According
to this view, stress occurs when one is unable to cope with a particular situation, and it
arises due to specific ‘constellations’ of threatening stimuli. Various stressors include
internal and external threats, performance pressures, social threats, and sport-specific
circumstances. One of the specific components of stress is anxiety.
Anxiety is an emotion characterized by uncertainty; a state of unoriented activation
that is learned through the socialization process and direct exposure to anxiety-producing
situations (Sage, 1984)). Fear, on the other hand, though similar to anxiety, is
characterized by the perception of danger in response to a known threat, is a reflex-like
defense, and is logical, self-protective, and adaptive (Hackfort & Schwenkmezger, 1993).
According to Cattell and Scheier (1961), fear is a specific reaction while anxiety is caused
by anticipatory and imaginative processes. Thus they are based on the degree of
specificity and recognizability.
Spieiberger (1966, 1972, 1983) defines stress as being closely related to state and
trait anxiety. The trait component is exhibited as an acquired behavioral disposition,
independent of time, causing the person to perceive a wide range of not very dangerous
circumstances as threatening. Conversely, state anxiety refers to subjective, consciously
perceived feelings of inadequacy and tension accompanied by an increase in arousal in the
autonomic nervous system. These characteristics are influenced by both cognitive and

51
emotional components in which the person is preoccupied with irrelevant thoughts and
eventual subjective excitement when the ego is threatened. Spielberger’s Anxiety Theory
(1966, 1972) states that those with higher trait anxiety tend to respond to stressful
situations with even higher state anxiety. In accordance with this view, studies (e.g.,
Hackfort & Schwenkmezger, 1989) have indicated that those who exhibited higher trait
anxiety reported anxiety as debilitating while those who were not trait anxious reported it
as facilitative to performance. Similarly, Martens (1971, 1974) determined that highly
anxious persons perform better on some tasks while lower anxious do better on others and
that the state anxiety level at the beginning of the learning process depends on the trait
anxiety level of the person. Furthermore, there appears to be an unexplored interaction
between anxiety level, situation-specific stress stimuli, task difficulty, and situation specific
conditions of learning and performance.
Another important distinction must be made between cognitive and somatic
anxiety. Cognitive anxiety is characterized by a state of worry, the awareness of
unpleasant feelings, and concerns about ability to perform and concentrate in a particular
environment. Worry is a cognitive process that takes place prior to, during, and after a
task and is marked by decreases in faith in the performance, increased concern, social
comparison, and fear of failure (Hackfort & Schwenkmezger, 1993). These
characteristics of worry may represent cognitive, evaluative processes that are suitable for
predicting performance, as high levels of worry tend to lead to lower levels of
performance (Martens, Burton, Vealey, Bump, & Smith, 1990).

52
Conversely, somatic anxiety refers to the perceptions of physiological arousal
such as shakiness, sweating, HR, respiration, and “butterflies” in the stomach. A
synonymous term used to describe somatic anxiety is “emotionality”, characterized by
affective physiological system changes caused by an increase in arousal level (nervousness,
increased HR, etc.) (Zaichkowski & Takenaka, 1993). Furthermore, cognitive and
somatic anxiety appear to have different antecedents. Somatic anxiety is elicited by a
conditioned response to competitive stimuli while cognitive anxiety is characterized by
worry or negative expectations about an impending performance or event. A handful of
studies has suggested that there tends to be a negative link between worry and motor
performance while there appears to be a positive link between somatic anxiety and
performance (e.g., Gould, Weiss, & Weinberg, 1981).
Due to the relevance of somatic anxiety to arousal, these terms are often used
interchangeably. However, there is a clear distinction between the two terms. Somatic
anxiety refers to perceptions of physiological states and is, therefore, a psychological
characteristic. On the other hand, arousal reflects the natural activity of one’s physiology
and is therefore a purely physiological construct (Rotella & Lerner, 1993). In this respect,
somatic anxiety is influenced by the subjective evaluation and interpretation of arousal.
The specific physiological mechanisms that govern arousal level are thought to be
regulated by the neurophysiology of the central nervous system, in particular. The four
primary structures involved include the cerebral cortex, the reticular formation, the
hypothalamus, and the limbic system. The cortex is responsible for cognitive appraisal of
incoming stimuli, the reticular formation acts as an organizer with the other components,

53
the limbic system provides emotional input in the regulation of arousal, and the
hypothalamus regulates sympathetic nervous system activity along with the pituitary gland
(Zaichkowski & Takenaka, 1993). These upper level control systems exert their influence
on the sympathetic nervous system which is primarily responsible for the
psychophysiological changes in HR, pupil dilation, respiration rate, blood glucose levels,
and other physiological responses.
Though the description of arousal appears straightforward, researchers have
conceptualized it in various ways. For instance, Sage (1984) suggests that arousal is
synonymous with activation level. Magill (1989) discusses it in a motivational context that
serves as an energizing agent to direct behavior to a specific goal. Cox (1990) has
defined arousal as alertness while Martens (1987) dislikes the term “arousal” altogether
and prefers the term “psychic energy” which serves as the cornerstone of motivation.
Based on these current views of arousal, collectively, anxiety appears to be a
multidimensional construct that serves as an energizing function of the mind and body and
varies along a continuum from sleep to extreme excitement. It contains a general
physiological response in which several systems may be activated at once in including HR,
sweat gland activity, pupil dilation, and electrical activity of the brain. It also includes
behavioral responses (performance) and cognitive processes (appraisal of physiological
arousal).
Therefore, in order to gain an accurate assessment of arousal, physiological,
behavioral, and cognitive components must be assessed (Borkovec, 1976). It should be
emphasized that changes in physiological function are not necessarily indicative of arousal,

54
and therefore must be accompanied by other measures because any of the physiological
components can be altered without impacting the others (Lacey & Lacey, 1958). These
issues will be addressed again later in the discussion of multidimensional anxiety theory.
Assessment of the Stress Response
As mentioned, due to the multidimensional nature of the stress response, multilevel
assessment is absolutely necessary to gather a better understanding of the influence of the
various components of stress on performance. Assessment effectiveness can be
maximized through the combination of physiological, behavioral, and cognitive (self-
report) measures. Physiological indices of arousal include such measures as skin
resistance, pupil dilation, heart rate, electroencephalogram, electrocardiogram,
electromyogram, and other biological measures. The advantages of physiological
assessments are that they are not tied to verbal statements. Also, they can be used with all
types of people and can assess changes in arousal continuously. However, the primary
disadvantage is the fact that physiological measures lack high correlations among each
other, a condition Lacey and Lacey (1958) referred to as autonomic response stereotype.
Also, in most sport contexts, physiological measures will be confounded by other
physiological changes due to exercise-induced responses.
Another level of assessment is behavioral. Observation of behavioral change (such
as the presence of nervous twitches, vomiting, etc.) can provide an indication of the stress
response. Unfortunately, often behavioral observations may be attributed to stress when
the actual root of the behavior is not stress-produced. For instance, vomiting could be

55
due to the flu rather than competitive stress. Therefore, often self-statements are needed
to interpret behavioral observations.
Assessment at the cognitive level is usually done through self-report measures.
Some of the more popular measures of anxiety include the State-Trait Anxiety Inventory
(STAI: Spielberger, Gorsuch, & Luschene, 1970), the Sport Competition Anxiety Test
(SCAT: Martens, 1977), and the Competitive State Anxiety Inventory - 2 (CSAI-II:
Martens, Burton, Vealey, Bump, & Smith, 1990). It should be mentioned that most
cognitive measures of arousal that have been used are those that measure anxiety, not
arousal. Though much time and effort has been devoted to the development of these self-
report measures, Kleine (1990) conducted meta-analyses that indicated only a moderate
relationship between various measures of anxiety and performance. Furthermore, his
results suggested that the STAI (a non-sport-specific measurement tool) was as good as
the SCAT (sport-specific) for predicting performance in sport. Further criticism has been
directed toward the SCAT due to the unidimensionality of the instrument (assessing only
the cognitive aspects of anxiety) and its bias toward assessment of the frequency of
debilitating anxiety while ignoring possibly facilitative aspects.
As mentioned, one of the primary weaknesses of research on stress and more
specifically, anxiety, is the lack of multidimensional assessment. The CSAI-2 is more
multidimensional in nature as it separates measures of cognitive and somatic anxiety and
has been used extensively in sport research. The reliability and validity of the instrument
and its ability to measure the multidimensional nature of anxiety is laudable. The next
section of the review addresses the multidimensional nature of anxiety and the importance

56
of obtaining a better understanding of the influence of specific components of anxiety and
their influence on performance from both a basic and applied point of view.
Multi-Dimensional Anxiety Theory
Due to recent concern with the lack of usefulness of the Inverted-U model of
anxiety and/or arousal, theorists began to search for a better explanation of the
stress/performance relationship. Researchers began to attempt to break down the stress
response into its various components. These concerns eventually lead to the formation of
multidimensional anxiety theory which has also spurred the development of other theories
such as Hardy and Fazey’s (1987) catastrophe theory. The generation and a general
summary of multidimensional anxiety theory follows.
Perhaps the first to attempt a defragmentation of the general stress response were
Liebert and Morris (1967), who identified two primary contributing factors to anxiety:
Worry and emotionality. In Liebert and Morris’s view, worry consisted of cognitive
concerns about one’s performance while emotionality referred to the autonomic reactions
to the performance environment. This initial identification heavily influenced Davidson
and Schwartz’s (1976) multidimensional model of anxiety. They were the earliest to use
the terms “cognitive” and “somatic” anxiety and formulated their theory in the context of
clinical applications. Thus, worry has become synonymous with cognitive anxiety and
emotionality has become synonymous with somatic anxiety. Cognitive anxiety is typified
by the awareness of unpleasant feelings and concerns about ability to perform and to
concentrate. Conversely, somatic anxiety is characterized by perceptions of physiological
arousal such as shakiness, sweating, HR, respiration, and “butterflies in the stomach”.

57
These general characteristics of the components of anxiety have held up under empirical
investigation and appear to be manipulable independently (e.g., Schwartz, Davidson, &
Goleman, 1978).
As mentioned, another characteristic of the multidimensional components of
anxiety is that they appear to have different antecedents. Somatic anxiety is elicited by a
conditioned response to the competitive environment, while cognitive anxiety is
characterized by worry or negative expectations. Researchers have consistently shown
that somatic anxiety tends to build as the event (or competition) grows nearer and
dissipates as performance begins, while cognitive anxiety continually fluctuates as the
subjective probability of success varies (Jones & Hardy, 1990; Martens et al., 1990).
Martens et al. (1990) found that cognitive anxiety remains stable and high during the
period preceding an event while somatic anxiety peaks at the moment just before
competition. Likewise, in an earlier study, Spiegler, Morris, and Liebert (1968) reported
similar results in the context of test anxiety.
Another means in which cognitive and somatic anxiety differ is with respect to
their effects on performance. In accordance with differences in the time course of anxiety
onset, somatic anxiety would be expected to have no influence on performance while
cognitive anxiety would have a significant influence, due to the ever changing subjective
probability of success. Consistent with this prediction, Martens et al. (1990) found that
this was the case. However, other studies have shown an Inverted-U relationship of
somatic anxiety to performance (Burton, 1988). Furthermore, studies using the time-to-
event paradigm have found that cognitive anxiety actually has a positive effect on

58
performance in the days leading up to a competition (Hardy, 1996). Thus, it appears that
rather equivocal results exist on both sides of the argument. Jones and Hardy (1990)
interpret the disparity and lack of consistency in findings to the multitude of different
paradigms that have been devised and the abundance of analyses that have been applied to
reduce the data.
Another problem that exists with respect to multidimensional anxiety theory is the
two-dimensional approach used to explaining the effects of somatic and cognitive anxiety
on competition. Specifically, the two-dimensional approach in analyzing results tends to
neglect the interaction of the components of anxiety, treating them independently rather
than in combination (Hardy & Fazey, 1987). According to the viewpoint of Hardy and his
colleagues, any relatively comprehensive treatment of these components must consider
them in an interacting, three-dimensional manner. In an attempt to improve the
predictability and structure of the model, therefore, Hardy and Fazey (1987) developed a
catastrophe model of anxiety and performance.
A Catastrophe Model of Anxiety
In an effort to advance understanding beyond the multidimensional approach to the
study of the effects of anxiety and arousal on performance, Hardy and Fazey (1987)
formulated a three-dimensional model of the relationship. Borrowing heavily from Thom
(1975) and Zeeman (1976) who originally devised the idea of catastrophes and then
applied them to the behavioral sciences, respectively, Hardy and Fazey’s (1987) model is
closest in form to the cusp catastrophe, one of the seven originally proposed catastrophe
models of Thom (1975). Zeeman (1976) borrowed Thom’s original ideas and described

59
the theory by developing a machine to model it. When describing human behavior,
however, events are less mechanistic and absolute, requiring the model to be revised such
that changes in one variable (i.e., anxiety or arousal) increases the likelihood that the
dependent variable (i.e., behavior) will change in a predicted direction.
Of the two independent variables in Hardy and Fazey’s (1987) model, anxiety is
the “splitting factor”, the variable that determines performance levels and ultimately,
catastrophes. The roles of cognitive anxiety and physiological arousal were chosen
specifically to be able to evaluate testable hypotheses with respect to the
anxiety/arousal/performance relationship. Specifically, when cognitive anxiety is low, the
model predicts that physiological arousal will influence performance in an inverted-U
fashion. However, when physiological arousal is high, such as on the day of competition,
high levels of cognitive anxiety will result in lower levels of performance. When
physiological arousal is low, such as during the days leading up to competition, higher
cognitive anxiety will lead to increases in performance. When cognitive anxiety is high,
the effect of physiological arousal depends on how high cognitive anxiety is elevated.
Usually the manipulation of anxiety and arousal is carried out through a time-to-event
paradigm in which assessments are taken at specified times leading up to a competition
setting. For instance, assessments will be taken one week prior, two days prior, and then
one hour prior to the competition setting. In this way, the time course of anxiety and
arousal can be assessed. In other instances, levels of anxiety and arousal are manipulated
through the use of both ego-threatening or other anxiety producing instructional sets and
through the use of exercise-induced arousal, respectively.

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Testable hypotheses have been generated from the conceptual framework of the
original catastrophe model (Fazey & Hardy, 1988). According to the model, physiological
arousal changes are not necessarily detrimental or facilitative to performance. However,
if physiological arousal is high, it can have catastrophic effects on performance in
situations where cognitive anxiety is also high. Another prediction is the hysteresis effect.
Due to the splitting effect of cognitive anxiety, under high cognitive anxiety conditions,
physiological arousal will have a differential effect on performance when it is increasing as
opposed to when it is decreasing. A third prediction is that intermediate levels of
achievement are most likely to occur under conditions where cognitive anxiety is high.
Finally, Fazey and Hardy (1988) suggest that it is possible to fit statistical models to cusp
catastrophes.
One notion that may become obvious in the discussion of the differences in the
catastrophe model versus multidimensional anxiety theory is the suggestion that cognitive
anxiety can facilitate performance at certain times, especially in the days leading up to
competition. This is in direct contrast to most studies of cognitive anxiety that have
demonstrated a negative relationship between it and skill execution. With further
thought, however, it is obvious that the motivating effects of cognitive anxiety in the days
leading up to competition could eventually facilitate achievement capabilities. Also, it
should be emphasized that in many of those studies in which a negative relationship has
been identified between cognitive anxiety and performance, assessment was made on the
day of competition, when physiological arousal can be assumed to be relatively high
(Hardy, 1996).

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Another obvious feature of the cusp catastrophe model of the anxiety-performance
relationship is the choice of physiological arousal rather than somatic anxiety as the normal
factor. The primary reason for this choice was based on the notion that it is part of the
organism’s natural physiological response to anxiety producing situations (Hardy, 1996).
The physiological response to performance anxiety is sufficiently well-established to be
considered in the context of a generalized response within the competition setting.
However, though the physiological response may be reflected in self-reports of the
presence of somatic anxiety, the purely physiological index can encompass the individual
task requirements, different situations, and other combinations of factors that override
reports of somatic anxiety. Furthermore, physiological arousal changes tend to be
mirrored by changes of somatic anxiety while the converse is not the case (Fazey &
Hardy, 1988; Hardy, 1996; Hardy & Fazey, 1988).
Substantial support has been shown for the cusp catastrophe model of the anxiety
performance relationship in seminal investigations of the model by Hardy and his
colleagues. Hardy, Parfitt, and Pates (1994) and Parfitt, Hardy, and Pates (1995)
conducted two studies to examine the relationship. In the first of their studies, the time-
to-event paradigm was implemented to manipulate anxiety independently of physiological
arousal in female basketball players and was primarily directed toward examining the
hysteresis hypothesis. Physiological arousal was measured by a Polar heart rate monitor
(HRM) and cognitive and somatic anxiety were measured with the CSAI-2. The task was
a basketball free throw that was performed after completing physiologically arousing
exercise. Findings indicated that both cognitive and somatic anxiety were elevated on the

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day before the tournament. This was a somewhat different finding as compared with
previous studies in which somatic anxiety increases usually only occurred on the day of the
significant event. The data with regard to the hysteresis hypothesis were supportive. In
general, performance followed a different pathway with respect to heart rate when
increasing as opposed to when it was decreasing in conditions of high cognitive anxiety
but not in conditions of low cognitive anxiety.
In the second experiment, Parfitt, Hardy, and Pates (1995) examined the
generalizability of these findings with women basketball players to male crown green
bowlers. The exception in this study was that cognitive anxiety was manipulated through
the use of instructional sets rather than through the use of the time-to-event paradigm.
The results of the first experiment were replicated in that the three-way interaction
between cognitive anxiety, HR, and direction of heart rate change influenced performance
in predictable directions.
Another interesting finding that provides support for the cusp catastrophe is a sub¬
prediction that performance will be most variable under the high and low cognitive anxiety
conditions (Hardy, 1996). Specifically, according to the surface of the performance curve,
it would be predicted that the highest levels of performance achieved in the high anxiety
condition would be higher than the highest levels achieved in the low anxiety condition.
Similarly, the lowest levels of performance in the high anxiety condition would be lower
than the lowest levels of performance in the low anxiety condition. In fact, these
hypotheses were supported in the second study; thereby providing evidence to support the
cognitive anxiety component as the splitting factor on the performance surface (Parfitt,

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Hardy, & Pates, 1995). Though relatively little work has been done to examine the
validity of the cusp catastrophe model, initial results provide evidence to support it and
many fruitful areas of research in this area are warranted.
One limitation, however, to the study of anxiety in the context of both catastrophe
theory and the other models mentioned above, is a lack of explanation for the performance
changes that are noticed in overly stressful situations. One specific cognitive mechanism
that has been implicated, but has received limited empirical investigation is the impact of
anxiety and arousal on attentional resources. The following section will outline some of
the research that has been directed toward examining this relationship.
Anxiety. Arousal, and Attention
One of the critical factors that could contribute to performance changes under
anxiety or arousal producing situations is the ability to allocate attentional resources in the
appropriate areas and to process information gathered in these areas effectively
(Kahneman, 1973; Landers, 1978; Nideffer, 1976, 1989). Evidence seems to suggest an
arousal/ performance relationship that is mediated by attentional factors. Support has
been found for this notion in both anecdotal and empirical evidence (Nideffer, 1988).
Perhaps the most compelling evidence that favors the notion of a mediating role of
attentional processes in the anxiety/arousal/performance relationship is the substantial
support provided for the idea of attentional (or peripheral) narrowing. Research has
indicated consistent changes in the peripheral acuity of subjects assessed in arousal and/or
anxiety producing situations. Various studies have indicated a narrowing of attention that
occurs in highly stressful environments, resulting in a tunneling effect where peripheral

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cues are selectively attenuated from further processing. Using dual task paradigms, results
have shown a facilitative effect in the performance of central tasks with a concomitant
decrease in performance of peripheral tasks when performed under a state of increased
arousal or anxiety. Literature relevant to the attentional narrowing idea will be reviewed in
the following section.
Peripheral Narrowing
The first researchers to address the idea of peripheral narrowing in terms of cue
utilization were Bahrick, Fitts, and Rankin (1952). Based on the assumptions that
anything to which an organism responds is relevant to performance, and that continuously
variable information is more important to interpreting a stimulus than are relatively
constant sources, Bahrick et al. (1952) hypothesized that perceptual selectivity would be
highly dependent upon cues available. They postulated that objects in the peripheral visual
field (as well as those aspects of the central task that are relatively unimportant) would
tend to be interpreted as less important than those in the central part of the field. Using a
tracking task and several intermittent peripheral tasks, they found that when subjects were
offered incentives, performance on the central task was superior to performance on
peripheral ones. These results were interpreted as suggesting that performance was
influenced by the degree of motivation manipulated by the incentives provided.
Easterbrook (1959) produced the most influential article on the topic of cue
utilization based on the findings of Bahrick et al. (1952), and others (e.g., Bruner, Matter,
& Papanek, 1955; Callaway & Dembo, 1958; Callaway & Thompson, 1953). Easterbrook
indicated that as the level of arousal increased to a certain point, performance on the

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central task was facilitated due to the blocking of irrelevant cues in the periphery from
being processed. In contrast, as arousal increased, he suggested that performance on tasks
requiring less of a central focus deteriorated due to the blocking of relevant cues.
Furthermore, performance on central tasks deteriorated if arousal level reached a state in
which the tunneling effect prohibited attention to relevant cues that were integral to
performance of the central task.
Easterbrook (1959) suggested that the degree of facilitation or disruption caused
by emotional arousal is dependent on the range of cues required to perform a task
effectively. These ideas were consistent with Woodworth’s (1938) concept of a
“recepto-effector span”, an index of the range of cue utilization. The size of the recepto-
effector span is related to the number of possible responses permitted following a stimulus,
and the influence of warning time on the ability to prepare responses. Based on the work
of Bartlett (1950) and Poulton (1957), Easterbrook suggested that in serial task
performance, “the effect of increased foreknowledge is that responses can be made in
larger units so that inter-response delay times become covert, inter-response junctions are
smoothed, net speed increases, precision improves, and the performance may be better
described as better integrated” (p. 186). Therefore, in tasks that require a large range of
cue utilization (larger receptor-effector spans), performance will be facilitated with an
increase in the amount of advanced preparation allowed. In relatively simpler tasks,
however, requiring reduced cue utilization and attention, a surplus in capacity to attend to
and process information exists, permitting the processing of (and distraction due to)
irrelevant cues (e.g., Porteus, 1956). In accordance with this view, effective execution on

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a variety of serial tasks including paced problem solving, mirror drawing, and tracking, has
been shown to decrease in anxious subjects as compared to control groups.
Easterbrook (1959) was insistent on the interdependence of perception and
response, based on the premise that a response cannot be made without some type of
perception. Similarly, in the absence of a response, it is virtually impossible to determine
whether perception occurred. Through this conceptualization, he defined the meaning of a
“cue” as occurring when a singular related response has been made to a percept.
Likewise, in highly variable situations, containing many cues, a response to a particular
cue can be said to have occurred when the response takes the form of the normal response
in the absence of other cues. In light of this operationalization of cue meaning, several
researchers during the 1950’s found that a tunneling or reduction of the perceptual field
resulted from induced psychosomatic stress (e.g., Callaway & Thompson, 1953; Combs
& Taylor, 1952). In most perceptual tasks administered, manipulations causing the range
of cue utilization to fall below that required to complete the task resulted in relative
decrements in achievement (Eysenck, Granger, & Brengelman, 1957; Granger, 1953).
However, it is important to note that the degree of skill deterioration on tasks is highly
relevant to task complexity. As Easterbrook wrote,
For any task, then, provided that initially a certain portion of the cues in use
are irrelevant cues (that the task demands something less than the total capacity of
the organism), the reduction in range will reduce the proportion of irrelevant cues
employed and so improve performance When all relevant cues have been
excluded, however, (so that now the task demands the total capacity of the

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subject), further reduction in the number of cues employed can only effect relevant
cues, and proficiency will fall. (p. 193)
One may question the effect of learning on the ability to select and process only the
most relevant cues in a display. Support for the idea that overlearning improves the ability
to select appropriate cues has been provided by Bruner, Matter, and Papanek (1955). In
regard to learning, the question of “What makes a cue relevant or irrelevant?” must be
answered. In other words, how does a person know what cues to select and what
information will be gained from selection of particular cues? It appears that cue relevance
is specified by the amount of information obtained from a cue and the task requirements at
hand. Thus, cue utilization is not merely a perceptual idea, but one that is mediated by the
“cerebral competence of the subject” (Easterbrook, 1959, p.196). Consequently, the
ability to select and incorporate the most relevant cues while ignoring irrelevant cues is
intricately tied to the intellectual competence of the person who must competently
complete various tasks.
Easterbrook’s (1959) conceptual contribution spurred much work to investigate
the mediating factors that influence the degree of peripheral narrowing, and the related
facilitation and inhibition in skill level resulting from this condition. The methodologies
used and factors investigated are quite varied. As a result, this review, though somewhat
comprehensive, cannot account for all studies that have been related to the concept of
peripheral narrowing.
Studies concerning the cue utilization theory were prevalent in the 1960’s and
1970’s and lent credence to Easterbrook’s (1959) ideas. Because much of Easterbrook’s

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theory is related to the conceptualization of arousal as a driving force which directs
behavior in a way to reduce the desire for something, many early researchers investigated
the cue utilization theory with this underlying theoretical backdrop. For instance, Eysenck
and Willett (1962) classified subjects into high and low drive categories based on whether
or not they had passed their entrance examination into a training school. Those who had
passed were classified as high-drive subjects, while those who had not were classified as
low drive subjects. Findings indicated that performance on a Tsai-Partington Numbers
test was significantly greater for subjects characterized by high drive rather than those
categorized in the low-drive condition. Though not a direct test of the cue utilization
hypothesis, the results do suggest limited performance on this highly visually dependent
task by those who were at presumably lower drive levels.
A direct examination of the cue utilization theory was conducted by Agnew and
Agnew (1963) who used two different tasks, the Porteus maze and the Stroop Color-
Word Interference test. Investigated was whether tasks which demand differing levels of
attentional span would be effected differentially by increasing and decreasing stress levels
as manipulate through electric shock. Success in the Porteus maze task, one that requires
a wide range of cue utilization, was detrimentally influenced by electric shock. However,
proficiency in the Stroop color word test, requiring a more narrow range of cue
utilization, was facilitated by increased levels of arousal. These results provided
substantial evidence for the validity of the cue utilization hypothesis.
A similar study was conducted by Tecce and Happ (1964) in which performance
on a card sorting task and the Stroop Color-Word Interference test were assessed while

69
stress levels were manipulated through electric shock. In this way, both relevant and
irrelevant stimuli were presented that would be thought to impede performance of the
central sorting task. Similar findings to those of Agnew and Agnew (1963) were
obtained in which the shocked subjects performed better on the card sorting task than did
a no-shock control group.
Another early study in which the cue utilization hypothesis was examined
incorporated a state measure of anxiety as assessed by the Taylor Manifest Anxiety Scale
(TMAS: Zaffy & Bruning, 1966). Those participants who scored in the upper and lower
20% of the distribution of TMAS scores were selected for the study. The task consisted
of learning 19 multiple choice items with 5 zeros for each 19 choice set. With the
presentation of the zeros which had to be identified, either a relevant cue, an irrelevant
cue, or no cue was presented. Findings showed that the low anxiety subjects performed
worse than the high anxiety subjects, responding to both relevant and irrelevant cues while
the high anxiety subjects responded to only the relevant cues, ignoring the irrelevant ones.
Follow up experiments using the same task as Zafly and Bruning (1966) but reducing the
items from 19 to 15 and increasing the choices from 5 to 7 provided similar results
(Bruning, Capage, Kozuh, Young, & Young, 1968).
In their first experiment, Bruning et al. (1968) manipulated anxiety through the
presence or absence of the test administrator, while in the second experiment, anxiety was
manipulated by feedback regarding the subject’s success and failure. Results in the first
experiment replicated the findings of Zafly and Bruning (1966). However, in Experiment
2, it was determined that the high drive subjects were superior in the irrelevant condition

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while the low drive subjects were superior in the relevant cue condition. Because subjects
responded in different manners based on their anxiety disposition, results do provide some
support for the attentional narrowing idea.
Wachtel (1968) also conducted a study to examine the cue utilization hypothesis.
However, his goal was to determine whether the cue utilization tendencies could be
altered through offering participants a means of coping with the anxiety. The tasks
consisted of a central continuous tracking task while identifying a random presentation of
peripheral lights. Performance was based on a combined score of accuracy on the pursuit
rotor task as well as reaction time to the peripheral lights. Three groups were tested in
which one was a control group, the second group was told that it would receive random
shocks that were independent of performance, and the third group was told that the longer
it went without a shock, the stronger the shock would be. However, this group was also
told that it would not be shocked as long as sufficient achievement was demonstrated.
Results indicated that groups 1 and 3 reacted slower to the peripheral stimulus, suggesting
that proficiency was impaired under the threat of electric shock, but not if the subjects had
a means of escaping it. Thus, once again, it appears that stress affected peripheral task
performance while facilitating the central task performance.
Hockey (1970) tested Easterbrook’s ideas based on the notion that the differential
selectivity effect observed between central and peripheral tasks is based not on the actual
location of the stimuli but rather the allocation of priorities to the two tasks. He
postulated that the high subjective probability of relevant signals to occur in the central
field predisposes subjects to focus attentional scanning of signals to the primary task.

