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Age Differences and Spatial Navigation in Novel Virtual and Real World Environments

Permanent Link: http://ufdc.ufl.edu/UFE0041348/00001

Material Information

Title: Age Differences and Spatial Navigation in Novel Virtual and Real World Environments
Physical Description: 1 online resource (72 p.)
Language: english
Creator: King, Emily
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: Clinical and Health Psychology -- Dissertations, Academic -- UF
Genre: Psychology thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: AGE DIFFERENCES AND SPATIAL NAVIGATION IN NOVEL VIRTUAL AND REAL WORLD ENVIRONMENTS Normal aging is associated with a decline in a number of cognitive abilities and numerous studies document the existence of age-related changes in human spatial cognition and behavior. Recent studies using virtual navigation paradigms have shown that performance on these tasks is correlated with performance on cognitive map-based way-finding tasks. While the use of virtual environments has made it possible to study navigation performance in a controlled setting, there is limited research that evaluates how performance on virtual navigation tasks translates to real-world allocentric navigation behavior. The broad aims of this study were to empirically evaluate changes in laboratory and real world navigation associated with normal aging and to help lay the foundation for establishing the ecological validity of virtual navigation tasks. Twenty-three healthy adults age 20-35 and twenty-seven healthy community-dwelling adults age 65 and older took part in this study. We used a 3-bedroom, 2-bathroom house to develop an ecologically valid navigation task that was based on theories of allocentric spatial navigation, as well as computer task modeled after the Morris water maze. We investigated group differences in navigation abilities and the relationship between performance on real world and computer-generated navigation tasks. Additionally, each participant completed a neuropsychological test battery. Consistent with previous findings, results from this study clearly demonstrated that overall, older adults do not navigate as effectively as younger adults in virtual or real world space. These data are consistent with theories that aging impairs the formation/retrieval of spatial maps of novel environments and spatial knowledge acquired from direct experience in the environment. Second, we were able to demonstrate the relationship between aging and poorer real world navigation performance was partially mediated by executive functioning. Third, while significant correlations exist between navigation in computer space and real space, results suggest that tasks with executive functioning demands are more powerful than computer navigation performance and age in predicting real world navigation. Overall, the present report provides additional evidence that adults 65 years and older demonstrate poorer performance on virtual and real world tasks of spatial learning and memory than do their younger counterparts. This group difference appears to be markedly influenced by executive functioning. As a result, age-related changes in executive skills should be taken in consideration in future studies of spatial cognition. These data also confirm the feasibility of using a real world navigation task in adults over age 65 and emphasize the importance of utilizing real world measures for accurate assessment of cognitive functioning.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Emily King.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Bauer, Russell M.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-02-28

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041348:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041348/00001

Material Information

Title: Age Differences and Spatial Navigation in Novel Virtual and Real World Environments
Physical Description: 1 online resource (72 p.)
Language: english
Creator: King, Emily
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: Clinical and Health Psychology -- Dissertations, Academic -- UF
Genre: Psychology thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: AGE DIFFERENCES AND SPATIAL NAVIGATION IN NOVEL VIRTUAL AND REAL WORLD ENVIRONMENTS Normal aging is associated with a decline in a number of cognitive abilities and numerous studies document the existence of age-related changes in human spatial cognition and behavior. Recent studies using virtual navigation paradigms have shown that performance on these tasks is correlated with performance on cognitive map-based way-finding tasks. While the use of virtual environments has made it possible to study navigation performance in a controlled setting, there is limited research that evaluates how performance on virtual navigation tasks translates to real-world allocentric navigation behavior. The broad aims of this study were to empirically evaluate changes in laboratory and real world navigation associated with normal aging and to help lay the foundation for establishing the ecological validity of virtual navigation tasks. Twenty-three healthy adults age 20-35 and twenty-seven healthy community-dwelling adults age 65 and older took part in this study. We used a 3-bedroom, 2-bathroom house to develop an ecologically valid navigation task that was based on theories of allocentric spatial navigation, as well as computer task modeled after the Morris water maze. We investigated group differences in navigation abilities and the relationship between performance on real world and computer-generated navigation tasks. Additionally, each participant completed a neuropsychological test battery. Consistent with previous findings, results from this study clearly demonstrated that overall, older adults do not navigate as effectively as younger adults in virtual or real world space. These data are consistent with theories that aging impairs the formation/retrieval of spatial maps of novel environments and spatial knowledge acquired from direct experience in the environment. Second, we were able to demonstrate the relationship between aging and poorer real world navigation performance was partially mediated by executive functioning. Third, while significant correlations exist between navigation in computer space and real space, results suggest that tasks with executive functioning demands are more powerful than computer navigation performance and age in predicting real world navigation. Overall, the present report provides additional evidence that adults 65 years and older demonstrate poorer performance on virtual and real world tasks of spatial learning and memory than do their younger counterparts. This group difference appears to be markedly influenced by executive functioning. As a result, age-related changes in executive skills should be taken in consideration in future studies of spatial cognition. These data also confirm the feasibility of using a real world navigation task in adults over age 65 and emphasize the importance of utilizing real world measures for accurate assessment of cognitive functioning.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Emily King.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Bauer, Russell M.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-02-28