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Using the manipulation of noise levels on central and peripheral tasks in which he ensured
that all signals were detected (making objective and subjective probabilities identical),
Hockey (1970) hypothesized that if a probability mechanism (priority allocation) was
working, a greater facilitation of central detections in noise would occur only when the
signal distribution was biased toward the center of the display. Attentional changes due to
noise were inferred by the latency of response to central and peripheral locations. Support
for the probability hypothesis was found. The response latency was faster when signals
were biased toward the center of the visual field, but not when probability was equal of the
signal being presented in the central or periphery. This explains, in part, that the tunneling
which occurs is a function of the higher probability of relevant cues occurring in the
central area, rather than as a function of the spatial location of the signal.
Bacon (1974), using a signal detection approach (Green & Swets, 1966), assessed
the nature of stimulus loss by hypothesizing that there is not necessarily a loss of
perceptual sensitivity to peripheral or irrelevant stimuli, rather a shift in the subjective
decision criterion to respond to peripheral cues occurs. Due to the inconsistencies
reported regarding whether performance on central tasks is enhanced or diminished,
Bacon suggested that cues that initially attract less attention will show even less attention
devoted to them while those that occupy the primary focus of attention attract an even
higher degree of attentional processing.
Using a dual task paradigm, Bacon’s (1974) results supported Easterbrook’s
(1959) hypothesis in that the increase in arousal (induced through electric shock) caused a
funneling of attention toward central areas and away from the periphery. More pertinent

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to the hypotheses tested, however, it was determined that the decrease in attention
devoted to the periphery was, in contrast to the expected result, due to a decrease in
sensitivity rather than a shift in the subject’s criterion for responding. Furthermore, due to
the lack of ability to attend to both tasks as well in the aroused condition, the capacity
limitation ideas of Easterbrook (1959) were also supported.
Though obviously laboratory-based and basic in nature, these early studies
established significant support for the attentional narrowing idea. Eventually, these ideas
were tested in more applied arenas. The less controlled studies and observations which
will be summarized in the next section provided practical evidence for the viability of the
attentional narrowing idea in actual stress-producing environments.
Applications of Peripheral Narrowing Research
Baddeley (1972) reviewed both anecdotal and empirical evidence of peripheral
narrowing in “dangerous environments”. Citing such examples as those from military
combat observations, Baddeley (1972) provided substantial evidence of the impact of
perceptual narrowing on real world situations. For example, he found that in the heat of
battle soldiers will use their rifles much less efficiently than in training, the ratio of error to
hits in combat increases, and tonnage of bombs needed to destroy a target increases.
These are each examples of anecdotal reports that indict the deterioration of ability to use
the most relevant cues in dangerous or stressful environments.
Weltman and Egstrom (1966) and Weltman, Smith, and Egstrom (1971) applied
the idea of peripheral narrowing to a deep sea diving environment under differing
conditions of stress. The experimental conditions consisted of surface testing, shallow

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diving in an enclosed tank, diving at ocean depths of 20-25 feet (6-8 m), and simulated
decompression dives in a pressure chamber. In general, they found that across conditions,
performance was maintained on a centrally located monitoring task, but as stress level
increased (i.e., the divers descended to more dangerous depths), attention to peripherally
located light stimuli deteriorated. Though intriguing, these results may be contaminated
by other extraneous factors such as the increase of nitrogen levels in the blood stream.
Surprisingly, relatively few investigations have been undertaken in sport settings to
examine the effects of peripheral narrowing. Landers, Wang, and Courtet (1985)
investigated peripheral narrowing with experienced and inexperienced rifle shooters. The
central task was a target shooting task while the peripheral task was an auditory detection
task. Although there were no differences found in secondary task performance between
the experienced and inexperienced shooters, they did find that under high stress
conditions, both groups shot worse.
As to other sport situations, two studies were conducted by Williams, Tonymon,
and Andersen (1990, 1991) to help substantiate Andersen and Williams’ (1988) model of
athletic injury. In the model, Andersen and Williams (1988) indicate that a possible
predisposition to athletic injuries may be precipitated by elevated levels of life stress,
resulting in an inability to attend to peripheral stimuli. Support for this possibility was
found in the two studies designed to test the model in which Williams, Tonymon, and
Andersen (1990, 1991) found significant decrements in detection of peripheral cues while
performing Stroop tasks under stressful conditions.

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The work done with regard to attentional narrowing in the sport context was
reviewed by Landers (1980). In the review, the Inverted-U in sports was explained using
Easterbrook' s cue utilization hypothesis. In sport, as with other domains, Landers
suggested that performance is proportional to the number of cues utilized. At low arousal
levels, there is a surplus of cues, including irrelevant cues that must be dealt with. With
increasing anxiety levels, irrelevant stimuli are eliminated before relevant ones. Therefore,
according to Landers, perhaps there is a bi-directional, reciprocal causality between
arousal and performance in sport. Other theoretical proposals have been forwarded to
account for the narrowing phenomenon. These will be reviewed in the next section.
Theoretical Explanations for Peripheral Narrowing
Many theories have been forwarded to explain the consistent reduction in cue
utilization during performance of tasks in stressful environments. Easterbrook (1959)
proposes that if intensity cannot be discriminated between stimuli, a reduction in the
employment of cues results. The reduction in the range of cue utilization can also be
explained in the context of both Hull’s (1943) Drive theory and the Yerkes-Dodson
(1908) Inverted-U theory. In the Hullian sense, an increase in arousal (or drive) increases
the stimulus generalization of a particular stimulus, resulting in the application of a trained
response to stimuli other than the one of interest. In the Yerkes-Dodson argument, as
arousal increases, some cues lose their ability to evoke the proper response, hence
increased arousal, to a point, will be beneficial, after which decrements will result.
Easterbrook (1959) also implies that the cue utilization hypothesis fits nicely into
Broadbent’s (1957) idea of the single channel hypothesis of attentional capacity. Though

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less popular than current theories of attention, Broadbent’s notion that there exists a single
cue channel that will affect processing capabilities elsewhere in the system accommodated
the cue utilization hypothesis effectively.
However, the idea can also be supported in the context of more recent
capacity/resource models such as proposed by Kahneman (1973) or Wickens (1984).
These theories, though opposed with regard to the number of resource pools available,
suggest a limit in the resources accessible to attain optimal attention as determined by
priorities. In line with this view, one primary feature of high arousal levels is a narrowing
of attention because the allocation policy is likely to shift away from the periphery and
toward the central area. Thus, the allocation policy is also consistent with the probability
results obtained by Hockey (1970).
In summary, it appears as though arousal tends to overload the system, narrowing
the range of stimuli that are processed by impairing the memory traces of the stimuli of
lesser importance, such that processing can continue to be devoted to the more central
cues. It seems that narrowing could be due to both an impairment at the perceptual stage
of processing and at the short term memory stage. However, the exact location of
impairment has not been clearly identified.
Distraction
An idea that consistently recurs as an explanation for performance changes in both
central and peripheral tasks is a narrowing of the attentional beam in which irrelevant cues
are somehow filtered from processing, either in the perceptual or encoding stage of
analysis. However, virtually no one has assessed the impact of distractors in this context

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and the concept of distraction has received very little attention from sport psychology and
cognitive psychology researchers, in general. It seems logical, however, that the central
task proficiency decrements that eventually occur as stress levels increase could also be
explained in the context of distraction.
The lack of research directed toward understanding distraction is surprising
considering the imperative need to ignore distractors and focus only on the most critical
cues in any performance situation. It is also surprising considering that the concept of
distraction was actually addressed by William James as early as 1890. Though many of
the ideas of James are being empirically investigated even at the end of the 20th century,
distraction continues to be a virtually untapped area of research on attention. Meanwhile,
examples of athletes and other performers who have been victimized by distraction are
numerous (Moran, 1996). The need to avoid distraction has prompted leading sport
psychologists such as Orlick (1990) to suggest that it is one of the most important mental
skills required to be successful in sport. Perhaps this is why virtually all mental training
skills programs developed by sport psychologists are directed toward maintaining
concentration on the task and appropriate cues. Interfering thoughts need to be regulated
and irrelevant stimuli ignored.
Brown (1993; as cited by Moran, 1996) defines distraction as situations, events,
and circumstances which divert attention from some intended train of thought or from
some desired course of action. This definition is somewhat different from James’ (1890)
original conceptualization of distraction which was more directed toward the description
of distracting thoughts and being “scatter-brained”. Each of these views of distraction can

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be more easily understood if categorized in the context of internal and external types of
distractors (Moran, 1996). Internal distractors refer to mental processes that interfere
with the ability to maintain attention while external distractors are environmental or
situational factors that divert attention from the task at hand. Each of the two types of
distraction lead to a wandering of attention which Wegner (1994) has suggested is “not
just the weakness of the will in the face of absorbing environmental stimulation ... but
rather it is compelled somehow, even required, by the architecture of the mind” (p.3).
Wegner (1994) has postulated that because the mind tends to wander, there is an attempt
to hold it in place by repeatedly checking in to see whether it has wandered or not.
Unfortunately, this results in a Catch-22 because by evaluating, attentional focus is
inadvertently drawn to the exact thing that one is trying to ignore. He also suggests that
when highly emotional, attentional resources are reduced and the mind is inclined not only
to wander av/ay from where it should be attending, but also wanders toward that which
we are attempting to ignore.
Effects of distraction. Obviously, the typical effect of distraction is a decrease in
performance effectiveness. The most plausible explanation for the decrease in
performance when distracted by either external or internal factors is the decrease in
available attentional resources for processing relevant cues. This idea is consistent with
the limited capacity models of attentional resources proposed in different forms by various
attention theorists (e.g., Allport, 1989; Kahneman, 1973; Shiffrin & Schneider, 1977).
Because attentional capacity is limited, resources directed toward the processing of
distractors reduces available resources for the processing of task-relevant information.

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This idea is supported by studies which have shown that distraction effects are greater for
complex rather than simple tasks, and that distraction effects are greater as the similarity
of distractors to relevant cues increases (Graydon & Eysenck, 1989). As tasks become
more complex and distractor similarity increases, the attentional resources needed also
increases due to a reduction in the automaticity of cue discrimination. Thus, any increase
in distractibility will inevitably reduce the attentional capacity available for the primary
task.
Distraction and stress. Though empirical evidence is scarce, many researchers
have suggested that increases in emotionality as embodied by stress and the various
components that make up stress (i.e., anxiety, worry, arousal) increase susceptibility to
distraction. Emotional stress would be classified as an internal distractor as it does not
exist except in the mind of the performer; but often internal distraction is caused by the
erroneous perception of an external distractor (Anshel, 1995). Numerous examples to
support the notion that stress impedes performance due to distraction can be found in
verbal accounts and behavioral observations of “choking” in competitive environments.
Moran (1994, 1996) provides substantial anecdotal evidence that the impact of anxiety is
the absorption of attentional resources which could otherwise be directed toward the
relevant task. Baumeister and Showers (1986) indicate that increased worry causes
attentional resources to be devoted to task irrelevant cues while self-awareness theorists
such as Masters (1992) suggest that under stress, not only is attention absorbed by
irrelevant stimuli, but also the performance of normally automated skills becomes less
automated as resources begin to be intentionally directed toward the process of the once-

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automated movement. Self-awareness, then, interrupts the normally fluid mechanics of the
movement arid inevitably decreases performance. Finally, Eysenck (1992) has provided
empirical evidence that anxiety provokes people to detect stimuli which they fear, usually
those that diverts them from attending to relevant information.
Paradoxically, it appears that there are two equally attractive explanations for the
decrease in performance that occurs under high levels of stress. On one hand, proponents
of the attentional narrowing argument would suggest that under high stress levels (either
anxiety or arousal induced), the visual field narrows to block out irrelevant information,
and subsequently relevant information as stress continues to increase. On the other hand,
proponents of the distraction argument would suggest that actually a widening of the
attentional field occurs such that irrelevant or distracting cues receive more attention then
when under lower stress levels. Evidently, a controversy exists unless in some way, both
mechanisms could be working at the same time. Perhaps, an increase in anxiety and/or
arousal results in a narrowing of the attentional field while at the same time, especially at
higher levels of stress, increases susceptibility to distraction. Thus, many theories can
account for how stress affects attention and the eventual impact of attentional variation
on performance, but none address specifically why this phenomenon occurs. By briefly
examining research in visual attention, perhaps some clues as to what exactly is happening
in these contexts may be surmised.
Visual Attention
It has long been known that there is a direct relationship between human
performance capabilities and the informational load as well as the response demands

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associated with a particular task (Fitts & Posner, 1967; Hick, 1952; Hyman, 1953). That
is, as the level of response uncertainty (informational load) increases, so too does reaction
time (RT). More importantly, laboratory research tends to indicate that RT to a single
unanticipated visual stimulus is in the order of 180-220 ms, with this delay composed of
latencies associated with stimulus detection, response preparation, and neural and
muscular activity associated with a simple key press (e.g., Wood, 1983). Given these
latencies, there is an apparent discrepancy between the obvious time constraints imposed
by complex situations (those dominated by heightened levels of response uncertainty) and
the ability of elite performers to routinely select and execute the most appropriate motor
response.
Hardware vs. Software Approaches
In an attempt to understand this paradox, researchers have forwarded two
competing explanations. The first approach posits that expert performers differ from
novices in that they possess advanced psychophysical and mechanical properties of the
central nervous system (Abemethy, 1991; Burke, 1972). That is, proponents of this
theory believe that experts have much faster overall RT's (simple, choice, and correction
times) than do novices, and also possess greater optometric (static, dynamic, and mesopic
acuity) and perimetric (horizontal and peripheral vertical range) attributes. In accord with
the notion that humans are somewhat genetically programmed to possess these qualities,
this perspective has been termed the "hardware" approach of expertise.
Support for the hardware approach, however, has been very limited. Studies by
Helsen (1994), McLeod (1987), Starkes (1987), and Starkes and Deakin (1984), in which

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expert and novice athletes were compared on a number of laboratory tasks involving
visual mechanisms (depth perception, static visual acuity) and processing abilities (simple
and choice reaction time tasks) demonstrated no significant differences between the two
groups. Thus, it appears as though expertise cannot be explained by a CNS advantage on
the part of the expert.
In contrast to the hardware theory of expertise, proponents of the "software"
approach argue that experts have a much greater knowledge base of information
pertaining to their particular area of expertise. Differences in expert performance as
compared to novices is thought to be the result of a cognitive advantage, rather than a
physical advantage. For example, it is believed that expert athletes make faster and more
appropriate decisions based on acquiring selective attention, anticipation, and pattern
recognition strategies associated with their sport (Abemethy, 1991). That is, experts learn
to know which cues to focus their attention on in their sport environment, and develop an
understanding of the importance of these cues in predicting the nature of future sport
related stimuli.
Support for the software approach to expertise has been repeatedly demonstrated
in studies assessing decision time and accuracy responses for sport-specific situations
(Bard & Fleury, 1976; Starkes, 1987). The same is true for the recognition and recall of
structured elements of game situations in sports such as baseball (Hyllegard, 1991; Shank
& Haywood, 1987), basketball (Allard & Burnett, 1985; Bard & Fleury, 1981), field
hockey (Starkes, 1987), and volleyball (Borgeaud & Abemethy, 1987). Given the vast
support for the software approach, the rest of this section will describe the cognitive

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elements of visual search that provide a better understanding of visual selective attention
capabilities.
Visual Selective Attention
Theeuwes (1994) has defined selective attention as “the process of selecting part
of simultaneous sources of information by enhancing aspects of some stimuli and
suppressing information from others” (p. 94). Visual selective attention theorists are in
agreement that there is primarily a two-stage process of selection: A preattentive stage
and an attentive stage. The preattentive stage is thought to be unlimited in capacity and
occurs in parallel across the visual display. Conversely, the attentive stage is capacity
limited and is serial in nature. Preattentive parallel search has been supported by the
notion that in simple search tasks, a flat function exists relating RT to the number of non¬
target items that are varied (e.g., Egeth, Jonides, & Wall, 1972; Neisser, Novick, & Lazar,
1963). This flat function has been regarded as a pop-out effect (i.e., the non-target items
pop-out of the display) and gives support to the notion that operations are carried out in a
spatially parallel manner. Thus the three properties of preattentive search are unlimited
capacity, independence of strategic control (exogenous, stimulus driven,), and spatial
parallelism at various locations. Attentive search is characterized by functions that show a
linear increase in RT as the number of non-target items increases. It is serial in nature,
usually found in tasks with specific arrangements and in conjunctive search, and is
probably capacity limited.
The specific nature of the attentive stage of visual search has been hotly debated by
theorists who favor the concept of a late selection approach versus those who favor early

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selection. In regard to the notion that the attentive stage is limited in capacity,
disagreement exists in regard to where the capacity is limited. Early selection theorists
(e.g., Theeuwes, 1994; Treisman, 1988; Treisman & Gelade, 1980; Treisman & Sato,
1990) suggest that perceptual operations can be performed during the attentional stage
that cannot be handled by the preattentive stage. Conversely, late selection theorists (e.g.,
Allport, 1980; Duncan, 1980; Duncan & Humphreys, 1989) say that during the attentive
stage, no perceptual operations are completed. Rather they propose that during the
attentive stage, selection of one of the competing response tendencies elicited by the
multiple stimuli occurs.
The idea of a limited spatial location property to attentive search has also been of
issue. Specifically, early selection theorists have suggested that there is serial inspection of
each item; a notion that is in line with several metaphors that have been forwarded to
describe visual selective attention such as the spotlight (Posner, 1980; Treisman, 1988)
and the zoom lens (Treisman & Gormican, 1988) which will be described later. The late
selection theorists, on the other hand, do not allocate a special role to spatial attention.
Different types of search tasks have been used in an attempt to better understand
the covert processes that distinguish the two stages and elements of the stages. The most
popular of these tasks have been those characterized as primitive features and conjunctive
features. In searches involving primitive features, Treisman and her colleagues (e.g.,
Treisman, 1988; Treisman & Gelade, 1980) have provided an abundance of evidence that
these tasks can be carried out preattentively, exhibiting flat search functions which are the
result of the popping-out of the most significant features. In these types of tasks,

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information does not need to be passed to the second stage because it is automatically
selected and there are no attentional limitations.
As mentioned, a special role for spatial attention has been advocated by those in
the early selection camp (e.g., Broadbent, 1982; Hoffman, 1986) to account for findings in
which items with unique attributes have not been shown to pop-out when they were
irrelevant to the task. This notion also contradicts the idea that top-down control
maintains gaze until it comes close to a conspicuous object, and then bottom-up control
takes over (e g., Engel, 1977). Thus, spatial attention may not strictly adhere to the
constraints of other types of primitive search tasks. These concepts support for the zoom
lens metaphor of spatial attention in that people may intentionally vary the distribution of
attention in the visual field (Eriksen & Yeh, 1985). In this case, search for the target
proceeds serially, omitting the need for the preattentive stage period. This is in line with a
series of studies by Eriksen and his colleagues in which it was shown that non-target items
may have a detrimental effect if they are spatially close to the target but have no effect
when they are further away. As will be seen later, the idea that attentive search is serial is
also an important factor in being able to infer that the line of sight coincides with attention.
Conjunctive feature search tends to show a linearly increasing relationship between
the number of different features in the task, and whether the target is absent or present in
the display. According to the early selection account, the reason this occurs is due to the
need for serial search rather than parallel operations only. However, under certain
circumstances such as relatively large displays or search for some particular attributes
(depth, movement), search functions become relatively flat (Pashler, 1987; Wolfe, Cave, &

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Franzel, 1989). These results can all be accounted for however, by the revised FIT which
incorporates some top-down mechanisms in conjunctive search so that non-targets (even
though conjunctive) which are very dissimilar to the target do not have the same
probability of entering into the attentive stage as do those that are similar.
Stages of visual search. As mentioned, the visual search process consists of two
distinct stages (Jonides, 1981). The first of these, the preattentive stage, involves
unlimited capacity in which visual information from sensory receptors is held in a rapidly
decaying visual sensory store. The literal representation of this briefly held information is
labeled "the icon" (Neisser, 1967). This stage of visual search is thought to be automatic,
with parallel processing of information, and demonstrates crude feature analysis or
detection.
The second stage of visual search, termed the focal or attention demanding stage,
refers to the process through which selected items in the iconic store are subjected to a
more detailed analysis (Jonides, 1981: Remington & Pierce, 1984; Yarbus, 1967). The
concept of selective attention in this context focuses on the determination and passage of
specific icons from the preattentive stage to the focal stage. It is in this focal stage that
only those cues (icons) in the sport environment that are deemed pertinent will be attended
to and used by the athlete.
The process of selecting and processing information from only specific aspects of
an entire visual display entails both overt visual orienting and covert mechanisms that
occur during eye fixations. Overt visual orienting includes the movement of the eyes and
head to focus on a particular spatial location. Both top-down (cognitively driven) and

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bottom-up (stimulus driven) processes control the ‘macrostructure’ of the scanpath (Levy-
Schoen, 1981), or where the visual receptors are focused. Covert orienting mechanisms
are unseen processors that occur within the attention allocation resources of the brain and
are also influenced by both top-down and bottom-up control (e.g., Posner & Cohen,
1984).
Temporal aspects. Though covert orienting mechanisms are, by definition hidden,
studies of the covert measures of visual orienting have been reported for the past 20 years
based on the cost-benefit paradigm developed by Posner and Snyder (1975) and Posner
(1978, 1980) to investigate mental chronometry; the time course of information
processing. Much work in this area led to the conclusion that reaction time decreases give
the perceiver a head start in shifting attention to the target’s location. However, questions
arose regarding the effect of location cueing as being related to perceptual sensitivity
changes or changes in the observer’s response criterion. Using SDT paradigms, results
have indicated that the benefit occurs mainly through a change in the perceptual sensitivity
(e.g., Downing, 1988). These results have further been substantiated by overt measures of
mental chronometry.
Specifically, Saitoh and Okazaki (1990) examined the temporal structure of visual
processing while performing a digit string search and matching task in an effort to
decompose the stages of reaction time. The time used to encode and memorize the
standard digit string increased linearly with each addition to the digit string. Also, it was
found that the entire visual search time and RT was associated more with the number of
eye fixations rather than the duration of the fixations. This provides support for the idea

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that each shift of eye fixation provides a shift in visual attention as well and that the ability
to measure the chronometry of information processing can be accomplished through the
study of eye movements.
Though the results obtained by Saitoh and Okazaki (1990) are encouraging, many
questions have been raised regarding the ability to infer visual attention shifts from eye
movements (Klien, 1994; Viviani, 1990). Attempts to clarify this issue have typically
involved determining whether saccadic eye-movements can be made without concomitant
eye-movements to the location. As mentioned, when highly salient aspects of the display
exist, stimulus driven (bottom-up) control takes over (Engel, 1971, 1974, 1977).
Cognitive control (top-down) of the scanpath is most evident when a particular aspect of
the display is of interest. Goal driven visual search strategies are produced on the basis of
cognitive control while stimulus driven responses appear to be elicited by the stimuli
themselves and take on the properties of reflexive shifts to the visual field (Yantis &
Jonides, 1984). Most research has indicated that while there appears to be a close
relationship between stimulus driven saccades and attentional shifts, less convincing
evidence exists for the validity of inferring attentional shifts from goal driven initiation.
Research indicates that in the case of stimulus driven saccades, the shift of
attention occurs before the initiation of the saccade (Wright & Ward, 1994). In their work
looking at express saccades, Fischer and Weber (1993) have shown that attention must
first be disengaged from the fixation point at the origin prior to target onset. Posner
accounted for these criticisms through an elaborative account of the disengage, shift, re¬
engage sequence that is probably mediated by activity in the posterior parietal cortex, the

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superior colliculus, and the pulvinar region of the thalamus (Posner, Peterson, Fox, &
Raichle, 1988). However, even in this description, most data were gathered from stimulus
driven rather than goal driven attentional shifts. An in-depth discussion of the benefits and
criticisms directed toward inferring attentional processing from eye-movement recording
devices will be provided in the following sections of the review.
Metaphors of Visual Attention
Though overt mechanisms of visual selective attention are relatively simple to
observe, covert attentional shifts are much more difficult to ascertain. As a result, much
debate surrounds the ability to infer cognitive processing from overt observations. Due to
the inability to precisely describe the association between line of fixation and attentional
processing, several different models have been posed to account for the psychological
mechanisms underlying attentional shifts. First, movement models suggest that the focus
of attention is shifted from one location to another in an analog or discrete manner (the
spotlight metaphor, e.g., Posner, 1980). Another popular metaphor is focusing models
which suggest that attentional focus can change from a broader, more diffuse state, then
back to a finer, more concentrated state at the destination of the shift (the zoom lens idea,
Eriksen & St. James, 1986). Finally, resource distribution models postulate an attentional
alignment process that does not involve a movement or a focusing component (Laberge &
Brown, 1989). Investigations of each of these models have provided data to support
them. However, as will be addressed later, Wright and Ward (1994) suggest that the
reason for many discrepancies is the use of a variety of experimental paradigms, tasks, and
cueing mechanisms.

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The primary question that arises from the debate is whether or not the line of sight
is independent of selective attention shifts. The evidence described so far in reference to
the stages of processing has been gleaned primarily from studies in which line of sight is
inferred from RT and other indirect measures of fixation location. However, much
research has been completed with eye-movement recording devices to determine precisely
when and where attention shifts during information processing of visual stimuli.
Eve-Movement Recording
The ability to infer attentional shifts from eye movements was first investigated by
Helmholtz in the 19th century when he discovered that he could shift his point of gaze to
illuminated letters before his actual attention shifted there (the latency of a normal saccade
is approximately 220 ms (Fischer & Weber, 1993). James (1890) described attentional
shifts as being under involuntary or voluntary control which was the genesis for the study
of exogenous (bottom-up) versus endogenous (top-down) processing. However, much
research in the area was not possible until the 1970’s with the advent of sophisticated eye
monitoring equipment. Even with the additional data acquired through eye movement
recording devices, researchers have been unable to provide indisputable evidence for the
notion that the line of sight coincides with the line of attention.
While the visual search paradigm would appear to be a fruitful means of assessing
selective attention strategies, it is not without criticism. Before concluding this section on
visual search, it is necessary to discuss some of the limitations and potential problems that
currently exist in eye movement recording research. These concerns are reflected in both

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the assumptions of selective visual attention theory, and in the eye movement recording
techniques themselves (Abemethy, 1988; Viviani, 1990).
According to Abemethy (1988), the first major limitation of eye movement
recording lies in the assumption that visual search orientation is reflective of actual
allocation of attention. That is, visual fixation and attention are one in the same (where
one looks is where one attends). This notion, however, has been refuted by Remington
(1980) and Remington and Pierce (1984), who demonstrated that attention can be
allocated to areas other than the foveal fixation point. Indeed, attention can be allocated
to areas in peripheral vision, a mode that cannot be measured with current visual search
equipment (Buckholz, Martinelli, & Hewey, 1993; Davids, 1987).
A second limitation of current visual search recording involves the high trial-to-
trial variability that is evident in the literature (Abemethy, 1988). These variable patterns
make reliable conclusions about the relevance of specific visual cues difficult. Related to
this limitation is the fact that the majority of studies include relatively low sample sizes
(often n = 6 or 8), thus causing internal and external validity concerns.
A third, and perhaps most important, limitation of eye movement recording
focuses on the issue of visual orientation and information pick-up. As Abemethy (1988)
notes, merely "looking" at visual information does not necessarily equate with "seeing" (or
comprehending) this information. Thus, a person may fixate upon pertinent cues in the
visual array, ¡but there is no guarantee that he or she is actually attending to or utilizing
these cues. In order to empirically determine whether one is actually "picking-up" and
using the cues available in the visual field, the technique of cue occlusion has been used.

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Like Abemethy, Viviani (1990) addresses many criticisms directed toward the use
of eye movement recording devices and the study of visual search to understand
underlying cognitions. The main question he poses is, “What can we learn from eye
movement data about the perceptual and cognitive processes involved in the exploration
of the visual world?” (p.353). Viviani suggests that the use of eye movement research
should be a good paradigm based on the following factors: (1) the instrumentation and
procedures have become standardized, (2) the information processing approach supplies
the intellectual scaffolding for the research, and (3) the central dogma provides much
motivation for work in the area. The ‘central dogma’ which Viviani refers to is the notion
that “exploratory eye movements can, at the very least be considered as tags or
experimentally accessible quantities that scientists can observe to understand underlying
processes of cognition.” (p. 354). Viviani goes on to point out three primary reasons why
the dogma may not be accurate and cites several lines of research that point to
independence between eye movements and cognitive activities (Fisher, Karsh,
Breitenbach, & Barnette, 1983; Teichner, Lemaster, & Kinney, 1981)
First of all, according to Viviani (1990), it is obvious that eye movements move in
sequential order, representing strictly serial behavior. Therefore, to posit a close
connection or dependence between eye movements and cognition, one must assume that
the behavior viewed is unfolding in sequential order. This, however, is not the case for all
activities as is suggested by parallel processing models (e.g., Rumelhart & McClelland,
1986). The central dogma would be valid of it was known that a given process unfolds
sequentially. However, it is false whenever several concurrent processes can be

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suspected, unless a theory is developed that describes how eye movements reflect these
processes.
Second, similar to Abemethy (1988), Viviani (1990) argues that there is little
evidence to suggest that attention coincides with the line of site. He uses examples such
as Posner and Cohen’s (1984) and Posner’s (1980) work to support the notion that under
appropriate pre-cueing conditions, visual attention can be directed almost anywhere in the
visual field, regardless of the line of sight. He also criticizes the use of static skills in the
sense that most laboratory tasks may not even produce results that are useful in predicting
eye movements during viewing of such dynamic situations as in a soccer game.
Third, Viviani (1990) says that even if the previous two statements can be assumed
to be true, it is difficult to identify the conditions in which it is proper to assume that the
sequence of operations actually conveys information. The problem with this point is that
information is not in the visual field until the image is able to eliminate a prior uncertainty.
Thus, the amount of information required depends on the probabilities of various
alternatives. Support has been found for the notion that eye movements tend to cluster
around areas of high informativeness (Antes, 1974: based on fixation clusters around
comers) and this can be taken to support the central dogma. However, Viviani disagrees
with this viewpoint, arguing that the most informative areas of the display are not always
those that are most salient.
According to Viviani (1990), three cognitive operations are inescapable when
exploring the world to solve a problem. These include (1) activation of a set of a priori
beliefs about the possible states of the world, (2) breaking up the complex, holistic

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hypothesis that normally regulates interactions with the world into hierarchy of simpler
alternatives, and (3) translating these alternatives into a sequence of locations in visual
space that will likely disambiguate each alternative. Though these criteria appear to be
valid primarily in situations where eye movements are information driven, goal-directed
behaviors, rather than simple stimulus driven percepts. If in fact, the specific search path
is stimulus driven rather than goal driven, much of Viviani’s arguments can be invalidated.
Most visual search researchers will agree that there are three processes that occur
within the 300 ms of a typical fixation. These include an analysis of the stimulus in the
visual field, a sampling of the periphery, and a planning of the next saccade. Of these
three processes, analysis of the stimulus in the visual field is the most important activity
during the fixation and the duration is said to reflect the load of cognitive processing (the
Process-Monitoring idea: Just & Carpenter, 1980). The next saccade cannot be initiated
until the information has been processed from the last one (Vaughan, 1982). A strong
body of knowledge regarding reading fixations has been developed through the work of
Just and Carpenter (1976, 1980). These studies have tended to show support for the
Process-Monitoring idea. However, there is some evidence that the Process-Monitoring
idea may not be accurate based on the finding that near normal reading remains possible
even when the experimenter controls reading rate (Potter, Kroll, & Harris, 1980).
Other evidence that the line of sight coincides, at least somewhat, with the shift of
visual attention, is provided by investigations of the buffer capabilities of the brain and
whether, in fact, buffering is a method by which visual information is coordinated and
analyzed. As Potter (1983) suggests, the beneficial effects of imposing buffers between

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the eye and the mind would be paid for in terms of an ability to infer mental events from
eye movement data, as these buffers would decouple the two processes. In fact, the very
purpose of the buffers would be to introduce some degree of uncoupling between stimuli
and their central effects (Potter, 1983). Potter has proposed the existence of an
integrative visual buffer where information from successive fixations is pasted together to
provide a coherent alignment of individual fixations, but has found little support for it.
Also, O’Regan and Levy-Schoen (1983) believe that the coding of information from the
retina may be more semantic rather than analog but, like Potter’s work, evidence for this
idea has not surfaced.
Scanpath recording sequences have also contributed some support for the central
dogma. Jacobs (1986) and others have provided evidence that each saccade brings the eye
to a zone where new information can be gathered. However, once again, most evidence
from scan path observations can only be used as support for the stimulus driven properties
of eye movements.
Based on the evidence provided on both sides of the argument, it appears that
weaker versions (i.e., those that do not assume direct relationships, but some sort of
association between eye movement and attention) of the central dogma stand a better
chance of being upheld than does the strong one (Viviani, 1990). Yarbus’s (1967)
suggestion that eye movements reflect human thought processes was immediately
embraced. However, this was based on purely stimulus driven information cues. Antes
(1974) has postulated a weaker version of the dogma, stating that there may be a
relationship between the distribution of fixations and the informativeness of a scene. As

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Viviani pointed out, however, the informativeness of a scene is difficult to quantify. Once
again, reaching beyond the stimulus driven nature of eye movement research is difficult
based on the informativeness dilemma.
Though the debate between those who accept the central dogma and those who do
not continues and remains unresolved, much research has been conducted to examine
fixation patterns of participants in a variety of experimental tasks. In the following
sections, some of these studies will be elaborated, especially those concerned with
examining fixation patterns of
those in driving and dynamic sport-related activities that require quick decisions and rapid
attentional shifts.
Visual Attention and Driving
Of all the psychomotor skills that have been researched in terms of the role of
visual selective and divided attention, perhaps the one that is most globally relevant and
that demands constant monitoring of both central and peripheral information is driving an
automobile. Many studies have been undertaken on the role of attention and the visual
processing that influences the ability of drivers to consistently process task-relevant
information and ignore interrupting or distracting stimuli in order to maneuver and
maintain control over motor vehicles. The focus of this section of the review will be to
summarize a variety of relevant literature that has been dedicated to understanding the
driver as an information processor. Also, a rationale for the investigation of peripheral
narrowing in the driving context will be provided.

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While driving (as with other psychomotor tasks) there is a limited capacity of
attentional resources that can be devoted to an almost infinite number of stimuli at any
point of time. The mind has adapted in a way that many functions of the driving task are
accomplished virtually automatically (some more than others) such that in many cases
drivers can transport themselves from one place to another without even remembering
how they got there or what critical pieces of information they may have noticed along the
way. However, as the task of driving becomes more complex due to decreased visibility,
bad weather, heavy traffic, mechanical malfunction, sudden unexpected obstacles, fatigue,
and other factors, the automaticity of driving becomes less instinctive and demands more
attentional resources. In these conditions, drivers may experience information overload
and may be more likely to place themselves in possibly risky situations.
During normal driving, the driver tends to focus on the central task of keeping the
vehicle “on the straight and narrow” so to speak, maintaining control of the vehicle based
on the constraints of the driving environment (e.g., speed limits, lane markers, etc.).
However, when an object or event that is not in the central (or foveal) field of vision, the
eyes are normally moved from the central task to focus more directly on the information
that has been attended to in the periphery. Based on the information provided by the
newly attended stimulus, a decision must be made regarding whether or not to change
driving behavior. These alterations occur both in serial and in parallel depending on the
specific situation presented (Schneider & Shiffrin, 1977; Shiffrin & Schneider, 1977). To
make matters more complicated, all of these processes are often limited by extremely
restrictive temporal constraints (Shinar, 1978).

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Level and Distribution of Attention
It has been estimated that approximately 45% of all driving accidents could have
been prevented if the driver had attended to critical events before the accident occurrence
(Shinar, 1978). Thus, it is obvious that the need to pay attention to critical signs, speed
limits, obstacles, and the like, is central to operating a motor vehicle effectively and safely.
Shinar (1978) suggests that the two primary characteristics of attention that influence an
ability to perform the driving task effectively are the level and distribution of attention.
Level of attention. During normal driving, the level or degree of attention that is
distributed to the task is a function of the external environment and its demands and one’s
internal state of arousal. In situations that are not demanding (e.g., when there is little
traffic, weather conditions are ideal, and no time constraints), the primary goal of the
driving task is to remain at an activation level high enough that the proper amount of
attention is devoted to the task. However, it appears that often this may be more difficult
than it seems as is evidenced by the number of incidences in which drivers fail to attend to
critical cues and, in extreme cases, actually doze while driving.
The opposite is the case when demands are excessive such as when driving
conditions are highly variable (e.g., curvy roads, rain, fog), traffic is heavy, and the driver
is in a hurry, trying to reach the destination point in a limited amount of time. In cases
such as these, the goal is to remain at a relatively lower and stable level of activation to be
able to respond in an effective manner to the demands of the task. Thus, the amount of
attention that must be allocated to driving is a direct function of the demands placed on
drivers in the particular environments in which they are placed. In accordance with this

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view, studies have shown that, in normal driving conditions where demands are few, spare
attentional capacity to perform other tasks, irrelevant to the driving situation, exists (e.g.,
Brown, 1965; Robinson, 1975). A clear example of this ability is being able to carry on
in-depth conversations with passengers, even to the extent of being able to look at the
other person for an extended period of time, while maintaining satisfactory driving
parameters. In this way, the driver can be seen as an information processor who attempts
to reduce the processing demands of the driving task to allocate attentional resources to
other, more interesting tasks (Shinar, 1978).
The other component of the information processing demands placed on the driver
is the amount of risk the driver is willing to tolerate while diverting attention from the
central driving task. Research has indicated that the content of a particular road sign has a
an influence on whether or not it is recalled, and recall depends on the relevance of the
sign to the perception of risk involved in discarding the message. Johanssen and Rumar
(1966) found that, in general, the perception of road signs is quite low, but that signs such
as speed limit signs were more readily recalled than other signs (such as pedestrian
crossing and other general warning signs). In order to recall a speed limit sign or a general
warning sign, the meaning of the sign must first be encoded and interpreted. However,
when asked to recall the signs passed, Johanssen and Rumar (1966) found that warning
signs were not recalled near as often as were speed limit signs. These results suggest that
after perceiving the sign and perceiving it as unimportant, the content of the sign is
possibly immediately discarded in favor of more relevant signs. Thus, the amount of

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processing allocated to the driving task appears to be a function of the subjective
importance of the signs.
Shinar (1978) suggests that the spare capacity that is not used under normal
driving is reserved to deal with other more demanding situations. A good example of this
is passing another vehicle on a two-lane roadway. As Shinar explains, “the release of
tension upon completion of the pass can actually be felt” (p. 75). Therefore, it appears
that this is an adaptive function and the ability to adapt is facilitated by experience.
Distribution of attention. The other important aspect of attention that influences
driving performance is the distribution of attention. As mentioned, though a variety of
different analogies have been proposed to explain the breadth of attention, some of the
more popular views include the spotlight view (Posner, 1980), and the zoom lens
metaphor (Eriksen & St. James, 1986; Erikson & Yeh, 1985).
Regardless of the view of attention that is adopted, researchers would agree that
attention can be selectively moved from one spot to another, giving rise to two important
concepts when discussing the distribution of attention: Divided attention and selective
attention. Divided attention refers to the ability to allocate resources to various objects of
interest while selective attention refers to our ability to select the single most important
aspects or objects of the display and direct our attention to those areas while ignoring any
other competing stimuli (e.g., Cherry, 1953). As is evident, both divided and selective
attention are important in the driving environment. While people may choose to
selectively focus most attentional resources on the central task of driving, resources must
be allocated to the periphery in order to perceive input that is critical to driving the car

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effectively arid minimizing accidents. Correspondingly, researchers have shown, using
both visual and auditory selective attention tasks, that there is a limit to the information
processing resources that exists beyond the sensory level, in which both inputs compete
for the same limited resources (Kahneman, 1973; Kahneman, Ben-Ishai, & Lotan, 1973;
Mihal & Barrett, 1976). With experience, the ability to divide attention increases due to
the automaticity of the driving task itself, leaving more resources available to deal with
competing stimuli (Logan, 1992).
Visual Search and Driving
Driving capabilities are influenced to a great degree by how well the driver’s vision
is adapted to the task. Much confusion exists regarding the type of visual capabilities that
should be required for licensure, and the confusion primarily surrounds the issue of
dynamic versus static visual acuity. Although most driving tests are strictly static in
nature, the ability to navigate effectively and safely down the road is dependent upon not
only static visual acuity but also dynamic visual acuity in both the central and peripheral
visual fields (Shinar, 1978). This may explain why most studies that have examined the
correlation of static visual acuity to driving performance have yielded rather inconclusive
data with regard to this relationship. On the other hand, researchers who have assessed
the relationship of dynamic visual acuity to accident rates have shown (though admittedly
not consistently) dynamic visual acuity to be more related to performance than traditional
static measures. Along these lines, drivers who are slower in responding to peripheral
targets tend to be more likely involved in accidents, and these accidents tend to be more
right angle accidents rather than rear end or head on collisions (Shinar, Mayer, & Treat,

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1975). Consequently, research efforts are being directed toward understanding, more
specifically the type of visual acuity that is most relevant to driving performance rather
than simple static visual acuity.
Early research. In addition to issues of visual acuity, further questions exist
regarding the ability to selectively attend to and utilize the most critical cues in the driving
environment and the ability of drivers to shift attention to both focal and peripheral areas
of the display. One way to assess these factors is through learning more about the visual
search patterns exhibited by drivers. Though there are inherent limitations in drawing
conclusions about visual attention from simply analyzing visual search patterns, most
researchers would agree that even if the “central dogma” is only partially true, much can
be learned from observing eye movements and inferring cognitive processes from these
observations.
With respect to driving, questions arise such as: Where does the driver tend to
focus attention most of the time? What are the informative and critical cues that must be
attended to drive effectively? How can we maximize the information gained from fixating
on the most informative and relevant cues? Shinar (1978) suggests that both internal and
external mechanisms govern how visual selective attention operates in the driving
environment. External factors are crucial when something in the periphery is deemed
worthy of a fixation to the peripheral area while internal factors refer to our expectations
of the driving stimuli with regard to where the most informative cues will occur. Though
Shinar (1978) admits that the eye-mind connection may not always be true, he also

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suggests that it is appropriate under stressful driving situations in which the attentional
resources of the driver become more restricted and processing is more serial in nature.
In order to examine, more closely, the relationship between eye movements and
driving performance, one must first understand how to describe the driver’s visual world.
The visual display of the driver can be described in terms of the degrees from a chosen
center of the display. The point chosen is usually the focus of expansion, the area where
the two edge lines of the road appear to converge and the point at which the road appears
to expand outward from the center. Rockwell (1972), after almost a decade of study of
the eye movements of drivers, concluded that approximately 90% of all fixations while
driving occur within + 4 degrees from the focus of expansion and that most eye fixations
last between 100 and 350 ms in duration. Thus, it appears that drivers adopt an adaptive
strategy in which information appearing in the roadway can be identified and processed
with the greatest speed. Of secondary importance, however, is that the driver must rely
on peripheral vision to account for potentially critical stimuli that do occur in the
periphery.
A second way to account for driver’s eye movement behavior is through
assessment of the time spent viewing various stimuli that appear in the visual display. For
example, Mourant, Rockwell, and Rackoff (1969) found that, during open road driving,
50% of the time was spent looking straight ahead, 20-27% was spent looking at scenery
to the right and left of the car, and the rest of the time was spent looking at other vehicles,
bridges, and the like. Interestingly, only about 2% of the time was spent looking at lane
markers or to areas of the road surface near the car. Thus, it appears that much

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information such as lane position and direction is assessed through peripheral vision. In
driving situations where the demands are higher, such as when following another car, the
location of visual fixation as well as the compactness of the fixation area changes
significantly. In general, fixations become closer to the car and more compact, and fewer
fixations are directed away from the roadway when following another car (Mourant et al.,
1969).
Another variable that has been investigated with respect to eye movements is the
actual geometry of the roadway. Research has indicated that visual search patterns are
different on curved roads rather than on straight-aways due to the change in the focus of
expansion (Shinar, McDowell, & Rockwell, 1977). Specifically, it appears that when
negotiating a curved roadway, subjects tend to fixate back and forth between the edge
markings immediately in front of the car, and the end of the road ahead. This could be due
to the fact that on a curved road the focus of expansion is somewhere off the road way
itself, and also due to the added importance of lane markings due to the necessity of
making larger steering corrections to keep the car in the lane. These results prompted
Shinar (1978) to suggest that “The drivers information processing capacity is severely
limited so that, when under stress, at any one time the driver can attend to either the
directional cues (end of the road up ahead) or the lateral positioning cues (edge markings
close to the car) but not to both simultaneously” (p. 91). One can assume that by
“attend”, Shinar is referring to focal vision rather than peripheral input.
Another factor that can influence where drivers attend is their experience level.
Though we consider the act of driving a car a relatively easy task due to the amount of

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practice that most adults have with it, there are approximately 1500 perceptual motor
tasks that must be mastered in order to drive an automobile safely on the roadway (Shinar,
1978). The importance of mastering mechanical skills such as accelerating, braking,
shifting gears, and others, is readily obvious. However, perhaps a more subtle group of
skills that must also be mastered includes perceptual skills (i.e., information gathering
skills). Though these skills, like the mechanical ones, become automated with practice,
research by Mourant and Rockwell (1972) suggests that the “looking” part of the
perceptual motor tasks must also be learned.
Research has shown that in general, novice drivers tend to miss significantly more
signs, are involved in more accidents due to improper directional control, sample their
mirrors less, and look closer to the front of the vehicle, when compared to experienced
drivers (Shinar, McDonald, & Treat, 1977; Summala & Naatanen, 1974). This
ineffectiveness of the visual search strategy employed by novice drivers is partly due to
their inability to use peripheral vision efficiently, thus requiring constant monitoring of lane
position and closer fixation of focal vision to the car than is the case for experienced
drivers. The lack of ability of novices to use peripheral vision effectively, may, in fact be
due to the highly stressful nature of the driving task for them, and the resulting narrowing
and/or distractibility in the visual field.
Experience may not be the best predictor of accident rates; however, when
considering results of Williams and O’Neill (1974) who found that experienced race car
drivers are involved with more accidents and violations than are “normal” drivers.
However, this may be due to the perception of risk and the change associated with

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experience such that many of the benefits accrued from experience are offset by risky
behavior that is deemed appropriate (Naatanen & Summala, 1976).
Recent research. Recent research has been directed toward understanding more
fully the ability of drivers to extract meaningful information from targets along the
roadway. In particular, many studies have been done on the demands of the external
environment while driving, such as the perception and processing of road signs in order to
aid road developers in facilitating the identification of signs, and, thereby, reducing the
number of accidents due to lack of perception and good decision making.
Hughes and Cole (1988) investigated the effect of attentional demands on eye
movement behavior during simulated road driving. They attempted to assess how a
driver’s performance was effected by purposely directing attention to particular features of
the road environment. Their research was based on the early work of Johansson and
Backhand (1970) who determined that traffic signs were more consistently remembered by
subjects who had been cued to look for them than others who were not. The experimental
design was one in which participants were assigned to four treatment levels: Free,
memory, attention, and search. Those in the free condition were simply asked to watch
the driving film. Those in the memory condition were informed that they would be asked
questions at the conclusion of the film, but were told nothing about the content of the
questions. The attention group was advised that it would be asked to identify all objects
encountered in the visual display during the course of the film. Finally, the search group
was told to explicitly report all road traffic control targets and any experimental discs that
were placed along the roadside.

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Half of the subjects in each condition performed the filmed observation task while
performing a pursuit rotor task in the center of the display. The others simply watched the
display without having to perform the dual task. Results showed that across groups, 25%
of the fixations were located at the actual focus of expansion while 80% of the remaining
fixations were centered within 6 degrees of the focus of expansion. Therefore, results
suggest that if road signs are located beyond the 6° point in the display, they will probably
not be perceived. Also, increasing task specificity (i.e., moving from the free condition to
the search condition) resulted in more fixations to the left part of the display (the area
where most signs were posted) with a corresponding decrease in fixations to the center of
the display. Furthermore, the addition of the dual task paradigm resulted in two
predominant effects on eye movements. First, eye fixations tended to move closer to the
central region (where the pursuit rotor task was located). Second, the distance of
peripheral fixation also moved closer to the focus of expansion.
Therefore, it can be concluded that in the dual task condition which requires
increased attentional resources, there is insufficient spare resources to perform the
tracking task without more fixation resources. This effect was hypothesized to arise from
the need to make visual inquiry into the region of the tracking task. Also, pursuant to the
second major finding in the dual task condition, the additional demand of the secondary
task not only necessitates more fixations to the region of the task, but also reduces the
extent to which the rest of the visual display is searched. Though not suggested by the
researchers, these results could be accounted for in the context of attentional narrowing
and/or distraction.