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041348:00001


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AGE DIFFERENCES AND SPATIAL NAVIGATION IN NOVEL VIRTUAL AND
REAL WORLD ENVIRONMENTS




















By

EMILY GREEN KING


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

2010
































2010 Emily Green King

































To my husband, Adrian, for his absolute love and encouragement









TABLE OF CONTENTS

page

TA B LE O F C O NTE NTS ........................... .......... .................................... 4

LIST O F TA BLES ......... ................ ..................... ...... ............... 5

LIST O F F IG U R E S .................................................................. 6

A B S T R A C T .............. ...... ........... ................. ....... .. ....................................... 7

CHAPTER

1 INTRODUCTION ........... ......... ............ ......... 9
Specific Aim 1 ......................... ................ 22
Specific Aim 2 ............... ......... .................. 23
2 RESEARCH DESIGNS AND METHODS .................................. ....................... 24
Participants .................... ............. ............... 24
Experimental Procedures........................ ......... ........................ 25
Neuropsychological Screening and Spatial Cognition.................................. 26
Self-Report Environmental Spatial Ability and Computer Game Experience.... 27
House Navigation Task ..... .................................. ... .... .......... 28
Computer-Generated Arena ..... ..................... .................. 32
Data Reduction ................ ........ ................... 35
House Navigation Variables ................. ................ ............... 35
Computer Generated Arena Variables ........ ......... ....... ............... 36
Cognitive Test Variables............................. ............... 37
3 RESULTS ......... .......... ....... ......................... 39
Smart House Navigation Task Performance .............................. ... ................ 39
Smart House Navigation Task Performance and cognition........................... 45
C G A rena Perform ance ................................................................................ 46
Relationship between navigation in CG Arena Space and Real Space.......... 49

4 DISCUSSION ................. ......... ........................ ...... ........... 52

APPENDIX

C O G N IT IV E TEST BATTERY .................................................. ........................... 62

HOUSE NAVIGATION TASK ITEM LIST................ ........................ ............... 64

LIST O F REFERENCES ........... ................... ........................................... 65

B IO G RA PH IC A L S K ETC H ............. ...................................................... ............... 72









LIST OF TABLES


Table page

Table 2-1. Demographic characteristics of experimental participants by group ........... 25

Table 2-2. C ognitive Test Battery........... ................ .......................... ............... 27

Table 3-1. Demographic characteristics of old and old old groups.............................. 40

Table 3-2. Mean scores for HNT variables......... ......... ........ ... ................... .. 43

Table 3-3. Composite Scores by age group M (SD) ............................................... 50

Table 3-4. Correlation of composite scores by group....... .... .................................. 50

Table 3-5. Summary of multiple regression analyses examining contribution of CG
Arena, age, and FE on HNT performance. .................................... ................ 51









LIST OF FIGURES


Figure page

2-1 Gator Tech Smart House, Gainesville, Florida (HNT)...................................... 29

2-2 House Object Recognition Task (HORT)................................. ..................... 31

2-3 House Reconstitution Map Task (HRMT). ............................................... 31

2-4 Representations of the C-G experimental room ........................................... 34

2-5 Representation of target once it is successfully acquired .............. ............ 35

3-1 Mean HNT composite performance by group ............................................... 40

3-2 Mean HNT composite performance by group......... ........ ................. 41

3-3 Correctly identified HNT locations by group ................................................ 42

3-4 Correctly identified HNT items by group ........................ .. ...... ......... 44

3-5 HNT path length for trial 3 and for delayed recall .................... ............. 44

3-6 Mean CG composite performance by group ................................................. 47

3-7 Mean Group CG Arena composite performance ............... ..... ................. 48

3-8 Composite scores by age group ....... ..... ......... ...................... ............... 50









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


AGE DIFFERENCES AND SPATIAL NAVIGATION IN NOVEL VIRTUAL AND
REAL WORLD ENVIRONMENTS

By


Emily Green King

August 2010

Chair: Russell M. Bauer
Major: Psychology

Normal aging is associated with a decline in a number of cognitive abilities and

numerous studies document the existence of age-related changes in human spatial

cognition and behavior. Recent studies using virtual navigation paradigms have shown

that performance on these tasks is correlated with performance on cognitive map-based

way-finding tasks. While the use of virtual environments has made it possible to study

navigation performance in a controlled setting, there is limited research that evaluates

how performance on virtual navigation tasks translates to real-world allocentric

navigation behavior. The broad aims of this study were to empirically evaluate changes

in laboratory and real world navigation associated with normal aging and to help lay the

foundation for establishing the ecological validity of virtual navigation tasks.