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A similar study was conducted by Luoma (1988) to examine the types of roadway
landmarks that are perceived and remembered better than others. As may be evident from
the results of Hughes and Cole (1988) in the previous paragraph, and others (e.g., Drory
& Shinar, 1982; Johansson & Backlund, 1970), drivers do not perceive nearly all of the
traffic signs that they encounter, even in situations where they have been precued to look
for the signs. In situations requiring increasing demand on the driving task, the perception
of signs is even less than in “normal” driving conditions.
Luoma (1988) tested the idea that the more casual the perception or the larger the
target signs, peripheral vision is used to a greater extent. However, an important function
of peripheral vision is to identify targets of importance to the driving task and, if the
situations warrants, direct focal vision to the sign. Another consideration is that even
when focal vision is directed toward the sign, perception and further processing probably
does not occur. To investigate these ideas, participants actually drove a 50 km route
while outfitted in eye movement monitoring equipment. Along the route, they were asked
to identify targets as they were passed and to report the essential content of each of the
targets. If the participant only identified the target but was unable to report the content of
the sign, it was not recorded as a correct perception.
Results indicated that correct perception only occurred, for the most part, when
the target was fixated foveally. Also, whether the sign was perceived or not depended
heavily upon the relevance of the sign to the driving task. For example, 100% of all speed
limit targets were perceived foveally and were recalled while signs such as pedestrian
crossings, roadside advertisements, and houses were perceived much less if at all. In fact,

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no subjects recalled passing “pedestrian crossing” signs even though 25% of them fixated
on it. Fixation duration was also recorded and these data suggested that in cases where
the fixation time was extremely short, perception was not evident. Luoma (1988)
interpreted this finding as suggestive that drivers did not even make an attempt to actually
process the information. In general, results of previous studies were replicated in that
overall, sign perception was rather poor. Furthermore, it appears that the processing
devoted toward identifying the signs was dependent upon the relevance of the sign to the
actual driving task and its informativeness.
Perhaps the most relevant study reported to date to examine the processing of
visual stimuli in both central and peripheral fields was conducted by Miura (1990). The
primary purpose was to assess changes in the useful field of view (UFOV) under situations
of varying task demands and to determine the corresponding variation in the acquisition of
visual information that accompanied these changes. The useful field of view can be
superficially conceptualized as the information gathering area of the visual display.
Mackworth (1976) has suggested that the UFOV will vary with changes in the situational
characteristics or specific demands of the environment. Furthermore, Menz and Groner
(1984) have suggested that the specific components that influence the UFOV are the
width of processing and the depth of processing of visual information. Along these lines,
Miura (1986) has shown that mean gaze duration becomes shorter in situations where
demands increase, providing the impetus for the idea that the UFOV size shrinks under
situations of high demand and the basis of this study.

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The study was conducted under actual driving conditions in which the subject had
to navigate along a roadway, in daylight conditions. Peripheral targets were presented at
random at a distance of about 55 cm and varied in eccentricities depending on the point of
fixation at the time of target presentation. The mean distance of eccentricity across all
trials was approximately 13.6°. The dependent measures of interest were the RT to the
peripheral stimulus and the distance between the target and the point of fixation at the
moment of the participant’s of response. The latter of the two has been shown to indicate
the size of the UFOV. Both measures have been suggested to be the primary indices of
peripheral visual performance (Miura, 1985). Five conditions were created varying the
demands of the driving task accordingly. They consisted of: (1) sitting in a drivers seat in
a stationary car, (2) driving on a low crowded road (LCR), (3) an expressway condition
(EW), (4) a moderately crowded road condition (MCR), and (5) a highly crowded road
condition (HCR). Driving demands were expected to increase correspondingly from the
control (sitting in the stationary car) condition to the HCR condition.
Results showed that RT to the peripheral lights increased as the situational
demands increased. Furthermore, response eccentricity became shorter, suggesting that
the fixation must occur closer to the actual target location to acquire the necessary
information. In general, this suggests that peripheral visual performance is impeded by an
increase in situational demands. Specifically, it appears that the UFOV narrows at each
fixation point, and the latency of each fixation lengthens. Miura (1990) also suggests that
it is the temporal density of acquiring and processing information related the demands and
not the speed of the driving itself that is responsible for the decrement in performance.

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Furthermore, the detection of targets requires a greater number of eye movements in more
demanding driving situations.
Miura (1990) offered two possible explanations for the decrease in peripheral
performance as central demands increased. Based on the findings from an earlier work
(Miura, 1987), he postulated that the depth of processing of an object in focus increases as
the situational demands increase. Specifically, the latency period of the eye movements
following fixation on a target lengthens as the demands increase. In more demanding
situations, when a narrower UFOV exists, information pickup at the fixation point appears
to be slower, causing a delay in the attentional switching capabilities of the driver. Other
evidence (Miura, 1985) indicates that with lower demands, the fixation points shift to the
inner area of the UFOV while during highly demanding situations, fixations shift toward
the outer part of the UFOV. Thus, as a result of the deeper processing that occurs at each
fixation point, participants attempt to acquire information more efficiently in the periphery
while using a smaller UFOV. Another hypothesis is that as demands increase, they
develop a stronger tendency to search for information in the periphery, a phenomenon
referred to as “cognitive momentum”, and a possible adaptation of the system to utilize
attentional resources in the most efficient manner to deal with the increase in demands
(Miura, 1986).
Though interesting and conceptually valuable, Miura’s (1985, 1986, 1987, 1990)
work fails to take into account what might be a primary influence on the decrement in
peripheral performance and the apparent narrowing of the UFOV. Though not mentioned
in any of his papers, a possible explanation for these findings can be attributed to the

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increase in arousal and anxiety that accompanies tasks that increase in complexity and
demands (Easterbrook, 1959). Although eye movements have been recorded in a variety
of real world and simulated driving situations, researchers have not attempted to examine
other, affective inputs to the system that may account for differences in performance.
Furthermore, in Miura’s (1990) study, as well as others, performance in the central driving
task was not recorded.
Perhaps the driving quality of drivers varies based on the task demands, and
changes differently than does performance in peripheral tasks. The resource allocation
principle (e.g., Kahneman, 1973) would suggest that indeed this may be the case, but it
has not been specifically investigated. More importantly, virtually no research has been
directed toward assessing visual search and cue utilization in the context of high speed
race car driving. Consequently, a large gap exists in the ability to generalize results
obtained from studies of cognitive skills and closed sport skills to these high speed,
dynamic, reactive settings.
Like normal driving, the sport of auto racing demands the coordination of an
extensive repertoire of perceptual and motor skills. However, the performance difficulty
of these skills is significantly compounded by the competitive nature of the sport. In
addition to mastering typical driving skills, the shear speed of the car requires split-second
decision-making and intense concentration on the most relevant cues. An ill-advised
momentary attentional shift or distraction can be (and often is) catastrophic under these
circumstances. Unfortunately, virtually no attempt has been made to empirically assess
these factors in auto racing or in “traditional” sport settings.

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Visual Attention and Sport
Though but few studies have been completed in the context of auto racing, other
activities have provided excellent contexts in which to test the assumptions of the
information processing model and its attentional limitations. For example, in fastball
sports such as baseball and tennis, athletes often have as little as 200-300 ms in which to
make decisions based on the intended direction and speed of the approaching ball
(Hyllegard, 1991; Slater-Hammel & Stumpner, 1950, 1951), as well as to organize and
execute the most appropriate movement to effectively coincide with the object in motion.
Similarly, when driving a race car at 200+ mph, temporal constraints require extremely
quick decision time and accuracy.
Visual Search in Sport
In recent years, the visual search activity of athletes has been examined as a means
of discriminating performance expertise in sport tasks (e.g., Abemethy, 1990; Goulet,
Bard, & Fleuiy, 1989; Helsen & Pauwels, 1990; Shank & Haywood, 1987; Williams,
Davids, Burwitz, & Williams, 1994). Specifically, researchers have attempted to outline
the mechanisms and patterns that differentiate experts and novices in their ability to
perceive and use vital information in the sport environment. The majority of research
performed in this area has been concerned with the relationship between visual search and
selective attention, and with the influence of these processes on decision making strategies
and eventual performance (Helsen & Pauwels, 1992, 1993).
Visual search applied to sport. Based on the models of visual attention proposed
by Neisser (1967) and Yarbus (1967), researchers interested in the selective attention of

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athletes of differing skill levels began to focus on the visual search patterns of athletes in
applied externally-paced sport task situations in the mid-1970's (e.g., Bard & Fleury,
1976, 1981; Bard, Fleury, & Carriere, 1975; Ripoll, 1984, 1988). It was the contention in
these studies that implications of the allocation of focal attention could be determined by
examining the locations and durations of ocular fixation patterns. These patterns were
assessed with eye-movement recording devices that measured ocular fixation through a
corneal reflection technique (e.g., Bard & Fleury, 1981) while athletes viewed slides or
videotape of particular sport situations.
Upon reviewing the sport-specific literature on selective attention and visual
search, it is clear that differences exist between expert and novice performers (Abemethy,
1988). Visual search patterns of expert performers differ from novice performers in a
wide variety of sports, including baseball (Bahill & LaRitz, 1984; Shank & Haywood,
1987), basketball (Vickers, 1996), fencing (Bard, Guezennec, & Papin, 1980), golf
(Vickers, 1992), gymnastics judging (Bard, Fleury, Carriere, & Halle, 1980; Vickers,
1988), ice hockey (Bard & Fleury, 1981), soccer (Tyldesley, Bootsma, & Bomhoff, 1982;
Williams et al., 1993, 1994), table tennis (Ripoll & Fleurance, 1985), tennis (Goulet, Bard,
& Fleury, 1989; Singer, Cauraugh, Chen, Steinberg, & Frehlich, 1996), and volleyball
(Ripoll, 1988; Sandu, 1982).
In the majority of these studies, experts required fewer fixations to achieve
successful response outcomes, and exhibited lower search rates for sport-specific tasks.
Also, experts made a greater number of fixations to the pertinent cues in the visual array
than did novices, and had search rates that were (in the majority of studies) more

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systematic and consistent than was the case with beginners (Abemethy, 1988). From
these results, it would appear that the success of an expert's performance in anticipating,
decision-making, and reacting is determined in part by where they look in the sport
environment. That is, experts seem to have a more efficient visual search pattern in which
they attend to only the most important aspects of the sport situation in which accurate and
rapid perceptions and actions are required.
Visual search and gaze control. In addition to the visual search process, research
on gaze control mechanisms of athletes has been conducted (Bahill & LaRitz, 1984;
Vickers, 1992). Specifically, four types of basic eye movements have been defined. These
include saccadic eye movements, used to rapidly scan from one fixation point to the next;
vestibulo-ocular eye movements, used to maintain fixation to a target when the head is in
motion; vergence eye movements, used to determine the distance between objects; and
smooth-pursuit eye movements, used when tracking a moving object (Bahill & LaRitz,
1984).
Noting gaze control differences in a study of baseball batters, Bahill and LaRitz
(1984) found that better hitters used faster smooth-pursuit eye movements, generated
more anticipatory saccades, and had a greater ability to suppress the vestibulo-ocular
reflex. Observing similar differences in an analysis of the putting stroke used by expert
and novice golfers, Vickers (1992) reported that low handicap golfers (experts) possessed
an economy of gaze allocation when compared to high handicap golfers (novices).
Specifically, experts made more express saccades, had quicker saccades between gaze
locations, and demonstrated greater fixation durations to the ball and target.

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Taken together, visual search and gaze control research indicates that expert
athletes have a highly systematic and selective process of focusing their attention, such
that they know what to view, and when, in the visual field to glean the most informative
cues. They make the most efficient and appropriate ocular movements to achieve this
information pick-up. It can be assumed that experienced drivers in motor sports exhibit
somewhat consistent eye movement patterns and tendencies which allow them to operate
extremely fast race cars safely and effectively. As mentioned, very limited research has
been directed to the examination of any psychological phenomena with race car drivers
and none has been done to investigating driver’s eye movements or other attentional
parameters that are critical to high performance in the fastest sport in the world. The
selective and divided attention demands of race car driving render it an ideal task and
environment to investigate attentional mechanisms and the eye-movement parameters that
underlie those mechanisms. Perhaps the first step that should be taken to better
understand the attentional capabilities necessary for effective race car operation is to
evaluate the visual search patterns of drivers as they navigate the race course. By
evaluating these parameters, it may be possible to assess whether the “software”
advantages that appear to predispose athletes in other sports to reach higher levels of
achievement are valid antecedents to high performance auto racing.
Summary and Future Directions
As may be obvious from the literature summarized in this review, there are many
questions remaining with regard to how performers are able to select and utilize the most
critical cues and how anxiety, arousal, and attention interact to influence performance.

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Consequently, the final section of the review will summarize the primary issues of interest
and outline possible directions for future research in this area.
Most empirical research reported to date to examine the effects of arousal and/or
anxiety on performance has been oriented in a very general fashion as exemplified by the
dependent measures of both stress and performance that have been used. Very little is still
known regarding the specific components of the stress response (either cognitive or
somatic anxiety, arousal, or both) that influence specific performance variables such as
attentional parameters, speed of information processing, and other cognitive factors. By
examining these factors within the context of the relatively newly developed cusp
catastrophe model and assessing specific aspects of performance, a greater understanding
of these processes may be gathered and eventually lead to more accurate performance
enhancement interventions geared toward these specific variables. For instance, if it was
consistently determined that high levels of cognitive anxiety are actually beneficial to
performance, typical anxiety regulation interventions (i.e., cognitive restructuring to
minimize anxiety) would have to be re-evaluated.
One body of research that has been devoted to examining the emotional influence
on cognitive factors is that dealing with the concept of attentional narrowing. Though
intriguing, most studies to date have failed to indicate an accounting for the particular
aspects of the stress response that lead to narrowing and further, have been unable to
specify the specific mechanisms of the narrowing phenomenon. Consequently, research
should be directed toward examining the particular perceptual (i.e., visual search
characteristics) and processing (i.e., memory, response selection) aspects that lead to

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peripheral narrowing. Also, in spite of the attractiveness of the narrowing idea, other
theories may be able to account for the decrease in performance that is evident when stress
levels are increased. One of the most attractive of these ideas is the idea of distractibility,
the notion that as stress levels increase, the propensity of the performer to be distracted
also increases. By devoting more attentional resources to distracting or irrelevant cues,
less attentional resources are available for primary task performance. No research
completed to date in the context of peripheral narrowing has been conducted in which
actual distractors have been presented to participants while performing central and
peripheral tasks.
Although the notions of attentional narrowing and hyperdistractibility appear to be
contradictions, perhaps each contributes to performance variation in a complementary
manner. Specifically, it appears as though attention may narrow during moderately high
stress periods and that eventually, as stress levels continue to increase, the remaining
attentional resources might be further consumed by an increased disposition to process
interfering internal and/or external stimuli. Though empirical evidence does not exist to
support this notion, anecdotal self-report from athletes and other performers warrants
investigation into this area.
Finally, virtually no research, either basic or applied in nature, has been generated
to examine the relationship of visual search patterns to peripheral narrowing under
stressful conditions. However, it is logical that shifts in visual attention from central areas
of a display to the periphery, and vice-versa, could be reflected, either directly or
indirectly, in visual search fixation paths, duration, and locations. Furthermore, the use of

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visual search may shed light on the question of distraction versus narrowing by indicating
whether eye-movement patterns are altered in the context of high levels of stress.
Therefore, it was my intention to attempt to delineate the contribution of distraction
and/or narrowing within the context of visual search to help explain the performance
changes that occur under stressful conditions. The specific objectives of my investigation
will be described in the following and final section of the review.
Visual Search as an Indicator of Distraction and/or Peripheral Narrowing
As mentioned earlier, the visual search process consists of primarily two distinct
stages. The first, the preattentive stage, involves the virtually unlimited capacity in which
visual information from sensory receptors is held in a rapidly decaying sensory store
(Neisser, 1967). Occurring without voluntary orientation of attention, this stage is
involved primarily with crude feature analysis or detection. The second stage is a more
focal or attention demanding stage during which the selected items in iconic store are
analyzed further (Jonides, 1981, Remington & Pierce, 1984). In accordance with a early
selection idea of attention (e.g., Treisman & Gelade, 1980), this is the phase when specific
icons from the preattentive stage are passed from iconic store to the focal stage and
processed for appropriate responses. Under conditions in which arousal and anxiety levels
are optimal, cues deemed important to elicit appropriate responses will be utilized.
Irrelevant cues may also be subjected to further processing, especially in underactivated
situations where the mind is relatively free to wander. However, under stressful
conditions, these irrelevant cues are funneled out of short term and working memory so
that attention can be devoted to only the most relevant cues. With further increases in

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stress, however, even relevant cues may not be permitted to enter higher levels of analysis
and processing.
Visual search has been used extensively to draw cognitive inferences regarding
what information is being extracted and processed during eye fixations, a concept Viviani
(1990) has termed the “central dogma” of visual search research. Though it is presently
impossible to empirically prove the central dogma, most researchers agree that eye
fixations do at least reflect cognitive processing. Assuming the dogma to be even partially
true, if an attenuation of cues in the periphery is evident, the need to pick up crucial cues
in the periphery during particular situations would necessitate an increase in scan path
variability and fixation rate in order to compensate for peripheral narrowing. Furthermore,
if distracting visual cues were actually introduced into the test environment, visual search
strategies may be altered, resulting in increased fixation and processing of distracting
stimuli and a reduction of attentional resources available for central task performance.
Viviani (1990) suggested that the central dogma of visual search and cognitive
inference would be valid if evidence for serial search is provided in particular tasks.
According to Kahneman (1973), as arousal increases, task difficulty also increases. Under
these circumstances, parallel (relatively automatic) processes tend to be modified by the
organism, becoming more serial and attentive in nature (Duncan & Humphreys, 1989;
Shiffiin & Schneider, 1977). In this case, the ability to relate eye fixations to cognitive
information processing is more valid than when parallel processing is dominant.
In light of the lack of research which has been devoted to examining visual search
mechanisms that may influence the sensitivity and processing of cues in peripheral

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locations, investigations are warranted. As is evident by previous discussion, the driving
environment provides an ecologically valid natural dual task paradigm in which to ideally
investigate this phenomenon.
By assessing various components of the stress response including the independent
and interactive effects of both cognitive anxiety and arousal, it was anticipated that a more
complete understanding of the mechanisms responsible for the narrowing phenomenon
would be gleaned. As may be obvious from the previous discussion of newer models of
anxiety and arousal such as Hardy’s catastrophe model (Hardy & Fazey, 1987), a re¬
examination of the attentional narrowing concept was justified. Along with contemporary
understandings of the stress/performance relationship, this investigation was an attempt to
clarify many of the loopholes that permeate previous literature on the subject. By
introducing and evaluating the possible influence of distractors in the stressful
environment, a more complete comprehension of the changes in performance at varying
levels of stress was revealed.

CHAPTER 3
METHODS
In this experiment, the influence of anxiety on visual search patterns was examined
in the context of a simulated high speed race car driving task. Data from this investigation
provide a conceptual analysis of the anxiety/performance relationship and contribute to a
greater understanding of the visual search and attentional mechanisms that underlie
performance variation in stressful situations.
Participants
Female volunteers (N=48) selected from courses in the Department of Exercise
and Sport Sciences at the University of Florida were randomly selected for this
investigation and randomly assigned into six groups. Males were excluded from
participation based on research findings that indicate they are less likely than females to
report emotions, especially those of a distressful nature (Briscoe, 1985; Verbrugge, 1985).
Furthermore, females have been shown to report higher levels of competitive state anxiety
than males (e.g., Jones, & Cale, 1989).
The number of participants was determined by using Cohen’s (1988) suggestions
to maximize power and effect size. The values for entry into the sample size tables were
as follows: a = .05 (level of significance), u = 10 [(k-l)(r-l)(p-l)], f = .40 (effect size),
121

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and power = .80. No prior knowledge of the true purposes of the study nor the hypotheses
being tested were provided. Also, only participants with 20/20 (reported) vision were
tested. Participants with corrective eyeglasses were excluded due to the reduction in eye
fixation recording capabilities that often occurs with wearing glasses.
Instruments and Tests
This section describes the specific tasks and physical equipment that were used in
the study.
Central and Peripheral Tasks
Participants were tested in a simulated race car driving environment in which they
were required to perform central and peripheral tasks. A dual task paradigm involving
both central and peripheral stimuli has routinely been used in studies of attentional
narrowing to delineate the attentional resource distribution between central and peripheral
locations during task performance. It should be emphasized that participants were
informed that both tasks were equally important in terms of the overall performance score
so as not to confound any findings due to changes in probability expectations among
participants (Hockey, 1970). The two tasks will be described in detail in the following
sections.
Central task. The central task consisted of a simulated IndyCar driving task
(IndyCar, 1996) which required each individual to navigate the race course (a simulation
of the Michigan speedway) by controlling the steering, acceleration, and braking functions
of the simulated Indy car. The Indy racing simulation was assembled from primarily three

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components: (1) racing computer software, (2) analog steering wheel and foot pedals,
and (3) video projection unit.
The racing software consisted of the Papyrus Design Group IndyCar Racing II CD
(1996) and accompanying software. The program is a graphically refined, multi-option
software package allowing external programming. The realism and graphics of the
program have prompted such statements as “It’s as close to the real thing as I’ve ever
experienced” by IndyCar driver Stefan Johanssen (IndyCar, 1996). Optional programming
includes but is not limited to track selection, track conditions, number of competitors,
driving aids such as automatic shifting and braking functions, weather conditions, tire
pressure and camber adjustments, and a variety of other choices. For the purpose of the
study, the least complex of the track options (Michigan International Speedway) was
chosen and all driving aids were selected to decrease task difficulty. By using driving aids,
the only functions explicitly controlled by the driver were the steering, acceleration, and
braking functions of the car. To aid in crash recovery, the automatic righting option was
used to minimize recovery time in the case of an accident.
The driving functions of the simulation were controlled with an analog steering
wheel and braking and acceleration pedals. All components worked in an analog fashion
so as to increase the perception of reality while performing the task. In this way, if, for
example, the participant was to quickly push the acceleration pedal to the floor, the result
was loss of control of the car due to “roasting the tires” so to speak. Likewise, if the
participant turned the steering wheel sharply, the severity of the turn was reflected in the
abruptness of the visual scene change.

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The realism of the simulation was enhanced further by the use of a Sharp Liquid
Crystal Video Projection unit (Model #XG-H400U) that projected the display image
generated from a Gateway 2000 P5-166 computer onto a large screen to make it appear
life-size. In order to project the computer image, an Elite VGA to TV converter
(Advanced Digital Systems, Model #FFN-100) converted the VGA image from the
computer to a TV video signal that could be fed through the LCD projector.
Peripheral tasks. In order to examine more precisely the changes in performance
that occur in highly stressful conditions, two types of peripheral stimuli were employed.
The first peripheral stimulus was denoted as relevant to the driving task and in this way
demanded attentional resources. The use of attention-demanding peripheral light stimuli
has been used in other studies in which the attentional narrowing construct has been of
interest. Specifically, the dual task paradigm was arranged such that participants were
obliged to attend to the central task (driving the car) while having to concurrently detect
and identify (through a button press response) the illumination of randomly intermittent
red LEDs that were displayed in the periphery. The response button was mounted on the
steering wheel to minimize interference with driving.
The red lights were illuminated, one at a time, on each side of the visual field at
approximately 90° from the point of expansion (POE) in the driving display. This point
was chosen based on pilot work which showed a dramatic loss in participants’ color
discrimination abilities at peripheral angles beyond 90°. Participants were required to
identify the presence of the light as soon as possible while continuing to perform the
central driving task as accurately and quickly as possible. Reaction time (RT) from the

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time of illumination to the response was recorded for each trial through the use of a
Lafayette Instrument Co. electronic timer (Model #54419-A). This type of cue was
referred to as a “relevant” cue.
The second peripheral stimulus was used to assess whether performance changes
in central and peripheral tasks were due to an actual narrowing of the attentional field, an
increase in the distractibility of the participant, or both. The second peripheral stimulus
was a green LED illuminated in the same peripheral position as the red LED from the
previous description. By illuminating the green light in the same location as the red
stimulus, the visual angle from the point of expansion (POE) to the peripheral location
remained constant. Participants were required to perform the central driving task while
ignoring the green peripheral stimulus. Any response made to the presence of the green
stimulus was construed as a false alarm. This second type of stimulus was referred to as
the “irrelevant” or “distracting” stimulus.
A total of four peripheral stimuli were presented at randomly chosen landmarks of
the track for each lap (20 stimuli per trial block, 80 stimuli per test session). For example,
on Lap 1, stimuli were presented in either the right or left peripheral field as the driver
passed the end of pit row, as they entered the first turn, as the second set of advertisement
signs came into view, and as they crossed the finish/starting line. On the following laps,
random assignment of peripheral light color, peripheral locations, and track landmarks
denoted subsequent stimulus characteristics. In this manner, any spatial and temporal
biases that could confound attentional resource allocation processes were minimized.

126
As will become evident in the discussion of experimental conditions, two groups
received only relevant stimuli while two others received a combination of relevant and
distracting stimuli. In conditions where both relevant and distracting stimuli were
presented, an equal number of each color were randomly presented in both right and left
visual fields. Specifically, in some conditions, in addition to the red light that was
illuminated in the task relevant condition, a green light may have been illuminated in the
periphery at the same location (but not at the same time) as the task relevant cue. The task
irrelevant stimuli was interspersed with task relevant stimuli but did not require a response
and should have been ignored. After an illumination period of 3 sec, the lights were
extinguished unless a response was made to the stimulus before the 3 sec period.
Measurement Recording Devices
The following instruments and equipment were used to record eye movement data,
performance on the central and peripheral tasks, and levels of cognitive anxiety and
arousal.
Eve Movement Apparatus
An Applied Science Laboratories (ASL; Waltham, MA) 4000 SU eye movement
system was used to collect eye-movement information. The 4000 SU system is a video
based monocular corneal reflection system that measures the point of gaze relative to
video images recorded by a helmet mounted scene camera. The system has the capability
to measure pupil position and corneal reflex which are used to compute visual gaze with
respect to optics. Data from the left pupil and cornea were processed by a Gateway 2000
IBM compatible P5-133 computer and superimposed in the video image recorded by the

127
helmet mounted scene camera. In this respect, the exact point of gaze at all times could
be evaluated frame by frame with respect to the visual display. System accuracy was ±1°
visual angle with precision of Io in both vertical and horizontal fields. Also, after
calibration in relation to the head mounted scene camera, free movement of the head and
eyes was permitted.
Visual search data. Visual search data was analyzed according to the procedures
outlined by Williams, Davids, Burwitz, and Williams (1994). During the actual test
sessions, recalibration of the eye monitoring equipment was performed following each trial
block to insure the integrity of visual search data. The primary eye-movement measures of
interest were exogenous saccades to peripheral lights, fixation location, and search rate.
Exogenous saccades. Saccadic activity refers to eye movements from one area of
fixation to another. In this investigation, exogenous saccades were recorded following the
presentation of each of the peripheral lights. Exogenous saccades refer to those saccades
that are stimulus driven or initiated in a bottom-up fashion (i.e., the presentation of the
stimulus causes a saccade to that location). Though it has been suggested that exogenous
saccades are automatic and do not tap attentional resources (e.g., Pashler & O’Brien,
1993), the frequency of exogenous saccades was recorded as an index of the amount of
time spent gazing to peripheral locations. In fact, because this measure was recorded on
line, many of the saccades could possibly have been fixations. However, fixation
information was also recorded off-line.
Fixation location. Fixation location refers to the areas in the display in which the
eye fixates during completion of the tasks. Fixation location was coded for simplification

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into four primary areas: (a) central locations (within 6° of the point of expansion), (b) 2
peripheral locations (i.e., the speedometer and rear view mirrors), and (c) irrelevant areas
(outside the central and peripheral locations).
Search rate. Search rate was computed as a combination score representing the
number of fixations and the duration of each fixation at particular locations as defined
previously. Fixations were operationalized as a pause in search during which the eye
remained stationary for a period equal to or in excess of four video frames (120 ms)
(Williams, Davids, Burwitz, & Williams, 1994).
Central Task Performance
Central task performance measures of interest were the number of errors as well as
the average lap speed. An error consisted of any deviation from the race course lane
markers. Errors were further subdivided into two classifications: (a) major errors and (b)
minor errors. Major errors were operationalized as driving errors which resulted in loss of
control of the car causing the car to “spin out” and come to a complete stop before
restarting. Minor errors were operationally defined as errors which did not result in total
loss of control such as colliding with the wall or other drivers, driving into the grass, or
driving below the white lane marker. Speed was recorded upon completion of each lap
based on the average speed that was presented on the screen following each lap. Each of
these measures was obtained by viewing the videotape recording obtained from the scene
camera of the 4000 SU Eye Tracking System (ASL, 1995).

129
Peripheral Task Performance
Upon presentation of each peripheral stimulus, the participant was required to
depress the response button (mounted on the steering wheel) to indicate they had
recognized the stimulus. Response time was operationalized as the time between
presentation of the stimulus and the button press. False alarms and misses (as described
earlier) were also recorded.
Cognitive Anxiety Level
Cognitive anxiety was manipulated through the use of a contrived time-to-event
paradigm and stress inducing instructional sets. The actual level of cognitive anxiety was
measured before each test session with the short form of the CSAI-2 (Martens et
al., 1990). The CSAI-2 (See Appendix A) has been shown to be a valid and reliable
measure of cognitive anxiety, somatic anxiety, and self-confidence and has been used
repeatedly to assess the independent contribution of these constructs to the stress
response.
Physiological Arousal
Physiological arousal was assessed through measurement of heart rate. Resting
heart rate (HR) baselines were obtained just prior to performance of the initial five trial
blocks during the familiarization session. Session averages of heart rate were computed
from data recorded after each trial block during the three sessions. A difference score was
calculated for these sessions by subtracting the baseline rates from the data obtained
during the test sessions.

130
Heart rate. Heart rate was recorded by a Polar (Polar Electro Inc., Model Accurex
II) heart rate monitor (HRM) which was worn by all participants during performance of
the tasks. The Polar Accurex II is a remote HRM consisting of a wrist worn data
recording watch which collects a telemetrically projected signal from a transmitter worn
just below the sternum. Average heart rate for each test session was reported.
Procedure
Upon entering the Motor Behavior Laboratory for testing, participants were
informed that the general purpose of the experiment was to assess their eye movements as
they drove a simulated race car under different task conditions. They were then asked to
read and sign an Informed Consent form (See Appendix B), and questions regarding the
study were answered. Following completion of the Informed Consent form, participants
were outfitted in a Polar Accurex II HRM and a 1-min initial baseline heart rate was
recorded as they complete the Competitive State Anxiety Inventory - 2 (CSAI-2: Martens
et al., 1990). The measures of HR and cognitive anxiety served as baseline measures of
anxiety and arousal for future comparisons. Following completion of the CSAI-2 and
recording of heart rate information, last minute instructions were given (See Appendix C)
and participants were seated at the driving apparatus.
Once seated at the apparatus, participants were asked to assume the position in
which they would be most comfortable for the driving task. At this point, peripheral
visual acuity was tested by illuminating the LEDs in the peripheral location and asking
participants to respond by naming the color of the LED when the light was illuminated.
The specific location of peripheral stimuli was determined individually due to variability

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between participants with regard to peripheral visual acuity. Peripheral stimuli position
coincided with the furthest distance from the POE in which color discrimination was still
possible. This was operationalized as the point at which participants were able to achieve
a 100% hit rate on five presented colors, and any movement beyond that point resulted in
a lower hit rate.
After completing the peripheral stimulus identification check, participants were
outfitted in an ASL 4000SU eye movement tracking system (Applied Science
Laboratories, 1995) which was calibrated using a simple 9-point calibration reference grid.
In this manner, their exact point of gaze corresponded to the fixation point as indicated by
a cursor. The reference grid was presented through the video projector and generated by
a computer graphics program (Microsoft Paint, 1995). The grid was the same size of the
viewing screen so that the scope of fixation points corresponded to the size of the video
image used during the simulation. After being calibrated, participants were ready to
complete the experimental tasks. They completed three test sessions (including the
familiarization session) according to specific experimental considerations based on the
group to which they were randomly assigned. The first session occurred on the initial visit
to the lab and then the second and third sessions took place two days later.
Experimental Groups
Participants were randomly assigned into six groups. Three of the six groups were
exposed to multiple variations of anxiety and task conditions according to a contrived
time-to-significant event paradigm and various instructional sets. As mentioned, the
instructional sets used were similar to those administered by Hardy, Parfitt, & Pates

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(1994) to manipulate levels of cognitive anxiety independent of somatic anxiety. These
manipulations have been shown to be valid in both sport specific (Hardy et al., 1994) and
other evaluative situations (e.g., Morris et al., 1981). Furthermore, the time-to-event
paradigm has been shown to be a reliable means of investigating temporal changes in
anxiety associated with impending competitions (Hardy, Parfitt, & Pates, 1994). Both the
time-to-event paradigm and the instructional sets will be described in detail later.
The other three of the six groups were not exposed to the time-to-event anxiety
manipulations. Rather, they merely completed three sessions in which they were told to
perform the task as best that they could. These control groups were used to assure that
changes in the other experimental conditions were due to anxiety manipulations and not
mere practice effects or other confounding variables.
Control groups. As mentioned, three groups (the control groups) did not
experience manipulations associated with the time-to-event paradigm or instructional sets
geared towards increasing the level of anxiety. The first control group {central control)
performed the central task without a peripheral task to perform congruently. The second
control group {relevant control) performed both the central and peripheral tasks with the
peripheral stimuli being the relevant (red) LED. The third control group {distraction
control) performed both central and peripheral tasks, similar to the second group.
However, the peripheral stimuli consisted of both task relevant (red LEDs) stimuli and
task irrelevant (green LEDs) stimuli.
Anxiety groups. As mentioned, a contrived time-to-event paradigm and anxiety
producing instructional sets were used to manipulate levels of anxiety for the other three

133
groups. Specifically, the time-to-event paradigm establishes a sequence of sessions which
lead up to a competitive event in the final session. In the context of sport, this is similar to
the athlete who has a preparation period leading up to the actual game or event of
importance. Because volunteers and not actual athletes were used as participants in this
study, the time-to-event paradigm required the manipulation of instructional sets which
influenced the participants’ perception that there actually was a significant competition at
the end of the specified training period.
The fourth, fifth, and sixth experimental groups were each exposed to the
instructional sets associated with the stages of the time-to-event paradigm. Specifically,
the fourth group performed the central task without performing the peripheral task (the
central anxiety condition) . The fifth group performed the central task but was also
instructed to make a response to the relevant stimulus that was presented in the periphery
at intermittent intervals (the relevant anxiety condition). The sixth group also performed
the same central driving task, but was instructed to ignore the irrelevant stimuli and
identify only the relevant ones that were presented in intermittent random intervals in the
periphery (the distraction anxiety condition).
Those groups that experienced the anxiety manipulations completed three sessions
denoted as a familiarization session, a practice session, and a competition session. The
familiarization session was operationalized as the low anxiety condition, the practice
session was the moderate anxiety condition, and the competition session was the high
anxiety condition.

134
At the beginning of the familiarization session, participants were informed of the
general format of the three sessions, the competition that would take place during the third
session, and a $50.00 prize that would be given to the best performer. It was also
emphasized that their overall performance score would be equally weighted by the ability
to drive the car as fast and accident-free as possible while detecting as quickly and
accurately as possible the presence of the peripheral lights. Furthermore, they were
informed that the familiarization session should be used to gain an understanding of how
to perform both the central and peripheral tasks and that their scores would not be
evaluated in any way. However, they were also encouraged to do their best as this would
help them prepare for the impending competition session (See Appendix D).
Following the familiarization session, each of the six groups completed two other
experimental sessions two days after the familiarization session. The two sessions
consisted of manipulations of anxiety according to the time-to-event paradigm for the
central anxiety, relevant anxiety, and distraction anxiety conditions. Specifically, during
the second session (the practice session), a moderate anxiety instructional set was used in
which experimental groups were told that although the session was a practice session,
their scores would be recorded and used for future comparison with other participants.
Furthermore, it was stressed that they would have no more practice before the actual
competition session (See Appendix E).
During the third session (the competition session), the high anxiety producing
instructional set was used. Participants were told individually that their previous
performances were good and that they were very close to winning the prize money. They

135
were also be shown a mock graphic depiction of their performance and how close they
were to the top performer. Furthermore, at this time they were advised that the amount of
extra credit they would receive was dependent on their performance in the competition
session. Finally, they were notified that CNN and the Discovery channel are interested in
this research and that if they performed well, they would be on TV as part of a science and
technology special later in the Spring. A video camera was then assembled to mimic
recording of the session. By providing an instructional set that highly stressed the
importance of the competition, it was anticipated that the significance of the competition
setting would become salient for the participant (See Appendix F). In this manner, the
time-to-event paradigm was established to mimic actual competitive situations.
In total, all participants were required to complete 20 laps of the simulated 2 mi
(3.4 km) course as quickly and as accurately as possible (i.e., minimize errors while
maximizing lap speed) during each test session. A trial block condition was established in
which there were 4 trial blocks for each test session with 5 trials (laps) per block. The
approximate time required to complete each trial block depended on the number of
collisions and average speed of the laps lasted approximately 5 min. See Table 3.1 for a
representation of the research design.
Upon completion of the study, participants were asked to leave the testing
area and debriefed as to the specific manipulations that were used and the true purpose of
the study. They were then asked to complete a short post-experiment questionnaire as a
manipulation check and to assess general feelings of driving efficacy (See Appendix G).

136
Table 3.1. Experimental Design
ExDerimental Desien
Familiarization
Practice
Competition
Task Type
Session I
Session 2
Session 3
Central
low anxiety
low anxiety
low anxiety
Relevant
low anxiety
low anxiety
low anxiety
Distraction
low anxiety
low anxiety
low anxiety
Central
low anxiety
moderate anxiety
high anxiety
Relevant
low anxiety
moderate anxiety
high anxiety
Distraction
low anxiety
moderate anxiety
high anxiety
Finally, they were given the opportunity to ask any questions related to the study. All
participants received full credit for participation regardless of performance on the task,
and the best performer received a $50.00 award for her performance.
It should be emphasized that the manipulations provided by the instructional sets
and the time-to-event paradigm were used to directly manipulate anxiety. Specifically,
only cognitive anxiety and not arousal were directly manipulated in this experiment.
However, in accordance with typical physiological responses to anxiety-producing stimuli,
it was anticipated that the elevation in cognitive anxiety would indirectly produce
increases in arousal level (Hardy & Fazey, 1987).
Data Analysis
Several analyses were used to examine data acquired on the dependent measures
of interest in this investigation. Due to the interdependence and possible association of

137
each of the dependent measures with each other, independent analyses of variance
(ANOVAs) rather than multivariate analyses of variance (MANOVAs) were the preferred
procedures. The factors of interest were Groups (1 -central control, 2- central anxiety, 3-
relevant control, 4-relevant anxiety, 5-distraction control, 6-distraction anxiety), and
Sessions (1-familiarization, 2-practice, 3-competition).
Anxiety and Arousal
Cognitive anxiety and arousal (HR and pupil diameter) data were evaluated with
separate mixed model factorial ANOVAs. Cognitive anxiety scores during each session
were analyzed with a 6 x 3 (Group x Session) mixed model factorial ANOVA with
repeated measures on the second factor. For arousal, data was collected for each trial
block and then collapsed across each session. The session means for HR and pupil
diameter were then statistically analyzed with separate 6x3 (Group x Session) mixed
model factorial ANOVAs with repeated measures on the last factor.
Central and Peripheral Tasks
As mentioned, the measures of central task proficiency were lap speed and driver
error rate (major and minor errors). Means for each of these measures were calculated for
the three test sessions. Lap speed and driving error information were each analyzed with
separate 6x3 (Group x Session) mixed model factorial ANOVAs with repeated measures
on the last factor.
Peripheral task performance was determined by analyzing both RT to the
peripheral stimuli as well as the number of errors in peripheral light detection. These

138
measures were analyzed with separate 4x3 (Group x Session) mixed model factorial
ANOVAs with repeated measures on the last factor.
Visual Search
The visual search measures of exogenous saccades, fixation location, and search
rate were analyzed with separate 6x3 (Group x Session) mixed model factorial ANOVAs
with repeated measures on the last factor. All visual search data used for analysis was
taken from the middle (third) trial of each trial block completed during the three sessions.
Multiple Regression
Finally, separate multiple regression analyses were performed for each test session
to examine the whether anxiety or arousal were predictive of changes in the various
performance measures. In these analyses, anxiety and arousal were used as the predictor
variables and were regressed against each of the performance factors. Finally, anxiety and
arousal were regressed against each of the separate indices of visual search patterns to
determine whether search strategy variations were predicted more accurately by
physiological or cognitive mechanisms.

CHAPTER 4
RESULTS
For all statistical analyses performed, alpha was set at .05. In situations where
sphericity was violated during repeated measures ANOVAs, the Greenhouse-Geisser
adjusted pt-value was used as the level of significance. Sheffé’s post hoc analysis was
applied to discriminate main effects, and simple effects tests were performed following any
significant interactions. The chapter is arranged so that data concerning the anxiety
manipulations and corresponding changes in arousal will be described first. Next,
performance on the central driving task under the various experimental conditions will be
presented. Visual search fixation data will then be summarized. Finally, results from
multiple regression analyses will be described. Note that in all tables and figures, groups
are denoted as such: distraction-control = D-C, distraction-anxiety = D-A, relevant-
control = R-C, relevant-anxiety = R-A, central-control = C-C, central-anxiety = C-A.
Anxiety and Arousal
Cognitive anxiety was determined on the basis of data collected with the CSAI-2
(Martens et al., 1990). The index of arousal was heart rate (HR), which was collected with
the Polar HRM. Data were analyzed with a separate 6x3 (Group x Session) ANOVA
with repeated measures on the last factor.
139

140
Cognitive Anxiety
Analysis revealed a significant main effect for Session (F(2>84) = 11.95, p<001).
More importantly, however, was the significant Group x Session interaction (F(i0,84) =
6.50, p< 001). See Figure 4.1 for a graphic representation of the results. Simple effects
analysis revealed that the anxiety groups significantly increased in cognitive anxiety levels
during the competition session while the control groups remained stable across the three
test sessions (See Table 4.1). There were no other significant effects found for anxiety.
Table 4.1
Cognitive Anxiety Levels for Each Group Across Sessions 1-3
Grouo
M
Session 1
SD
Session 2
M SD
M
Session 3
SD
D-C
10.37
1.76
11
2.56
10.87
2.41
D-A
13.12
4.22
14.37
3.85
18.12
5.93
R-C
14.5
4.10
13.5
4.62
11.62
3.70
R-A
13
4.47
14
5.65
19.75
4.59
C-C
13.37
3.37
11.12
2.53
10.87
1.88
C-A
10.62
1.40
11.5
3.77
17.87
7.93
HR Change
Analysis of heart rate data indicated a significant main effect for Group (F(5;42) =
17.31, ja<001) and Session (F(2,84) = 42.87, p<001). The relationships were described

141
Figure 4.1. Changes in cognitive anxiety for each group across sessions 1-3.

142
more accurately, however, by the significant Group x Session interaction (F(i0,84) = 29.04,
P< 001). Figure 4.2 provides a graphic depiction of the results. Simple effects analysis
revealed that the three anxiety groups exhibited significant increases in HR in session 3 in
comparison with the three control groups which remained stable or experienced decreases
in HR (See Table 4.2). No other differences were found.
Table 4.2
Change from Baseline HR for Each Group Across Sessions 1-3
Group
M
Session 1
SD
Session 2
M
SD
M
Session 3
SD
D-C
-2.24
3.93
-8.37
5.01
-10.67
6.21
D-A
1.43
2.61
9.89
7.97
21.54
7.80
R-C
5.53
8.93
2
5.10
-1.53
5.24
R-A
3.39
3.00
10.56
5.68
23.24
7.14
C-C
1.81
5.08
2.68
5.76
0.43
6.17
C-A
2.06
3.91
6.18
5.33
19.15
6.25
Performance Data
Several factors were used as a basis for evaluating performance. Achievement on
the central driving task was based on (1) lap speed, (2) the number of minor errors, and
(3) the number of major errors. Performance on the peripheral light detection task was
based on (1) response time to the peripheral stimuli, and (2) the number of
misidentifications of peripheral lights. Performance differences were determined using

143
Figure 4.2. Change in HR from baseline rates for each group during sessions 1-3.

144
separate mixed design 6x3 (Group x Session) ANOVAs with repeated measures on the
last factor.
Lap Speed
Lap speed was recorded after completion of each of the 60 laps and was averaged
according to each 20-lap session. The analysis yielded a significant main effect for Session
(E(2,84) = 70.83, p<001). A more meaningful finding, however, was the significant Group
x Session interaction (F(io,84) = 3.63, p<001). Figure 4.3 graphically illustrates this result.
Simple effects analyses revealed an interaction between two of the anxiety groups and the
control groups. Specifically, the distraction anxiety group and the central anxiety group
exhibited a significant increase in speed from Session 1 to Session 2 and then a significant
decrease in speed from Session 2 to Session 3. Conversely, all control groups as well as
the relevant anxiety group improved significantly from Session 1 to Session 3 (See Table
4.3).
Driving Errors
Driving errors were dichotomized based on the severity of the error as (1) major
errors, and (2) minor errors. Each type of error was recorded and averaged for each of
the three test sessions and analyzed separately.
Major errors. A significant main effect was found for Session (F(2,84) =44.03,
P<001). A more important finding, however, was the significant Group x Session
interaction (F(io,84)= 2.00, p< 05). See Figure 4.4 for a graphic representation of the
interaction for major driving errors. Simple effects tests indicated that although all groups
were able to significantly decrease the number of major errors from Session 1 to Session 2

145
and then stabilize from Session 2 to Session 3, the distraction anxiety group exhibited an
increase in the number of major errors for Session 3 (See Table 4.4).
Table 4.3
Driving Performance (Lap Speed)
Session 1
Session 2
Session 3
Group
M
SD
M
SD
M
SD
D-C
161.87
16.90
170.58
16.17
184.95
9.95
D-A
170.54
8.74
185.52
12.86
176.50
17.51
R-C
161.88
9.43
181.50
8.57
185.56
7.20
R-A
160.83
15.30
182.04
15.62
185.92
10.16
C-C
166.53
17.91
184.51
9.94
190.40
6.73
C-A
175.46
11.94
190.45
7.91
182.26
4.85
Minor errors. Analysis of the minor errors yielded a significant main effect for
Session (E(i84)= 22.28, p<001). The Sheffé follow-up procedure indicated that the
number of minor errors was significantly less in Sessions 2 and 3 (M = 27.35, SD = 4.54;
M = 25.38, SD = 7.86, respectively) than in Session 1 (M - 37.63, SD = 3.88). No other
significant main effects or interactions were found for minor errors.
Peripheral Task Performance
The two dependent measures used as indicators of peripheral stimulus
identification proficiency were (1) response time (RT), and (2) number of mis-
identifications (misses). Since the central groups did not perform the peripheral stimulus

146
—D-C
-A-D-A
—H—R-C
—®—R-A
-•-C-C
—t—C-A
Figure 4.3. Lap speed for each group across sessions 1-3.

147
Table 4.4
Number of Major Driving Errors
Group
Session 1
M
SD
Session 2
M
SD
Session 3
M
SD
D-C
10.37
4.62
8.62
6.43
4
2.87
D-A
8.25
2.91
3.62
1.92
6.25
4.77
R-C
12.37
5.23
5.62
3.58
4.37
2.77
R-A
12.87
5.05
4.37
4.74
4.25
3.91
C-C
11.12
7.18
4.87
3.09
2.87
2.35
C-A
8.25
4.36
4
3.25
4.25
2.12
identification task, the only groups included in the analysis were those in the relevant and
distraction conditions. Data for RT and number of misses were analyzed with separate
mixed design 4x3 ANOVAs with repeated measures on the last factor.
Response time. The analysis of RT indicated a significant main effect for Group
(E(3,28)= 6.29, p<01). More importantly, however, was the significant Group x Session
interaction (F(6;56)= 6.76, p<001). A graphic depiction of the interaction can be seen in
Figure 4.5. Simple effects tests suggested that no differences existed between groups in
Session 1. However, the distraction anxiety group exhibited higher RTs than the relevant
control group in Session 2. Furthermore, in Session 3, the distraction anxiety group
responded slower to the peripheral stimulus than did the distraction control and

148
Figure 4.4. Number of major driving errors for each group across sessions 1-3.

149
relevant control groups. Similarly, in Session 3, the relevant anxiety group showed
longer response times than did the relevant control group (See Table 4.5).
Table 4.5
Mean Response Time Across Sessions 1-3
Session 1
Session 2
Session 3
Group
M
SD
M
SD
M
SD
D-C
679.84
74.29
628.54
46.33
576.18
53.49
D-A
676.81
81.40
672.98
76.66
730.09
62.12
R-C
603.25
96.37
543.07
72.21
513.88
69.64
R-A
602.27
86.99
611.48
70.24
647.98
92.73
Number of misses. Results of the ANOVA on “miss” data revealed a significant
main effect for Group (F(3;2g)= 6.19, p<.01). Of greater interest, however, was the
significant Group x Session interaction (F(6,56) = 4.02, p< 01). See Figure 4.6 for a graphic
representation of the interaction for the number of misses. Simple effects analyses
indicated no differences between groups in Session 1. However, in Session 2, the relevant
anxiety group committed significantly more misses than did the distraction control and
relevant control groups. In Session 3, the distraction anxiety and relevant anxiety groups
exhibited significantly more misses than did the two control conditions. Furthermore,
although the relevant anxiety group was significantly more error prone in Session 2, the
distraction anxiety group committed significantly more misses during Session 3 (See
Table 4.6).

150
—D-C
—A—D-A
—X—R-C
-e-R-A
Figure 4.5. Mean response time across Sessions 1-3.

151
Table 4.6
Mean Number of Peripheral Light Misidentifications
Session 1
Session 2
Session 3
Group
M
SD
M
SD
M
SD
D-C
3
2.44
1
0.75
0.62
1.06
D-A
4
3.46
2
1.92
7.87
4.99
R-C
2.12
4.08
0.62
0.91
0.5
0.92
R-A
3.25
3.61
4.5
3.54
5.37
3.70
Visual Search Data
Measures of interest for the visual search data included (1) frequency of
exogenous saccades to the peripheral stimuli, (2) fixation location, and (3) search rate.
With the exception of the first dependent measure which was limited to the relevant and
distraction conditions, other visual search data were analyzed with separate mixed design
6x3 ANOVAs with repeated measures on the last factor. Exogenous saccade
information was analyzed with a 4 x 3 mixed design ANOVA with repeated measures on
the last factor.
Exogenous Saccades
Analysis of exogenous saccades yielded significant main effects for Group (F(3j28) =
21.55, p<001) and Session (F(2,56) = 18.77, p<001). However, a more meaningful finding
was the significant Group x Session interaction (F(6,56) = 23.73, p<001). A graph
illustrating these results can be seen in Figure 4.7. Simple effects tests indicated that in

152
—D-C
~A— D-A
-X-R-C
-e-R-A
Figure 4.6. Mean number of peripheral light misidentifications.

153
Session 1, the distraction groups demonstrated significantly more saccades to the
peripheral stimuli than did the relevant groups. In Sessions 2 and 3, although the
distraction control, relevant control, and relevant anxiety groups exhibited similar
saccadic activity, the distraction anxiety group made significantly more saccades to
peripheral stimuli (See Table 4.7).
Table 4.7
Number of Saccades to Peripheral Stimuli
Session 1
Session 2
Session 3
GrouD
M
SD
M
SD
M
SD
D-C
9.5 5.29
6.87
6.72
6.62
7.57
D-A
10.5
6.78
24.37
9.60
42.5
20.50
R-C
3.25
3.10
2.37
1.99
0.87
1.12
R-A
2
1.06
2.87
3.48
4.62
2.82
Fixation Location
For the purpose of this study, fixation locations were coded into four areas: (1) the
point of expansion (POE), (2) the speedometer, (3) rear view mirrors, and (4) off the
projected viewing area. Of the locations of interest, the only differences between groups
were found with respect to the number of fixations that occurred off the screen. These
results are described next.
Analysis of fixation location data yielded a significant Group x Session interaction
(F( io,84)= 1.97, p <05). A graphic representation of the results can be seen in Figure 4.8.