Twenty-three healthy adults age 20-35 and twenty-seven healthy community-

dwelling adults age 65 and older took part in this study. We used a 3-bedroom, 2-

bathroom house to develop an ecologically valid navigation task that was based on

theories of allocentric spatial navigation, as well as computer task modeled after the

Morris water maze. We investigated group differences in navigation abilities and the









relationship between performance on real world and computer-generated navigation

tasks. Additionally, each participant completed a neuropsychological test battery.

Consistent with previous findings, results from this study clearly demonstrated that

overall, older adults do not navigate as effectively as younger adults in virtual or real

world space. These data are consistent with theories that aging impairs the

formation/retrieval of spatial maps of novel environments and spatial knowledge

acquired from direct experience in the environment. Second, we were able to

demonstrate the relationship between aging and poorer real world navigation

performance was partially mediated by executive functioning. Third, while significant

correlations exist between navigation in computer space and real space, results suggest

that tasks with executive functioning demands are more powerful than computer

navigation performance and age in predicting real world navigation.

Overall, the present report provides additional evidence that adults 65 years and

older demonstrate poorer performance on virtual and real world tasks of spatial learning

and memory than do their younger counterparts. This group difference appears to be

markedly influenced by executive functioning. As a result, age-related changes in

executive skills should be taken in consideration in future studies of spatial cognition.

These data also confirm the feasibility of using a real world navigation task in adults

over age 65 and emphasize the importance of utilizing real world measures for accurate

assessment of cognitive functioning.









CHAPTER 1
INTRODUCTION

Normal aging is associated with a decline in a number of cognitive abilities and

numerous studies document the existence of age-related changes in human spatial

cognition and behavior. These age-related changes include, but are not limited to,

visuospatial working and long-term memory (e.g., Park, Lautenschlager, Hedden,

Davidson, Smith, and Smith, 2007), the learning of novel environmental layouts (e.g.,

Kirasic, 1991), the learning of routes (Barrash, 1994), and abilities on mental rotation

and spatial visualization tasks (Hertzog and Rypma, 1991). One complication in

studying spatial cognition in aging humans is that many distinct constructs (e.g.

wayfinding, landmark knowledge, spatial cognition, sense of direction, mental rotation,

point localization, route-based learning) can fall under the general rubric of "spatial

abilities." Adding to the confusion, several different methods, including psychometric

tests, real-world natural environments, virtual environments, and self-report 'sense of

direction' questionnaires, are used to study these different constructs, and the

conceptual and psychometric relationships among these methods has not yet been fully

elucidated.

Navigating through familiar and novel environments in order to arrive at a

destination is a highly complex skill that draws upon basic abilities such as learning and

recalling the layout of the environment (mental visualization), visual perception

(detection of landmarks), spatial perception (determining the direction to take), and map

reading (Nadolne and Stringer, 2001). Two commonly described ways of learning the

layout of a novel environments are wayfinding (also referred to as cognitive mapping.

allocentric navigation, or environment-dependent navigation) and egocentric navigation









(also referred to as route learning, route following, or viewer dependent navigation).

Egocentric navigation has its foundations in route-based knowledge. In egocentric

navigation, the animal (i.e. rat, human) follows a predetermined series of directions and

turns with the goal of moving toward a specific targeted location. In contrast, allocentric

navigation relies on a viewer-independent, external perspective (a map-like or aerial

view) that is thought to allow direct access to a representation or memory of the overall

spatial layout (Shelton and Gabrieli, 2001). Clearly, the ability to remember the location

of important elements of the external world (e.g., food caches, locations of predators,

shelter) provides great adaptive and survival value. In humans, such abilities contribute

heavily to independent function in everyday environments (e.g. navigating unfamiliar

buildings, streets, and cities).

Understanding these abilities and their neuroanatomic substrates has been

advanced by extensive investigations, and there exists an abundant amount of literature

speculating how organisms form and retrieve cognitive maps of novel environments

(O'Keefe and Nadel, 1978). One of the most reliable tests used to test allocentric spatial

learning and navigation is the Morris Water Maze (MWM; Morris, 1981), a paradigm that

has been mainly used with rodents. In the MWM, rats are placed in a circular pool of

opaque liquid that contains a submerged platform that allows escape when the animal

finds it and climbs on to it. Surrounding the circular pool are four walls, each containing

a distinctive visual cue that may provide the animal information about relative platform

location. Healthy young rats learn to find the platform efficiently and accurately. In

contrast, rats with hippocampal damage show severe impairments in the ability to find

the platform when compared to sham-operated rats (Morris, Garrud, Rawlins, and









O'Keefe, 1982; Sutherland, Whishaw, and Kolb, 1983). Further, aged rats perform more

poorly on the MWM than do their younger counterparts (Wilson, Ikonen, McMahan,

Gallagher, Eichenbaum, and Tanilla, 2003). While age differences in rats have been

found in allocentric spatial learning skills (circular MWM), age differences in

performance have not been demonstrated on an egocentric spatial task (T shaped

MWM; Begaga, Cienfuegos, Rubio, Santin, Miranda, and Arias., 2001).

In humans, functional imaging and lesion studies suggest that the hippocampus

and associated structures are involved in spatial memory