# Exogenous Saccades to Periphery
154
Figure 4.7. Number of saccades to peripheral stimuli across sessions 1-3.

155
Simple effects tests indicated that all groups exhibited similar fixation location tendencies
during the first two sessions with the exception of the relevant and distraction anxiety
groups which fixated significantly more often to the periphery. However, in Session 2 and
3 the distraction control, relevant control, and relevant anxiety groups demonstrated less
fixations to peripheral locations than did the distraction anxiety group (See Table 4.8).
Table 4.8
Number of Fixations to Peripheral Locations Across Sessions 1-3
Group
Session 1
M
SD
Session 2
M SD
Session 3
M
SD
D-C
4.67
.58
2.25
1.89
1
0
D-A
8.5
3.53
5.6
4.38
7.4
4.77
R-C
3.8
.95
3
.88
1.5
.71
R-A
9
8.99
2
0
2
0
C-C
1
0
1.5
1
2
0
C-A
5
4.36
1.25
.5
0
0
Search Rate
The final visual search data of interest in this investigation was the search rate
exhibited by drivers or the overall duration of fixations at each location. No significant
differences in search rate were found, however, with respect to the each of the four
locations of interest.

Figure 4.8. Number of fixations to peripheral locations across sessions 1-3.

157
Multiple Regression Analyses
Correlational research methods were also used to analyze the data and the Pearson
Product-Moment intercorrelation coefficients for all variables can be seen in Appendix H.
In addition to the use of ANOVA and simple correlations, stepwise multiple regression
analyses were performed to determine which of the indicators of activation added to a
linear function to predict changes in performance as measured by the indices of central and
peripheral task proficiency. Also, it was of interest to determine whether visual search
pattern alterations were predicted by variations in anxiety and arousal levels.
Activation and Performance
The predictor variables used in the multiple regression analysis were the levels of
anxiety and arousal obtained for each session. The dependent variables included central
task measures of lap speed, major errors, and minor errors, and peripheral task measures
of response time and accuracy.
Central task. The predictor variables of anxiety and arousal were able to account
for a significant amount of the variance in lap speed during Sessions 2 and 3. During
Session 2, arousal accounted for more variability in lap speed than did anxiety. That is, as
arousal level increased, so too did lap speed. However, during Session 3, anxiety level
was the most influential predictor. In general, as anxiety levels increased, lap speed was
detrimentally affected. Summary information for the multiple regression analyses can be
seen in Table 4.9.

158
Table 4.9
Sessions 1 to 3
Variable
r!
b Beta
SEb
í E
Session 2
HR
.09
.45 .29
.22
2.07 .04
Session 3
Anxiety
.11
-.59 -.33
.25
-2.38 .02
The total percentage of variability in lap speed explained by arousal was 8.5 %
during Session 2. In Session 3, fluctuations in anxiety levels were able to account for
approximately 11% of the variability in lap speed.
Peripheral task. Separate stepwise multiple regression analyses were also
performed for peripheral task measures of response time and the number of
misidentifications of peripheral stimuli. The analyses indicated that HR was able to
account for a significant amount of variability in both measures during Session 3 and also
during Session 2 for the number of misidentifications (See Tables 4.10 and 4.11).
The total amount of variability in peripheral task response time that was explained
by heart rate change was 29% during the final session. With respect to the explained
variability in misidentifications, heart rate change accounted for 16% in Session 2 and 40%

159
Table 4.10
Across Sessions 1 to 3
Variable
R:
b Beta
SEb
£ E
Session 3
HR
.29
3.54 .54
1.01
3.50 .001
Table 4.11
Stepwise Multiple Regression Analysis Predicting Misidentifications of Peripheral Stimuli
with Activation Data Across Sessions 1 to 3
Variable
r!
b Beta
SEb
1 E
Session 2
HR
.16
.10 .40
.04
2.40 .02
Session 3
HR
.40
.17 .63
.04
4.45 .001
in Session 3. In general, as HR increased, participants required more time to respond to
peripheral lights and misidentified more stimuli.

160
Activation and Visual Search Patterns
Of the visual search patterns examined in the investigation, the number of saccades
to peripheral locations was the only measure that was significantly predicted by activation
changes and was significantly accounted for by fluctuation in anxiety levels (See Table
4.12).
Table 4.12
Data Across Sessions 1 to 3
Variable
R*
b
Beta
SEb
í p
Session 3
Anxiety
.14
1.3
.37
.59
2.20 .04
Anxiety variation accounted for 13.8% of the variability in the number of
exogenous saccades to peripheral stimuli with more saccades occurring to peripheral areas
as anxiety levels increased.
Manipulation Checks
In order to accurately study the measures of interest, it was critical that levels of
arousal and anxiety were manipulated. As mentioned, the significant increases in both
cognitive anxiety as well as heart rate provided verification for the potency of the time-to-
event paradigm as well as the instructional sets used. To further substantiate the

161
effectiveness of these manipulations, a post-experiment questionnaire was distributed to
participants at the completion of the study.
Items of relevance included questions regarding specific aspects of the
instructional sets used. Participants were asked to rank on a Likert scale ranging from a
score of 1 (not at all) to 7 (very much so) the degree to which they believed that (1) they
would be on the Discovery channel, (2) they would receive $50.00 for being the best
driver, (3) they were in second place after the practice session, and how much each of
these factors affected them. They were also asked whether they believed that they would
not receive full credit if they did not perform at a high level in the competition session.
Results of the post-experiment questionnaire confirmed that anxiety was
manipulated in the expected direction. Participants rated their belief in anxiety
manipulations from a low of 5.71 for the false feedback graph to a high of 6.04 for the
$50.00 reward for the best driver. Also, the overall influence of the manipulations on
anxiety levels produced an average score of 5.88. Taken collectively, the results of the
manipulation check, CSAI-2 scores, and physiological arousal levels provide ample
evidence that the manipulations were quite effective. Post experiment comments were
also recorded and can be seen in Appendix G.

CHAPTER 5
DISCUSSION, SUMMARY, CONCLUSIONS, AND
IMPLICATIONS FOR FURTHER RESEARCH
The attentional narrowing phenomenon relating activation levels to attentional
processing has provided a theoretical framework for understanding the influence of stress
on attention in a variety of basic and applied environments. Early investigations in this
area led cognitive psychologists such as Bahrick, Fitts, and Rankin (1952) and Callaway
and Thompson (1953) to theorize that at different levels of activation, the utilization of
environmental cues changed in a predictable manner. This idea culminated in
Easterbrook’s (1959) influential article on the topic in which he explained attentional
narrowing on the basis of several factors including the “intellectual competence” of the
participant: i.e., knowing what cues to attend to at the appropriate times. His classic
paper included an elaboration of the concept to specify the changes in attentional
processing that occurred under increasing levels of emotionality and how this would affect
performance on central and peripheral tasks in a dual task scenario. The necessity to
select and process critical information in a timely manner is crucial to high achievement in
sport. How ever, few researchers have attempted to examine the practical and theoretical
implications of the attentional narrowing construct in the sport domain.
162

163
Though support for the concept of attentional narrowing has been provided in
several investigations (e.g., Granger, 1953; Williams, Tonymon, & Andersen, 1990,
1991), the underlying mechanisms of the narrowing phenomenon have never been
specified, but merely speculated. Findings have consistently indicated a decrement in
performance on peripheral tasks with a corresponding facilitation of central task
performance at moderate levels of activation when compared to lower levels. Similar
support has been shown (though not quite as conclusively) that a decrease in performance
of both central and peripheral tasks occurs at relatively high levels of anxiety/arousal (e.g.,
Bruner, Matter, & Papanek, 1955; Callaway & Dembo, 1958; Callaway & Thompson,
1953; Williams, Tonymon, & Andersen, 1990, 1991). Though the topic has been
extensively researched, several gaps currently exist in the literature. For example,
although numerous methods have been used to increase level of activation in participants
(including anything from testing at variable depths of water to testing under extremely
noisy conditions), few attempts have been made to identify the potentially unique influence
of anxiety and arousal on performance, nor has it been possible to pinpoint the interactive
effects of these factors (Hardy, 1996). Specifically, questions remain as to whether
changes in performance are related primarily to physiological mechanisms (such as heart
rate, skin resistance, and electroencephalographic activity) or to changes in cognitive
indices of anxiety.
In addition to clarifying the activation/ performance relationship, another primary
issue of interest was to provide evidence that many of the results of the narrowing
phenomena could be accounted for in the context of distraction. Upon further
examination of the literature which has been used to support the idea of a narrowing

164
phenomenon (e.g., Landers, 1980), it becomes quite evident that the same results can be
described by a scenario in which participants become hyper-distracted at high levels of
emotional energy. Accordingly, visual search data were incorporated in my investigation
to shed light on the distraction/ narrowing question and to determine where performance
changes may be rooted.
These issues provided the initial impetus of the investigation and will be discussed
in this chapter. The first section deals with the observed changes in cognitive anxiety and
arousal that occurred due to the use of the time-to-event paradigm and instructional sets.
A description of the impact of the anxiety manipulations on performance of the central and
peripheral tasks follows. Next, a discussion of the relationship of visual search patterns to
performance and activation variations will be elaborated. In lieu of these considerations,
an attempt will be made to describe the relationship between the attentional mechanisms
that may have been influenced by anxiety and arousal and the effect of these attentional
changes on performance. Finally, practical applications of this knowledge will be offered
and the chapter will conclude with a discussion of future research directions pertaining to
the topic of attention, anxiety, and performance.
Discussion
Changes in levels of cognitive anxiety and physiological arousal due to the use of
the time-to-event paradigm and instructional sets are addressed next. Possible reasons for
the trends in the data are also proposed in this section.
Cognitive Anxiety
It was hypothesized that the anxiety groups would experience changes in cognitive
anxiety levels that would vary from low levels in the familiarization session to moderate

165
levels in the practice session, and eventually to high levels during the competition session.
These changes were expected to occur in response to the time-to-event arrangement as
well as the instructional sets used at each stage (Hardy, 1996). Conversely, it was
expected that cognitive anxiety levels would remain relatively stable across the three
sessions for control groups.
Scores on the cognitive anxiety subscale of the CSAI-2 (Martens et al., 1990)
generally supported this prediction. Although anxiety levels increased slightly in the
practice session for the anxiety groups, a dramatic increase in anxiety was evident during
the competition session. Cognitive anxiety levels for the control groups remained stable
across the three sessions and even decreased in two of the three groups.
Upon farther examination of the data, it is evident that the manipulation of
cognitive anxiety was successful. If this had not been the case, it would have been
extremely difficult to draw any conclusions from the rest of the data and impossible to
empirically evaluate the primary purposes of the study. Obviously, the manipulations used
created feelings of anxiety that were reflected in both the self-report scores of the CSAI-2
as well as anecdotal comments regarding how the manipulations affected them (See
Appendix G).
Aside from the anxiety increases experienced by those in anxiety groups, it is also
important to note the decrease in anxiety levels of the control groups. Upon farther
consideration, this is an effect that should have been expected (Carver & Scheier, 1981).
control group participants were unaware of any costs and benefits for performance on the
task. They had no reason to fear any consequence of performing poorly (except perhaps
embarrassment) and had no incentives to perform well. It would be expected that the first

166
session would be the most anxiety producing due to the lack of familiarity with the test
conditions, discomfort felt in an unknown environment, and the like. One would presume,
therefore, that as the experiment progressed, a level of comfort would be achieved and
anxiety levels would drop. Furthermore, as participants were given extensive practice on
the task, it appears that a comfort state was achieved which led to less wrecks and an
increase in lap speed.
These findings are congruent with other studies of the multidimensional nature of
anxiety in that the level of cognitive anxiety is expected to fluctuate based on the
probability of success/failure and the consequences of success/failure (Jones & Hardy,
1990; Martens et al., 1990). In line with this notion, increases in cognitive anxiety would
be expected for the anxiety groups due to the changes in instructional sets at each stage of
the study. On the other hand, anxiety level in the control group would be expected to stay
relatively the same or decrease based on the fact that they were given identical instructions
at each stage.
Heart Rate
Substantial evidence has been compiled to suggest that as anxiety levels increase,
one of the primary physiological indicators of arousal that accompanies this increase is
accelerated heart rate. Though not a uniform or infallible measure of arousal due to
individual response stereotypes (Lacey & Lacey, 1958), HR has been used extensively as a
measure of a rousal (e.g., Fazey & Hardy, 1988; Hardy, 1996; Hardy, Parfitt, & Pates,
1994; Parfitt, Hardy, & Pates, 1995) and, therefore, was of interest in this study. HR data
were expected to reflect the general changes that occurred in anxiety across the three test
sessions. More specifically, postulated was that those in the anxiety groups would

167
experience moderate increases in HR during Session 2 and then experience higher HR
levels in Session 3. Meanwhile, it was hypothesized that those in the control conditions
would experience stable or progressively decreasing heart rates from Session 1 to Session
3. In general, these expectations were supported.
Participants performing under the anxiety conditions experienced heightened levels
of arousal in Session 2 and then even higher levels during Session 3. Conversely, control
groups experienced very little change in heart rate across sessions and even demonstrated
slight decreases in the measure. Though not the case for every participant, means for the
groups suggest that HR variations reflected general changes in anxiety across sessions.
One very important implication of this finding is the fact that physiological indices
of arousal could be measured and interpreted accordingly in the context of a natural
response to anxiety-producing stimuli. In previous investigations of the cusp catastrophe
model, for instance, arousal level of participants was artificially manipulated through
strenuous exercise or other means (Hardy, Parfitt, & Pates, 1994). It could be contended,
however, that this is not a valid means of varying arousal as it relates to the stress
response. Simply increasing HR through exercise or otherwise does not necessarily
provide an accurate indication of a physiological response to affective stimuli. By
manipulating arousal in the manner used in this investigation (as a by-product of anxiety),
it can be concluded that the increase in arousal reflected a physiological response to
anxiety-producing stimuli. When arousal levels are manipulated through exercise or other
unnatural means, it is impossible to attribute any change in arousal to cognitive or
emotional mechanisms, or to understand how the person would respond in extremely
anxiety-inducing circumstances without a cardiovascular workout beforehand.

168
The changes in arousal as well as anxiety allowed for valid comparisons of a
variety of different emotional states and enabled the study of attentional mechanisms
which were the major thrust of the study. The influence of these changes on performance
will be discussed in the following section.
Dual Task Performance
As mentioned, considerable evidence exists which specifies the performance
changes that occur in dual task situations under varying levels of activation (e.g., Landers,
1980). Several performance variables were of interest in this study. Due to the dual task
nature of the experimental arrangement, specific hypotheses were directed toward each of
variables based on their characteristics as measures of either central or peripheral tasks.
Proficiency on the central driving task was based on measures of lap speed, the number of
major errors, and the number of minor errors. Peripheral task dependent measures
included response time (RT) to the peripheral lights and the number of misidentifications
of peripheral stimuli. Each of these measures will be addressed in depth in the following
section.
Central task performance. In line with the ideas of Easterbrook (1959) and others,
it was postulated that the ability to drive the car (the central task) would follow the
specified predictions of the cue utilization hypothesis. More directly, driving skill was
expected to increase under moderate levels of anxiety and/or arousal (in comparison to
baseline levels) and to decrease at high levels of activation.
Lap speed. One would expect to see an increase in overall lap speed for the
anxiety groups during the practice session and then a decrease in lap speed during the

169
competition session. The control groups would be expected to exhibit a continuous
increase in lap speed from Session 1 through Session 3.
Results confirmed the hypotheses for the distraction anxiety and central anxiety
groups but not for the relevant anxiety group. Control groups also exhibited sequential
increases in lap speed from Sessions 1 to 3 and, as expected, the central control group
displayed the fastest competition session speed.
Interestingly, the central anxiety group demonstrated an extremely fast lap speed
average during the second session. As may have been anticipated from Easterbrook’s
(1959) original predictions, performance on the central task at moderately high levels of
anxiety would be expected to be higher than at low and high levels of activation.
Evidently, the moderate level of anxiety and arousal experienced led to a facilitation in lap
speed for the central anxiety group up to the level of the third session for the central
control group.
As hypothesized, the central anxiety group and distraction anxiety group
increased driving speed in the practice session but then exhibited a decrease in lap speed
during the competition session. Though lap speed did not decrease to initial
familiarization session levels, the drop was dramatic and in real terms would translate to
an average decrease of 504 s during a 500 mile (810 km) race, a significant amount of
time in auto racing. When placed in this context, the reduction in lap speed by those in the
anxiety conditions would be seen as catastrophic by most racing standards.
Error rate. Error rate data were categorized based on their severity into two
different classes: (1) major errors and (2) minor errors. Errors were dichotomized in
order to more accurately identify the possible underlying causes for changes in lap speed.

170
Although it would have been less tedious to merely record any error and attempt to relate
it to lap speed, such data may have been misleading. During pilot testing it was noticed
that although a driver may commit five minor errors, they did not influence overall lap
speed to the same degree of even one major error. It was expected, therefore, that major
error rate would be highly related to lap speed and also more reflective of changes in
affect and physiological arousal. Data acquired from error information will be discussed in
the context of their relationship to lap speed and activation levels.
Major errors. Extrapolating from the original hypotheses directed toward central
task performance, one would expect the anxiety conditions to decrease the number of
major errors made on the driving task as they progress from the familiarization session to
the practice session. Afterwards, an increase in the number of major errors would be
expected during the competition session. In contrast, the number of major crashes should
decrease sequentially from Session 1 to Session 3 for the control groups.
Data regarding the number of major errors were somewhat perplexing in light of
the original predictions directed toward central task performance. Of the anxiety groups,
the only one to perform as expected was the distraction anxiety group. Though
decreasing the number of major errors from the familiarization session to the practice
session, analysis of the competition session showed an increase in errors to levels slightly
below the original familiarization session. In racing or even normal driving, one major
error marks the end of a day, the destruction of a car, and possibly loss of life. In the
context of this investigation, the number of major errors correlated highly with lap speed.
Though other factors also led to lap speed increases or decreases, the ability to keep

171
control of the car was a major factor in predicting performance in this group, as indicated
by the high correlations between the two variables.
Somewhat unexpectedly, the other anxiety groups exhibited similar trends to the
control groups with respect to major errors. In general, these five groups showed
decreases in major errors from Session 1 to Session 3. Taken with the data from lap
speed, the results are somewhat confusing, especially with regard to the central anxiety
group. As the number of major errors increased, lap speed should also increase. However,
this was not the case. Although the rate of major errors remained relatively consistent for
the central anxiety group, the average lap speed decreased by over 7 mph (12 kmph).
Thus, it appears that other factors were responsible for the decrease in lap speed. In fact,
much of the remaining variance in lap speed was accounted for in this group upon
examination of the data acquired for minor errors.
Minor errors. As driving skill improved, it was expected that the number of minor
errors would tend to decrease in a similar fashion to major errors from Session 1 to 3 for
the control conditions. Furthermore, it was expected that those in the anxiety conditions
would demonstrate a reduction in the number of minor incidents during the practice
session, but not during the competition session.
The hypotheses held for control groups. As expected, minor error rates dropped
from Session 1 to Session 3 and, as evidenced by moderate to high correlations, had a
positive impact on the overall lap speed improvement made by these three groups. The
same can be said of the distraction anxiety and relevant anxiety conditions.
An interesting finding, however, was that the central anxiety group seemed
adversely affected by the number of minor errors, attaining a level in the competition

172
session that was similar to that exhibited during the familiarization session. Across groups,
major errors had more impact on driving speed that minor errors. However, in this case, a
large number of minor errors accounted for the observed decrease in lap speed during the
competition session.
The different patterns of performance on the central task among the three anxiety
groups can be explained more accurately by examining the aspect of performance that was
primarily impacted across the three sessions. Although the maintenance of performance
levels for the relevant anxiety group is difficult to explain, differences between the
distraction anxiety and central anxiety groups may be better understood upon re¬
examination of error data.
Taking into consideration both error data as well as lap speed information, it
becomes apparent where performance differences existed between the central anxiety
condition and the distraction anxiety condition. Evidence obtained from minor error data
suggests that drivers in the central anxiety group may have adopted a more cautious
driving style during Session 3, favoring smaller errors and sacrificing driving speed for the
assurance of less crashes. A strategic problem with driving too slow, however, is a
tendency to make more minor errors due to the difficulty of keeping the car on the track.
Because the racetrack turns are banked, they tend to force the car toward the infield at
lower speeds, causing driving difficulty and over-correction problems. It appears that this
may have been the case for the central anxiety condition.
In contrast, when examining the same performance variables for the distraction
anxiety condition, trends are quite different. The relationship of major errors to lap speed
in the third session was extremely high, accounting for approximately 80% of the variance

173
in lap speed during the third session. Perhaps drivers became more reckless, causing more
accidents and a significant decrease in lap speed. In this case, the effect of anxiety on
performance appears to be different from the central anxiety condition and may be a result
of the absence of the dual task scenario in the central anxiety condition. An elaboration of
this idea will be presented later.
Peripheral Task Performance
Two primary measures of peripheral task performance were examined in the study.
They included (1) the number of misidentified lights and (2) the response time to identify
relevant peripheral lights. Data related to both of these variables will be discussed in light
of the hypotheses regarding the expected detriment in peripheral task performance as
activation levels increased.
Response time. Several hypotheses were directed toward response time based on
results obtained from previous tests of the attentional narrowing construct using dual task
paradigms (e.g., Hockey, 1970; Williams et al., 1990; Yoo, 1996). First, it was suggested
that the anxiety groups would respond more slowly to peripheral lights in the practice
session than would the control groups. Furthermore, it was expected that this trend
would continue in the competition session, but to a greater degree. Specifically, predicted
was that the anxiety groups would respond slower to the peripheral lights in the
competition session than they did in the practice session and that the control groups would
respond faster to the peripheral lights in the competition session than in any previous
sessions. Furthermore, it was hypothesized that the fastest response times would be
exhibited by the relevant control group in the third session. Finally, it was suggested that

174
the distraction groups would perform proportionately worse than would the relevant
groups (Eysenck, 1992).
Findings were generally supportive of the hypotheses. Specifically, control groups
exhibited decreases in response time from the first to third sessions, anxiety groups
showed opposite trends with RT increasing sequentially from Sessions 1 to 3. Also, as
expected, the inclusion of irrelevant stimuli led to slower response times for the
distraction groups as compared to the control groups. Across groups, the fastest
response time was exhibited by the relevant control group, as expected. Furthermore, the
slowest response time was demonstrated by the distraction anxiety group in the
competition session. Not only was this a significant difference in comparison with other
groups that were required to identify peripheral lights, but it was quite dramatic. The
relevant control group displayed an advantage of more than 200 ms over the distraction
anxiety group during the third session.
Misidentifications. Expectations for the number of misses were similar to those for
response time. Specifically, it was expected that the anxiety groups would tend to miss
more of the peripheral stimuli in the practice session and even more in the competition
session than would the control groups. Also, it was hypothesized that the least number of
misses would occur for the relevant control group during the third session and the most
number of misses would occur for the distraction anxiety group during the same session.
Results supported the hypotheses.
Overall, the distraction groups tended to be more inclined to misidentify a
peripheral light than were the relevant groups. Also, as was expected, the anxiety groups
misidentified more lights than did the control groups. Furthermore, the distraction

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anxiety group missed more peripheral lights than did the relevant anxiety group under
higher levels of anxiety. These results are to be expected based on the fact that the
relevant groups merely identified the presence of any light while the distraction groups
were required to decipher the nature of the peripheral stimulus as being relevant or a
distractor.
Taken together with response time data, a clearer picture is painted of the
influence of increased activation on the performance of peripheral tasks. The results of
this investigation are not surprising in this respect. Virtually all published research dealing
with attentional narrowing has clearly shown a detriment in performance of peripheral
tasks as anxiety levels increase (e.g., Yoo, 1996). The unique contribution of the
performance data summarized here, however, is the impact of distractors on peripheral
task performance. Specifically, it appears that the impact of distractors becomes even
more devastating at high activation levels, resulting in longer response times and more
misidentifications.
Visual Search Data
Several indices of visual search tendencies were assessed during this investigation.
Specifically, the dependent measures of interest included (1) exogenous saccades to
peripheral stimuli, (2) fixation areas, and (3) search rate. The results of analyses
performed on each of the visual search parameters will be discussed in the following
section.
Exogenous Saccades
It was hypothesized that participants in the anxiety conditions would exhibit an
increase in the number of exogenous saccades to peripheral stimuli. That is, they would

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tend to make a shift in gaze from more central locations of the display to peripheral
locations that corresponded with the onset of a peripheral stimulus. In accordance with
the attentional narrowing theory, it was expected that this would be the case due to the
lack of ability to notice and discriminate peripheral stimuli as the attentional field
narrowed.
Participants in anxiety conditions were expected to systematically increase the
number of saccades to peripheral areas from Session 1 to Session 3 due to the distracting
and narrowing affect of anxiety. In contrast, it was hypothesized that those in the control
conditions would tend to sequentially decrease the number of saccades to peripheral
locations from Session 1 to 3 due to the higher degree of automaticity achieved in both
tasks as participants were given the opportunity to practice, and the lack of interference
from activation increases. Furthermore, it was expected that those in the distraction
anxiety group would exhibit more saccadic activity at higher activation levels than would
the relevant anxiety groups.
Results generally supported these hypotheses. The control groups were less likely
to make saccades to the periphery as they progressed from Session 1 to 3. Also, as
expected, the anxiety groups showed the opposite trend. As activation levels increased,
so too did the number of saccades to peripheral locations. Furthermore, the distraction
anxiety group tended to dramatically increase saccadic activity to peripheral locations,
especially in the high anxiety condition.
Fixation Location and Search Rate
Hypotheses for fixation location and search rate paralleled those for exogenous
saccades. Specifically, it was expected that with increasing levels of anxiety, more

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fixations (duration of at least 120 ms) would occur to areas away from the POE (toward
the periphery) in order to compensate for the narrowing of the attentional field and
consequential lack of ability to differentiate distractors from relevant cues. Furthermore, it
was expected that the duration of fixations to peripheral areas would increase.
Although support for fixation frequency to peripheral areas was supported, no
statistically significant changes were observed for search rate. With respect to fixation
location, the distraction anxiety condition exhibited more fixations to peripheral areas
during the third session while the other three groups tended to demonstrate less fixations
to peripheral locations during the same session.
These results can be taken as substantial support for the changes in performance
that were predicted by attentional narrowing phenomenon, and also reinforce the idea that
the narrowing phenomenon can be reflected in alterations of visual search patterns.
Furthermore, changes in visual search patterns implicate perceptual mechanisms as a
primary factor responsible for attentional narrowing. This notion will be addressed later.
Perhaps more importantly, this information involves visual distraction as an
underlying mechanism for the performance changes that occur at high levels of anxiety and
arousal. Although exogenous saccades may not be a direct indication of a shift in visual
attention (e.g., Pashler & O’Brien, 1993), they do, however, indicate a shift in visual gaze
away from the central task and toward irrelevant cues. Logically, by quadrupling the
number of saccades to peripheral areas (as was the case for the distraction-anxiety group)
less time was spent fixating on the central task. Taken together with the increase in the
number of fixations to peripheral areas, it appears that higher levels of activation increase

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the propensity to acquire information from cues outside of the useful field of view and
thereby detrimentally influence central task performance.
In this respect, data are consistent with the notion that as demands increase,
drivers develop a stronger tendency to search for information in the periphery, a
phenomenon Miura (1986) referred to as “cognitive momentum”. Miura (1990) suggests
that this is an adaptation of the attentional system to utilize resources in the most efficient
manner to deal with the increase in demands. Though Miura (1990) did not mention
attentional narrowing or distraction in his study, the visual search results from the present
study support his ideas.
Another interesting finding is the fact that even though the number of exogenous
saccades and fixations to peripheral areas increased, response time actually was longer in
the last session for the anxiety conditions. The data indicate that participants may have
adopted a different response style during the final test session. Specifically, it seems that
drivers tended fixate more on peripheral lights in order to effectively discriminate the
relevance of the light before responding to it. In earlier sessions, fewer shifts in gaze were
made to the periphery, yet response accuracy was greater and so was response time. Both
of these indices point not only to narrowing, but an increase in the tendency to be
distracted by other cues when the driver was highly activated.
Summary of Findings
Due to the large number of dependent measures recorded in this study, a synopsis
of the findings and an elaboration of their interactive effects will be provided in this
section. Generally speaking, as arousal and anxiety levels increased, participants indeed

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experienced attentional changes in both central and peripheral areas which led to
corresponding performance changes.
Driving proficiency decrements were primarily noticed in measures of lap speed
and the number of major errors experienced in the dual task situation. Individuals in the
dual task scenario who experienced performance decrements at high levels of anxiety
tended to show a decrease in lap speed as well as an increase in the number of major
driving errors. However, in the single task environment, they appeared to become more
cautious, as suggested by the increase in minor error rate at high levels of activation.
With respect to peripheral task performance, at higher levels of activation, drivers
showed a decreased ability to respond to peripheral stimuli and were more inclined to
misidentify the presence or the relevance of the stimuli. Furthermore, when distractors
were included in the task environment, more time was required to identify the presence of
these stimuli. This ability was further compounded by an increase in activation, leading to
enhanced saliency of distracting stimuli and an higher propensity to respond to distractors.
Multiple regression analyses indicate another interesting point. Anxiety was more
predictive, overall, of central task performance while arousal was more predictive of
peripheral task performance. Furthermore, anxiety was more predictive of changes in
visual search patterns (exogenous saccades). These findings will be described in
comparison to those of Yoo (1996) in the following section of the chapter.
When combining the results from performance and visual search data, a more
complete understanding of the interactions among variables is obtained. As anxiety levels
increased, more saccades and fixations were made to peripheral locations. Though it is
impossible to prove that these shifts in point of gaze absorbed attentional resources

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(Viviani, 1990), they did divert the point of gaze away from the central task and toward
peripheral areas. Correspondingly, it appears that this may have led to the performance
decreases exhibited particularly by the distraction anxiety group.
Findings Which Contradict and Augment Previous Research
As is evident in the review of literature, a majority of studies conducted in the area
of attentional narrowing demonstrate that performance on central and peripheral tasks
varies in a predictable manner depending on the level of emotionality exhibited. However,
the findings from this study are not completely congruent with the performance changes
predicted by Easterbrook (1959). One would expect that in dual task situations,
performance on peripheral tasks would decrease at moderate levels of activation and
continue to be even more adversely influenced at higher levels. In fact, these propositions
were fulfilled in the context of this investigation. However, central driving task
performance was not totally consistent with the predictions of the attentional narrowing
phenomenon. The lack of a performance decrease during the high anxiety session by those
who were merely required to identify relevant cues is inconsistent with the predictions of
the model.
A recent investigation by Yoo (1996), however, showed similar trends to the lack
of a central task performance decrease exhibited by the relevant anxiety group. In Yoo’s
study, performance in the central task (a pursuit rotor) did not change at higher overall
levels of cognitive anxiety. Results from other studies as well (e.g., Bacon, 1974;
Wachtel, 1968) have been similar. That is, although peripheral task performance was
affected in predicted directions, central task performance was not detrimentally influenced.

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The present investigation may provide clues as to why the findings on central task
performance have been somewhat equivocal. Evidence is provided here that distraction
caused by irrelevant peripheral stimuli may have an effect on central driving proficiency
above and beyond that described in the original framework of the attentional narrowing
concept (Easterbrook, 1959). In order to embrace this argument, however, it is necessary
to replicate the results of this study in others that involve the presence of distracting cues.
The principal goal of the Yoo (1996) study was to identify the aspect of the stress
response which contributed most to changes in performance. To this end, cognitive
anxiety acco unted for most of the variance in the performance changes associated with the
peripheral light identification task. This result is also inconsistent with the findings
obtained in my investigation. Specifically, the primary predictor of peripheral task
performance change (as measured by RT and misses) was physiological arousal (HR).
Perhaps more perplexing when relating Yoo’s (1996) work to my study is the
changes identified in central task performance. As mentioned, the most influential
determinant of peripheral task performance in the familiarization and practice sessions was
physiological arousal. However, during the competition session, cognitive anxiety was the
primary predictor of lap speed. Though at odds with the findings of Yoo (1996), this
result is consistent with predictions of the cusp catastrophe model which proposes
cognitive anxiety as the splitting factor and the primary determinant of performance
changes (Hardy, 1996).
Findings are even more intriguing with respect to visual fixation data which were
collected as indications of attentional focus and gaze tendencies. In fact, neither arousal
nor anxiety were significant predictors of the number of saccades made to peripheral

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stimuli during the first two sessions. However, during the third session, cognitive anxiety
was indeed a significant predictor of the number of saccades made to areas outside the
UFOV. Thus, at higher levels of anxiety, more saccades were made to peripheral
locations, and, correspondingly, lap speed markedly decreased. Evidently, the increase in
the number of saccades away from the point of expansion impacted the ability to drive
effectively as reflected in reduced lap speeds during the third session.
Theoretical Implications
One of the primary contributions of this study to the literature dealing with
attentional narrowing is the inclusion of experimental conditions in which distractors are
present along with relevant stimuli in peripheral locations. By including distractors in the
testing environment, it was possible to investigate whether, in fact, attentional narrowing
was the singular determinant of achievement changes at high anxiety levels or whether
distraction played a significant role in these performance fluctuations. This and several
other issues of theoretical importance will be revisited in this section of the chapter.
Numerous lines of evidence converge to indicate that indeed distraction was
present at high levels of anxiety. First, performance on the peripheral light detection task
was far worse under high anxiety levels for those in the distraction anxiety condition than
for any other experimental condition. Not only was more time required to identify the
presence of relevant stimuli, but also the number of misidentified stimuli increased.
Furthermore, performance on the central driving task was greatly hindered in this
same group during the competition session while driving performance was not hindered in
the other dual task conditions. Although this may not be a direct indication of an increase
in distraction during the competition session, it appears as though the need to direct more

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attention to peripheral stimuli absorbed resources that were necessary for central driving
task proficiency.
Perhaps most convincing when attempting to plead a case for the role of
distraction, however, was the increased number of fixations and saccades made to
peripheral locations during the final, and highest, anxiety-producing situation. During this
session, increased saccadic activity appeared to detract attention from the central task and
led to more driving errors and slower lap speeds. Wegner (1994), Moran (1996), and
others have presented anecdotal evidence to support the notion that as stress levels
increase, the propensity to be distracted is enhanced. However, until the completion of
my study, virtually no empirical evidence existed to support this notion. By including
distracting stimuli in the experimental protocol, not only was the task made more
ecologically valid, but answers were provided to verify speculation on this topic.
Another theoretical issue of interest was whether performance changes were due
to variability in psychological affect (i.e., cognitive anxiety), an increase in arousal level, or
some combination of both. This question was resolved in the context of my investigation
yet also produced inconsistent data that raised more conjecture about the complex
relationship of these factors to performance. The peripheral task appeared to be most
negatively affected by relatively higher physiological arousal levels. In contrast, the
primary predictor of changes in driving achievement was cognitive anxiety. Furthermore,
the number of exogenous saccades made to peripheral stimuli was influenced mainly by
cognitive anxiety.
As was alluded to earlier, the most troubling aspect of these findings is that they
are almost opposite of the results reported by Yoo (1996), who found that peripheral task

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performance was primarily impacted by cognitive anxiety levels and that arousal levels
accounted for very little variability peripheral task identification ability. However, upon
further scrutiny of Yoo’s (1996) data, it becomes evident that over the three sessions,
cognitive anxiety did not fluctuate. Furthermore, the values used for the median split
between high and low anxiety groups was not reported and the variability accounted for
by anxiety levels was quite small (R2= .136). Thus, these results should be interpreted
with caution.
With respect to driving performance, cognitive anxiety was strongly associated
with lap speed. Because the multidimensional nature of the anxiety response has not been
previously examined in the dual task situation and therefore changes in the central task
could not be attributed to specific predictor components, it is useful to compare these
results to single task scenarios where changes have been noticed. Perhaps most relevant
then, is Hardy’s work with the cusp catastrophe model (e.g., Fazey & Hardy, 1988;
Hardy, 1990; Hardy & Fazey, 1987; Hardy, Parfitt, & Pates, 1994; Parfitt, Hardy, &
Pates, 1995) .
To reiterate the central postulates of the model, when cognitive anxiety is low, the
model predicts that physiological arousal will influence performance in an inverted-U
fashion. However, when physiological arousal is high, high levels of cognitive anxiety will
result in lower levels of performance. Finally, when physiological arousal is low, higher
cognitive anxiety will lead to increases in performance. Therefore, cognitive anxiety is
projected to be the most influential determinant of performance changes at high levels of
arousal.

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In this study, high levels of arousal were exhibited during the competition session
and cognitive anxiety accounted for a significant amount of variability during this session.
Although these results do not provide concrete evidence for the specifications of the cusp
catastrophe model, they point to cognitive anxiety as the splitting factor with regard to
performance changes. Congruent with the predictions of the model, support is provided
for the notion that cognitive anxiety level appears to be the most salient predictor of
central task performance. Furthermore, as has been speculated by Hardy and others (e.g.,
Hardy, 1990; Hardy & Fazey, 1987; Hardy, Parfitt, & Pates, 1994), it appears as though
the decrease in performance noticed for two of the three groups during the high anxiety
stage was indeed quite catastrophic by both racing and normal driving standards.
Questions remain unanswered, however, with respect to the specific processes that
are impacted due to elevated cognitive anxiety levels. Because changes in performance
decrements were noticed not only in the dual task situation when distractors were present
but also in the single task situation, it is not clear what mechanisms are being affected. As
will be addressed later in this chapter, perhaps at high anxiety levels in the single task
scenario, participants become more internally rather than externally distracted by
worrisome thoughts and concerns (Moran, 1996).
Another goal of this investigation was to determine whether performance changes
under higher levels of activation were due to the perceptual alterations in visual selective
attention or other non-perceptual factors during the information processing of relevant and
irrelevant stimuli. In other words, how can the information processing differences that
occur under high levels of activation that lead to performance changes be explained?
Proposed was that changes in visual search patterns would offer some clues as to where

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these processing decrements occur, and would also provide some insight into the apparent
lack of effective cue utilization at high activation levels.
Specifically, if visual search pattern activity remained relatively stable in spite of
variability in proficiency levels of the central and peripheral tasks, it would be difficult to
attribute these changes to perceptual mechanisms and would implicate later stages of
processing (i.e., encoding, response selection). If, however, variability in performance
changed in a manner that corresponded with visual search patterns, it could be surmised
that different information was actually being gathered at the perceptual level (Abemethy,
1991). Thus, even if the information was being processed as efficiently as when one was
not exposed to high levels of anxiety, in essence, irrelevant information was being
processed.
In this study, differences indeed existed among groups with respect to visual
search patterns. In fact, at high levels of anxiety and arousal, the number of fixations to
the periphery and to distracting stimuli increased. As emphasized earlier, this provides
sound evidence for the idea that perceptual mechanisms are changed which predispose
performers to acquire and process irrelevant cues.
Relating this information to expert-novice studies dealing with visual search in
sport-type situations, findings are relatively consistent with the typical search patterns of
better performers across various sports. Overall, previous researchers have shown that
the best performers tend to exhibit less fixations of longer durations to the most relevant
areas of the display (e.g., Abemethy, 1988; Singer, Cauraugh, Chen, Steinberg, &
Frehlich, 1996; Vickers, 1996). Though expertise was not a factor of interest in the
present investigation, data indicate that the best driving performance was exhibited when

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visual search patterns were less variable and more focused on the most informative driving
cues. In contrast, the worst overall driving proficiency occurred when search patterns
were more erratic and drifted to irrelevant cues.
The observed relationships among visual search, anxiety/arousal level, and
performance also provide superficial support for the idea that visual search patterns are at
least somewhat reflective of attentional focus [Viviani’s (1990) notion of the “central
dogma” of visual search research]. As elaborated in the review of literature, since
Helmholtz first attempted to decouple attention from line of sight, researchers have been
concerned with the ability to pinpoint the direction of attention from eye movement data
(Abemethy, 1988; Viviani, 1990). With current measurement technology and paradigms it
is impossible to resolve this issue. However, although not a specified goal of this
investigation, it was hoped that the manipulations used would allow inferences to be made
concerning the ability to infer shifts of attention from shifts in gaze. A recap of the
primary criticisms directed toward visual search investigations follows.
Critics of the central dogma are quick to sequester support for the notion that
attention can be allocated to areas other than the foveal fixation point (e.g., Buckholz,
Martinelli, & Hewey, 1993; Davids, 1987; Remington, 1980; Remington & Pierce, 1984).
Abemethy (1988) and Viviani (1990) have noted that merely "looking" at visual
information does not necessarily equate with "seeing" (or comprehending) this
information. Thus, a person may fixate upon pertinent cues in the visual array, but there is
no guarantee that he or she is actually attending to or utilizing these cues.
According to Viviani (1990), three cognitive operations are inescapable when
exploring the world to solve a problem. These include (1) activation of a set of a priori

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beliefs about the possible states of the world, (2) breaking up the complex, holistic
hypothesis that normally regulates interactions with the world into hierarchy of simpler
alternatives, and (3) translating these alternatives into a sequence of locations in visual
space that will likely disambiguate each alternative. These criteria appear to be valid in
situations where eye movements are information driven, goal-directed behaviors, rather
than simple stimulus driven percepts. If in fact, however, the specific search path is
stimulus driven rather than goal driven (as was the case in this investigation), much of
Viviani’s arguments can be invalidated based on evidence that abrupt onsets of visual
stimuli are virtually impossible to ignore and undeniably provide information which was
not present in their abscence (e.g., Yantis, 1993).
Other criticisms levied by Viviani (1990) include the fact that because eye
movements move in sequential order, representing strictly serial behavior, one must
assume that the behavior viewed and corresponding thoughts unfold in sequential order.
He suggests, therefore, that the central dogma would be valid of it was known that a given
process unfolds sequentially. However, it is false whenever several concurrent processes
can be suspected, unless a theory is developed that describes how eye movements reflect
these processes.
He also contends that even if seriality can be assumed to be true, it is difficult to
identify the conditions in which it is proper to assume that the sequence of visual
operations actually conveys information. Support has been found for the notion that eye
movements tend to cluster around areas of high informativeness (Antes, 1974: based on
fixation clusters around comers) and this can be taken to support the central dogma. Other
evidence that the line of sight coincides, at least somewhat, with the shift of visual

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attention, is provided by investigations of the buffer capabilities of the brain and the lack
of support for the notion that buffers exist (Potter, 1983).
In this examination, procedures (i.e., the introduction of anxiety-producing stimuli)
were introduced to increase the ability to make inferences about attention from visual
search patterns. As mentioned, Viviani (1990) proposed that the central dogma of visual
search and cognitive inference would be more valid if evidence for serial search is
provided in particular tasks. According to Kahneman (1973), as arousal increases, task
difficulty also increases. Under these circumstances, parallel (relatively automatic)
processes tend to be modified by the organism, becoming more serial and attentive in
nature (Duncan & Humphreys, 1989; Shiffrin & Schneider, 1977). In this case (as was the
case in this investigation), the ability to relate eye fixations to attention and information
processing is more valid than when parallel processing is dominant. Furthermore, by
including abruptly presented peripheral, the ability to infer a level of informativeness that
did not exist previously is enhanced (Yantis, 1993).
Virtually all findings used to support the central dogma have been based on
paradigms in which gaze shifts are initiated in a bottom-up, stimulus driven, exogenous
manner (e.g., Just & Carpenter, 1976, 1980; Posner, 1980). In the exogenous context,
Jacobs (1986) and others have provided evidence that each saccade brings the eye to a
zone where new information can be gathered. However, once again, most evidence from
scan path observations can only be used as support for the stimulus driven properties of
eye movements.
I have suggested previously that the results of this investigation implicate the
perceptual stages of processing due to the observed differences in fixation location and

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exogenous saccades made to peripheral stimuli. More importantly, the shifting of gaze
from central to peripheral locations was strongly associated with detrimental performance
on the central driving task. Apparently, a shift in attentional resources away from the
central location and to the periphery led to a lack of information acquisition from central
locations. Admittedly, these findings do not support the notion that attention can be
undeniably inferred from the line of sight in an endogenous manner. However, they do
provide further substantiation for the role of gaze location on attentional focus in the
processing of exogenous information.
Another area of theoretical interest was the effect of elevated activation levels on
specific performance variables. In particular, by evaluating performance in terms of a
variety of accuracy, speed, and reaction time measures, a more complete understanding of
the separate elements of proficiency that are impaired or facilitated was ascertained. As
Jones and Hardy (1990) have suggested, the lack off attention to these specific
performance variables rendered it difficult, if not impossible, to prescribe interventions to
enhance them.
Measures of central task proficiency were affected in a similar manner. As has
been addressed frequently in this discussion of the data, lap speed was closely related to
the number of major errors for all groups except the central anxiety group. However,
after observing the minor error rate, it becomes evident that the effects of anxiety in single
task scenarios seemed to be somewhat different than in dual task situations. Without
having dichotomized error information into subcategories, these subtle differences would
not have been noticed.

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Similarly, misidentifications and response time varied in a comparable fashion.
When combined with visual search information, however, it became clear that the changes
in miss rate and RT occurred due to the increase in saccadic and fixation activity to
peripheral areas. Once again, by including several behavioral measures as performance
variables, it was possible to obtain a more complete picture of the influence of emotions
on these factors.
Practical Implications
Practically speaking, this investigation provides several possible explanations for
performance failures at high levels of anxiety and arousal both in the context of driving
and other situations demanding continually quick and accurate responses. Though merely
a simulation, the findings from this investigation give an indication of the manner in which
excessive driving demands (such as heavy traffic, being “cut off’, or near accidents) which
increase the level of activation of drivers will negatively affect their attentional abilities.
Furthermore, the impact of attentional abilities on the central task of driving the car
(accelerating, braking, and steering) as well as the ability to detect and effectively process
peripheral information were clarified.
Results are relatively unambiguous with regard to peripheral task performance.
That is, performance on the peripheral tasks changed as expected from session to session
as anxiety and arousal levels increased. From a practical point of view, one could surmise
that under anxiety-producing circumstances, drivers (both race car drivers or otherwise)
and athletes involved in sports requiring reactive capabilities may be more susceptible to
missing important cues in the periphery and tend to be more distracted by irrelevant cues.

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Similarly, in anxiety producing situations, drivers could be expected to respond less
quickly to relevant cues and have a more difficult time ignoring distractors.
If this information is applied to actual driving situations, several possible inferences
can be made. It can be expected that because drivers must consistently detect and respond
to information in the periphery, their ability to do so will be inhibited under higher levels of
activation. Consequently, important information that occurs outside the UFOV (such as
warning signs, other cars, pedestrians, and the like) will not be noticed at all or will be
responded to in a delayed fashion. The consequences of these performance changes vary
on a continuum from inconsequential (if, in fact they have no bearing on driving or
drivers’ safety) to devastating (hitting a pedestrian or not avoiding a collision).
Practically speaking, the implications of the results of the central driving task for
the everyday driver are somewhat different than for the race car driver. For the casual
driver, perhaps the most relevant data here deal with the impact of higher activation levels
on error rate. Although many drivers frequently drive under time constraints in which the
faster they drive the better, the typical objective when riding to work, school, and
extracurricular activities is not to see how fast the destination can be reached. Rather the
usual objective is to arrive safely. Thus, error rate information is critical in this context.
Findings clearly indicate that a majority of errors occurred at highest individual
activation levels. In this vein, evidence is provided that drivers may be more accident
prone under stressful conditions, an observation that is supported by related research (e.g.,
Miura, 1990). An awareness of this fact by the everyday driver could prove to be critical
in mediating the emotional response to typical anxiety producing roadway occurrences
such as time constraints, poor driving conditions, inconsiderate drivers, near accidents,

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and the presence of law enforcement officials. Based on the results of my research and
other studies, recommendations include increasing the awareness of attentional problems
that occur under stressful driving conditions, and encouraging the use of arousal
regulation strategies to mediate these effects.
In the context of race car driving, not only is the safety of the driver critical, but
the maintenance of high speed becomes of paramount importance. In order to drive
efficiently and safely while monitoring other factors such as engine temperature, tire
pressure, fuel levels, and other competitors, the driver must possess a high degree of
attentional flexibility. Furthermore, the ability to extend this high level of concentration
throughout the duration of a 4-hour race necessitates the ability the keep attention focused
in the most relevant, information rich areas of the environment at all times. Any possible
maladaptive influence such as that produced by emotional concerns must be minimized.
Creating an awareness of the impact of anxiety on attentional processes is the first step in
helping drivers to regulate thoughts and emotions more effectively, leading to higher
performance.
Summary
The purpose of this investigation was to examine the influence of distraction on the
attentional narrowing construct in the context of a dual task driving simulation under
varying levels of anxiety. Forty-eight women were randomly assigned to one of six
experimental conditions: distraction control, distraction anxiety, relevant control,
relevant anxiety, central control, and central anxiety. Those assigned to central
conditions only performed a driving task while the other four groups were required to
identify peripheral lights in addition to driving. Those in anxiety conditions were exposed

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to increasing levels of anxiety as manipulated by instructional sets. All participants
completed three sessions consisting of 20 trials each during which measures of cognitive
anxiety, arousal, visual search patterns, and performance were recorded.
Data indicated that as individuals in dual task conditions reached higher levels of
anxiety, their ability to identify peripheral lights became slower and less accurate.
Furthermore, driving ability for those in the distraction and central groups was impaired
at high levels of anxiety. The decrease in driving proficiency for the distraction anxiety
group was highly associated with a shift in visual search patterns toward peripheral
locations. With respect to the central anxiety condition, driving proficiency was heavily
influenced by an increased tendency to make minor errors which could be attributed to a
more cautious driving style when highly activated. Overall, performance in both central
and peripheral tasks was worse for the distraction anxiety group during the period of
highest anxiety. Furthermore, visual search patterns tended to increasingly drift to
peripheral areas during the high anxiety session for this group.
Results suggest that drivers who are highly anxious and aroused experience an
altered ability to process peripheral information at the perceptual level, leading to a
decrease in attentional resources available for the processing of central information. In
addition, it appears that this effect is amplified when distractors as well as relevant cues
are present in peripheral areas. Implicated in the study is the role of visual search patterns
and distractors in the dual task context. Suggestions are made to revise the current notion
of attentional narrowing to include the role of distraction as a contributor to performance
variability.

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Conclusions
Based on the findings of the study, the following conclusions are made:
1. With increases in anxiety and arousal to high levels, performance in peripheral
tasks tends to be detrimentally affected due to a narrowing of the attentional field. When
distractors are added to the peripheral task environment, this effect is compounded and
appears to alter visual search patterns. The alteration in visual search patterns in which
more fixations are directed toward peripheral areas of the display is associated with a
debilitative effect on central task performance.
2. The central tenants of the attentional narrowing concept should be revised to
incorporate the role of distraction in performance changes. The results of this study do
not contradict the attentional narrowing phenomenon, but add a dimension to it that
provides further explanation for the influence of attentional changes on task proficiency.
3. From a practical point of view, driving instruction personnel, trainers, and sport
psychologists must be aware of the possible impact of anxiety and arousal on attentional
abilities. A need exists to create awareness of the reasons for the detrimental effects of
anxiety and arousal on performance among pilots, casual drivers, auto racers, athletes, and
a multitude of other occupations and trades. By combining this awareness with cognitive,
self-regulatory strategies such as those used by the most effective and safest participants in
various sports and activities, individuals will develop the ability to be better prepared to
deal with emotional circumstances when they arise.

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Issues for Future Consideration
Several issues remain unresolved in spite of this comprehensive investigation, and
others have arisen during the course of collecting these data that will provide fruitful areas
of research in the future.
1. This study was the first in which the influence of distractors were investigated
in the context of an attentional narrowing dual task paradigm, and one of the first in which
the impact of activation changes on visual search patterns were examined. Because it is a
seminal investigation, replication of these results would add to the predictive value of the
data as well as an understanding of whether or not the findings are generalizable to other
situations and populations. With this in mind, it would be of interest to examine this
paradigm with athletes in other high speed reactive sports. Furthermore, perhaps future
investigations could be directed toward assessment of attentional cue utilization and
distraction in self-paced, closed sports. Finally, due to the decline in perceptual skills and
RT in elderly drivers, combined with the increasing fear of driving that often occurs with
age, studies could be undertaken to understand more thoroughly whether safety is being
compromised in older populations with regard to the measures recorded in my
investigation. These findings could also be used to develop comprehensive driving tests
which are based not only on static but also dynamic visual acuity.
2. One possible limitation in the present study was the use of young, relatively
inexperienced female participants. It would be interesting to see the effect of performer
expertise on the variables of interest in this investigation. This could be accomplished in
two different ways. First, it would be highly desirable to have access to some of the best

197
drivers in the world and to examine whether their search patterns and performance
changes are similar to those observed for lower skill-level drivers. Alternatively,
participants (either aspiring professional drivers or “normal” drivers) could be trained to
certain criterion levels and then exposed to similar instructional sets and anxiety
manipulations that were used in this investigation to examine attentional changes across
varying degrees of expertise.
As mentioned previously, one of the practical implications of this research is the
establishment of attentional training protocols that are based on the profiles exhibited by
the safest and most efficient performers. The expert-novice paradigm has proved useful in
the study of visual search differences in other sport domains (e.g., Abemethy, 1990;
Goulet, Bard, & Fleury, 1989; Helsen & Pauwels, 1990; Shank & Haywood, 1987;
Williams, Davids, Burwitz, & Williams, 1994). The results of my investigation provide an
indication of the visual search patterns associated with varying proficiency levels.
Correspondingly, one would expect that expert visual search patterns would be consistent
with those observed for individuals who performed at a high level in my investigation.
However, without explicitly testing experts, it remains unknown whether these patterns
are similar to those exhibited by the best performers.
3. A disappointing finding in this investigation was the lack of clarification
provided with respect to the relationship between various components of the stress
response and their influence on both central and peripheral tasks. Because both arousal
and anxiety levels varied similarly in the anxiety groups, it was difficult to observe the
effect of a change in anxiety independent of a change in heart rate - they occurred
relatively simultaneously. If, in fact, anxiety and heart rate scores varied in a dissimilar

198
fashion, a more valid identification of which aspect of the stress response contributed most
to changes in performance of both tasks would have been possible. Perhaps future
researcher attempts should be made to provide instructions that are geared toward
manipulating one aspect of the stress response independently of the other as Hardy and his
colleagues have done in past studies of the cusp catastrophe model (Hardy, 1996).
Furthermore, although cognitive anxiety levels increased significantly in this
investigation from Session 1 to Session 3, they did not approach maximum scores, even in
the high anxiety conditions. These findings are relatively consistent with studies in which
anxiety has been manipulated (e.g., Parfitt et al., 1995), but fall well short of others in
which the cusp catastrophe model has been of interest (e.g., Hardy et al., 1994).
Accordingly, it would be of interest to examine whether even higher levels of cognitive
anxiety (and arousal) influence performance in a similar (and possibly more dramatic)
fashion to that demonstrated in my study.
4. Though the results of the study were relatively clear with regard to the impact
of arousal and anxiety on the central task, it is difficult to pinpoint the underlying cause of
this relationship when there was no performance decrement exhibited by the relevant
anxiety condition at high levels of activation. As mentioned, central task performance
changes have been rather equivocal in the study of attentional narrowing. Perhaps there
are other, uncontrolled variables which render the affect of anxiety as positive or negative,
but have not been elaborated thus far.
It has been proposed that one of the primary influences or mediators of the
anxiety-performance relationship could be self-confidence (Martens et al., 1990). The
underlying assumption to this argument is the idea that although anxiety may increase, if

199
the performer maintains a high level of self-confidence, the rise in anxiety levels will be
facilitative to performance. In the context of my investigation, this may, indeed have been
the mediating factor that led to performance facilitation for the relevant anxiety group
even as they achieved high levels of anxiety. One would expect that if, in fact, self-
confidence does play a major role in the effects of anxiety on performance, the detrimental
consequences of attentional narrowing and distraction would be minimized.
It would also be valuable to examine more closely why there was a change in the
single task only situation. Proposed was that this may be due to an increase in internal
distraction as opposed to external distraction (Moran, 1996). The paradigm used in this
investigation only permitted empirical testing of external distraction. However, it is
logical to suggest that because there are fewer variables to consume attentional resources
in a single versus a dual task scenario, those resources may be directed more internally to
the anxious feelings themselves.
Perhaps the degree of internal distraction could be reflected by the level of somatic
anxiety. By definition, somatic anxiety is associated with the perception of arousal
(Martens et al., 1990). If somatic anxiety levels remain high, this could be an indication
that arousal has become more salient to the participant, and that attentional resources have
been diverted to perceiving these physiological signs. In this respect, attentional resources
would be reduced for the processing of task-relevant cues and performance would be
expected to decrease.
5. Another possible improvement to the present study would be a comparison of
the simulation used here to the actual auto racing environment. Although simulations tend
to provide an environment that is similar to the actual one, what is gained in terms of the

200
ability to control the testing situation may be compromised by a loss of pertinent data.
One of the intentions of this study was to test the constructs of interest in an ecologically
valid environment. This was accomplished to a large extent by the use of a simulation.
However, other factors such as the g-forces exerted on the drivers, the lack of
communication between crew chief and driver, and the real competitive and life-
threatening stimuli that are part of the actual racing environment were not included in this
investigation.
Perhaps these other factors play a significant role in determining driving
performance beyond that which was evidenced in my study. By including other factors
that require attentional focus and flexibility, a more complete understanding of the
attentional narrowing phenomenon and practical ramifications of this phenomenon to the
actual racing environment may be obtained. Though instrumentation would be virtually
impossible at the present time, this may be a possibility in the future with the development
of lightweight, highly compact, and portable eye movement systems.
6. Another area of concern is the possibility that by notifying participants that a
distractor may be present, attention may have been more inclined to be drawn to irrelevant
cues. Wegner (1994, 1997) has suggested that people tend to exhibit what he terms
“ironic mental processes” when confronted with a variety of information. The central
notion to Wegner’s theory is that when trying to avoid thinking about a particular thought
or object, or ignoring it, attention is inadvertently drawn to the exact thing one is trying to
ignore.
With regard to the practical applications of this research, I have advocated an
awareness strategy as one that would be most beneficial to performers. That is,

201
individuals should become aware of the attentional changes that occur when highly
emotional as a first step in overcoming the negative consequences of these changes. In
light of Wegner’s ideas however, perhaps more mainstream awareness interventions
should be tempered with non-awareness strategies and “paradoxical interventions” that
free the mind from distraction. These ideas have far-reaching implications to many
traditional sport psychology interventions used presently. Investigation of this
phenomenon in the sport domain could provide valuable insight to the role of (especially)
internal distraction not only in sport but in other domains as well.
7. Finally, though differences were found among several visual search parameters,
others could have been included in the analysis which may have provided valuable insights.
For example, measures such as the total number of fixations, how many different areas
were fixated, what part of the UFOV was fixated, and the like, could provide clues as to
where perceptual differences exist as performance changes. Including these variables in
future studies could be of value in continuing to unravel some of the mysteries of the
attentional narrowing phenomenon and the impact of distraction on performance.
Final Comment
In conclusion, this study addressed a critical issue in the broad realm of health and
human performance: The necessity of attending to and processing relevant information in
an efficient manner. Without this capacity, human performance is greatly compromised in
a variety of different environments. Furthermore, the ability to excel, especially in the
sport context, is highly dependent on the continual refinement and appropriate application
of these attentional skills. Previous evidence has suggested that when placed in stressful
environments, attentional ability of participants is negatively modified by a narrowing of

202
the attentional field. The present results indicate that although this may be the case, the
influence of anxiety on attention is also determined by distraction. On the basis of this
evidence, I conclude that individuals in situations which require the continuous monitoring
of both central and peripheral cues should be aware of the influence of anxiety on
attention, and take appropriate measures to reduce the potentially devastating
consequences of ignoring these attentional alterations.

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APPENDIX A
COMPETITIVE STATE ANXIETY INVENTORY - 2 (CSAI-2)
Name:
Racing Self-Evaluation Questionnaire
Sex: M F Date:
Directions: A number of statements which athletes have used to describe their feelings
before competition are given below. Read each statement and then circle the appropriate
number to the right of the statement to indicate how you feel right now - at this moment.
There are no right or wrong answers. Do not spend too much time on any one statement,
but choose the answer that describes your feelings right now.
Not At All
1. Iam concerned about this competition 1
2. I feel nervous 1
3. I feel at ease 1
4. I have self-doubts 1
5. I feel jittery 1
6. I feel comfortable 1
7. Iam concerned that I may not do as
well in this competition as I could. . 1
8. My body feels tense 1
9. I feel self-confident 1
10.1 am concerned about losing 1
11.1 feel tense in my stomach 1
12.1 feel secure 1
13.1 am concerned about choking under
pressure 1
14. My body feels relaxed 1
15. I’m confident that I can meet the
challenge 1
16. I’m concerned about performing poorly 1
17. My heart is racing 1
18. I’m confident about performing well. . . 1
Somewhat
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
Moderately
So
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
Very much
So
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
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224
19. I’m worried about reaching my goal. ... 1 2
20.1 feel my stomach sinking 1 2
21.1 feel mentally relaxed 1 2
22. I’m concerned that others will be
disappointed with my performance. . . 1 2
23. My hands are clammy 1 2
24. I’m confident because I mentally picture
myself reaching my goal 1 2
25. I’m concerned that I won’t be able to
concentrate 1 2
26. My body feels tight 1 2
27. I’m confident of coming through under
pressure 1 2

APPENDIX B
INFORMED CONSENT FORM
University of Florida
Department of Exercise and Sport Science
Informed Consent
Project Title: Visual Search Processes During a Simulated Racecar Driving Task
Principal Investigators: Janelle, Christopher M., Graduate Student; Robert N. Singer, PhD.,
Professor and Chair, Department of Exercise and Sport Sciences, FLG 100, Phone: (352) 392-
0584
This is to certify that I , hereby agree to participate as a volunteer in this scientific
investigation as part of an authorized research program at the University of Florida, under the
supervision of Robert N. Singer.
Purpose and Testing Procedures
The purpose of this study is to investigate the visual search patterns of participants as they
perform a simulated driving task. A contrived competition will be derived in which subjects
will compete with others on two separate test sessions. Upon arrival at the Motor Behavior
Laboratory for testing, participants will be outfitted in visual search collection instrumentation
and a heart rate monitor and then complete a short questionnaire that evaluates how they are
feeling at that moment. They will then complete 20 laps on the simulated race course. The
experiment will take approximately one hour per session for a total of two hours.
General Information
(a) The principal investigator will answer any of my questions about the research
project and my rights as a volunteer subject.
(b) There is no more than minimal risk to my health and well being.
(c) I will receive experiment participation credit in the amount of 10 extra credit points which
amounts to 1.25% of my final grade for volunteering.
(d) I am free to withdraw my consent and to terminate my participation at any time.
(e) If there are questions I do not wish to answer, I do not have to answer them.
225

226
(f) My data and answers to any questions will remain confidential to the extent provided by
law. My identity will be withheld from data files, sheets, and analyses because a
number coding system will be used. Only grouped data will be reported in any future
publication.
(g) Questions or concerns about my rights as a participant can be directed to the UFIRB
office, Box 112250, University of Florida, (352) 392-2556
I have read and /or discussed the procedure described above and I understand the
procedure. There is no anticipated risk to participating in the experiment. I voluntarily agree to
participate in the procedure and I have received a copy of this description.
Signature of Participant Date Age
Signature of Witness Date Age
I have defined and fully explained this study to the above named subject:
Signature of Principal Investigator Date

APPENDIX C
PRE-RACE INSTRUCTIONS
Please have a seat in the driver’s chair and I will give you some preliminary
instructions that will help you in learning how to drive the race car. Keep in mind that this
is a difficult task so please do not get frustrated or discouraged but continue to try your
best to get the hang of it. The steering wheel controls the direction of the car while the
foot pedals you see mounted on the floor control the braking and acceleration functions of
the race car. Just as in your car, the accelerator is on the right side and the brake is on the
left.
You will be driving on an exact scale representation of the Michigan International
Speedway. As you can see, it is an oval track and you will be going around the track to
the left. Once again, your perspective will be as if you are sitting in the driver’s seat of the
race car. In front of you will be a dashboard with a variety of instruments. Of these
instruments, the only one you will have to pay attention to is the speedometer which will
be a digital readout at the bottom of the screen. When the race begins, you will be in Pit
Row and your car will be rolling forward so do not bother hitting the accelerator. The
maximum speed you can go through the Pit area is 80 mph and the computer will
accelerate you that fast so there is no need to hit the gas pedal.
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228
It is important to monitor your speed as you will want to maintain different speeds
as you approach different parts of the track. The car can go up to 250 mph. However,
when going around the curves, try to keep it around 200 mph. If you go much faster, the
car will have a tendency to run into the outer wall. However, you’ll notice that the turns
are banked (meaning they’re sloped to hold the car on the track at high speeds). For this
reason, you do not want to go too slow as the sloped turns will tend to force the car down
toward the infield. You can go as fast as you like down the straight-aways. However,
keep in mind that you will have to slow down going into the turns.
One thing that will inevitably happen is that you will wreck. First of all, do not get
discouraged when you wreck because everyone has. Second, there are a few things you
should remember in order to recover from the wreck and begin racing again. Specifically,
you will probably end up in the infield and possibly facing the wrong direction after a
wreck. If this is the case, the computer will turn the car around so you are facing the
correct direction and it will also start the car rolling again. Your only responsibility will be
to gradually steer the car back onto the track and do so very gradually. Because the tires
on these cars have no treads, driving on the infield grass is like driving on ice and if you
turn too sharply this will probably result in another wipe out. One critical thing to
remember at all times is never hit the accelerator (gas pedal) until the computer has
accelerated the car up to 80 mph or more. If you do, it is very difficult to control the car.
Another important factor to remember is that the steering wheel is very sensitive so a
small turn of the wheel results in large turns of the car, especially when the car is going
upwards of 200 mph.

229
You will be completing 20 laps around the track and will do so 5 at a time.
Therefore, after you complete 5 laps, I will stop the race, check the HRM and then restart
it. You will do this 4 times and then be finished with this session. Your objectives are to
drive as fast as possible while minimizing the number of errors you make. An error
constitutes any time you hit the wall, run into another race car, or run over the white line
and into the infield.

APPENDIX D
FAMILIARIZATION SESSION INSTRUCTIONS
General Instructions. During this experiment you will be completing a study that
will be used to understand the visual search patterns of people as they drive an
automobile. Once seated at the driving apparatus, you will complete a driving task while
wearing visual search monitoring equipment. In total, you will complete 60 laps around
the simulated race track. These 60 laps will be divided among 3 sessions. Therefore, you
will complete 20 laps per session. During each session, you will complete 5 laps, then
after a short break, you will complete 5 more, and so-on until you complete 20 laps.
Anxiety Group. The first session, which you will complete today, is a
familiarization session so that you can become accustomed to the driving apparatus and
experimental conditions. The next time you come in you will have one more practice
session and then a competition session. The practice session will be similar to the
familiarization session in that your scores will not be counted toward your overall
performance but will be recorded so we can make comparisons with other drivers. It will
be the last opportunity to practice before the competition session.
Immediately following the practice session, you will complete a competition
session in which your scores will be recorded and used to judge your performance against
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231
other drivers. It is in your best interest to perform as well as possible during the
competition session as you can win $50.00 if you are the best driver. Your performance
will be determined through an equally weighted calculation of driving proficiency (as
determined by lap speed and driving errors) [and for peripheral tasks] and peripheral light
detection accuracy and speed. Therefore, try to drive as fast as possible without going off
the track [and identify the lights as quickly and accurately as possible].
This is a familiarization session. Remember your goal is to drive as fast as possible
without running into the walls, off the track, or into other cars. [(If performing the
peripheral task) Also, remember that you must identify as quickly and accurately as
possible, the presence of the red light by responding with the word “light”. Your
performance score will be determined by a combination of your driving speed and errors
as well as how quickly and correctly you identify the lights. As both tasks are equally
important to your overall score, you should try to perform both tasks as well as possible].
Though your score will not be included in the final standings, it will be evaluated by the
experimenters and used to determine your eligibility for the $50.00 award. Remember,
this is the last opportunity you will have to practice before the actual competition session.
Are there any questions?
Control Groups. This is the first of three driving sessions. Remember your goal is
to drive as fast as possible without running into the walls, off the track, or into other cars.
[(If performing the peripheral task) Also, remember that you must identify as quickly and
accurately as possible, the presence of the red light by responding with the word “light”.
Your performance score will be determined by a combination of your driving speed and
errors as well as how quickly and correctly you identify the lights. As both tasks are

232
equally important to your overall score, you should try to perform both tasks as well as
possible]. Remember, please try to perform the (two) tasks to the best of your ability. Are
there any questions?

APPENDIX E
PRACTICE SESSION INSTRUCTIONS
Anxiety Group. This is a practice session. Remember your goal is to drive as fast
as possible without running into the walls, off the track, or into other cars. [(If performing
the peripheral task)] Also, remember that you must identify as quickly and accurately as
possible, the presence of the red light by responding with the word “light”. Your
performance score will be determined by a combination of your driving speed and errors
as well as how quickly and correctly you identify the lights. As both tasks are equally
important to your overall score, you should try to perform both tasks as well as possible].
Though your score will not be included in the final standings, it will be evaluated by the
experimenters and used to determine your eligibility for the $50.00 award. Remember,
this is the last opportunity you will have to practice before the actual competition session.
Are there any questions?
Control Groups. This is the second of three driving sessions. Remember your goal
is to drive as fast as possible without running into the walls, off the track, or into other
cars. [(If performing the peripheral task) Also, remember that you must identify as quickly
and accurately as possible, the presence of the red light by responding with the word
“light”. Your performance score will be determined by a combination of your driving
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234
speed and errors as well as how quickly and correctly you identify the lights. As both
tasks are equally important to your overall score, you should try to perform both tasks as
well as possible]. Remember, please try to perform the (two) tasks to the best of your
ability. Are there any questions?

APPENDIX F
COMPETITION SESSION INSTRUCTIONS
Anxiety Groups. As you know this is the competition session which you have
been practicing for. Your performance thus far has been relatively good -putting you in
contention for the $50.00 reward. As you can see, we have plotted your average score in
comparison to the other participants. Though close, you are slightly behind the leader at
his point. You will have to run a virtually flawless race in order to win the prize money,
but given your performance thus far, that is possible. Remember, the amount of extra
credit that you receive for participating in the experiment depends on your performance in
the competition session.
Also. I just received a call from the Discovery Channel and they were wondering if
I could send them a tape of the experimental setup and an actual participant in the study.
We routinely have requests for tapes and given your high performance, I was wondering if
you would mind being taped as you drive.
Remember your goal is to drive as fast as possible without running into the walls,
off the track, or into other cars. [(If performing the peripheral task) Also, remember that
you must identify as quickly and accurately as possible, the presence of the red light by
responding with the word “light”. Your performance score will be determined by a
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236
combination of your driving speed and errors as well as how quickly and correctly you
identify the lights. As both tasks are equally important to your overall score, you should
try to perform both tasks as well as possible]. It is imperative that you do your best on
both the driving and light identification tasks so Discovery has some interesting and useful
data to report for the special, and so you receive credit for participating. Any questions?
Control Groups. As you know, this is the third and final session. Remember your
goal is to drive as fast as possible without running into the walls, off the track, or into
other cars. [(If performing the peripheral task) Also, remember that you must identify as
quickly and accurately as possible, the presence of the red light by responding with the
word “light”. Your performance score will be determined by a combination of your
driving speed and errors as well as how quickly and correctly you identify the lights. As
both tasks are equally important to your overall score, you should try to perform both
tasks as well as possible]. Please do your best on both the driving and light identification
tasks. Any questions?

APPENDIX G
POST-EXPERIMENT COMMENTS
Relevant Anxiety
Knowing that I was actually competing against others made me feel more nervous because
it made me want to do better and I was hoping I wouldn’t crash moreso than if it was only
an experiment.
Losing the extra credit points or not getting as much made me want to win more
They made me try harder and concentrate on doing my best.
Made me more nervous.
The competition was my main concern.
Made me more nervous and anxious and I wanted to do well. Before that I really didn’t
care.
Made me feel more competitive, but more nervous. I felt those factors helped me know I
could do well.
Central Anxiety
I was nervous, shaking, and concentrating much harder.
They screwed me up - unconsciously.
They put more pressure on me and I felt dumb after - no big deal though.
I tried to convince myself “I’m not anxious” - and I usually felt mentally that I was doing a
good job, but often my performance showed otherwise. The more I think about it, often
the worse I perform.
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238
Made me more anxious to do well - I’m very competitive by nature.
The more I was told, the more pressure I felt.
I felt more pressure to do well.
I became a lot more focused on how I was driving, trying not to make as many mistakes
and avoiding ones I had already made. I became a more tense during the actual
(competition) driving part but not before. Even though I was tense, I really began to
enjoy driving more because I was more focused.
Distraction Anxiety
I felt pressure and stress.
Worked harder to get a better score.
These factors increased my nervousness and desire to do well once I sat in the driver’s
chair.
The part about not getting all of the extra credit points made me nervous, especially since
I crashed at the end.
I felt more attention would be directed to the last session, and it affected my concentration
in a negative way.
Made me try harder to better my driving and light perception.
They made me feel more insecure and unfocused at the wheel. Besides just thinking about
the lights and the driving, I also had these other factors on my mind.

APPENDIX H
PEARSON PRODUCT-MOMENT SIMPLE CORRELATION COEFFICIENTS

SPEED1
SPEED2
SPEED3
MINOR1
MINOR2
MINOR3
MAJOR1
MAJOR2
MAJORS
EX1
EX2
EX3
SPEED1
1
.69**
.26
-.22
-.44**
-.17
-.78**
-.51**
-.18
.04
.11
.14
SPEED2
1
.41**
-.27
-.46**
-.13
-.40**
=.82**
-.26
-.15
.06
.15
SPEED3
1
-.13
-.32*
-.34*
-.05
-.17
-.76**
-.32
-.33
-.35*
MINOR1
1
.54**
.49**
.21*
.51**
.26
-.32
-.21
-.29
MINOR2
1
.69**
.27
.58**
.33*
-.13
-.12
-.15
MINOR3
1
.22
.27
.45**
-.17
-.18
-.17
MAJOR1
1
.38**
.13
-.25
-.27
-.26
MAJOR2
1
.22
-.09
-.18
-.27
MAJOR3
1
.07
.18
.21
EX1
1
.70**
.65**
EX2
1
.95**
EX3 1
to
O
Variable Names:
SPEED = Lap speed
MINOR = Minor errors
MAJOR = Major errors
HRCHANGE = Heart rate change
ANXIETY = Anxiety level
EX = Exogenous saccades
MISS = Misidentifications
RT - Response time

HR HR HR MISS1
CHANGE1 CHANGE2 CHANGE3
SPEED1
.16
.08
.11
-.05
SPEED2
.29*
.29*
.26
.24
SPEED3
.21
-.11
-.18
-.03
MINOR1
-.09
-.07
-.02
.11
MINOR2
-.14
-.08
-.13
.08
MINOR3
-.03
.01
.05
.21
MAJOR1
.05
.04
.03
.17
MAJOR2
-.09
-.21
-.18
-.21
MAJOR3
-.09
.17
.18
.28
EX1
-.24
-.19
-.13
.10
EX2
-.22
.14
.27
.12
EX3
-.16
.25
.37*
.18
HRCHANGE
1
HRCHANGE
1
.43**
.34*
-.01
1
.86**
.34*
2
HRCHANGE
1
.23
3
MISS1 1
MISS2
MISS3
ANXIETY1
ANXIETY2
ANXIETY3
RT1
RT2
RT3
“Significant at the 0.01 level
* Significant at the 0.05 level
MISS2
-.10
.10
-.29
-.00
-.15
-.13
-.05
-.29*
.25
-.03
-.05
.03
-.07
.40*
.43*
.21
1
MISS3
ANXIETY
ANXIETY
ANXIETY
RT1
RT2
RT3
1
2
3
.14
.01
-.05
.00
.15
.24
.27
.10
.08
-.00
.07
.15
.02
.21
-.55**
-.08
-.22
-.33*
-.06
-.30*
-.34*
-.15
-.13
-.23
-.14
.02
.01
-.10
-.27
-.11
-.19
-.14
.03
-.11
-.23
-.25
-.23
-.24
-.01
.15
-.16
-.23
-.18
-.00
.06
.02
-.15
-.37*
-.35*
-.28
-.10
-.13
-.17
-.15
-.16
-.32*
44**
.02
-.02
.09
.26
.24
.28*
.14
-.06
-.05
.01
.23
.32*
.26
.34*
-.13
.01
.24
.31*
.42*
.52**
.43*
-.03
.14
.37*
.28
.37*
.56**
.00
.29*
.20
.23
-.26
-.32*
-.15
.48**
.12
.13
.35*
-.09
.08
.42*
.63**
.03
.17
.56**
-.11
.17
.53**
.19
-.32*
-.09
-.16
.69**
.38*
.41*
.61**
.23
.14
.24
.01
.30*
.46**
1
.26
.34*
.48**
.08
.51**
.75**
1
.66**
.26
-.45*
-.36*
-.12
1
.64**
-.16
-.13
.12
1
-.28
.02
.33*
1
.61**
.41*
1
.84**
1
N>

BIOGRAPHICAL SKETCH
Bom on May 5, 1969, in Boston, Massachusetts, Christopher Matthew Janelle was
raised by his parents, Jean and Fran Janelle. After graduating from St. Xavier high school
in Cincinnati, Ohio, Chris earned his Bachelor of Arts degree from Miami University in
1991. He then attended Springfield College to pursue his Master of Science degree in
sport psychology under the tutelage of Dr. Mimi Murray. Upon completion of this degree
in 1993, Chris enrolled in the College of Health and Human Performance at the University
of Florida as a Ph.D. candidate in motor behavior and was mentored by Dr. Robert N.
Singer. He completed his dissertation and was awarded the Ph.D. degree in August 1997.
242

I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
(Lf\a
Robert N. Singer,
Professor of Exercise and Sport
Sciences
I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
L. Keith Tennant
Associate Professor of Exercise and
Sport Sciences
I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor o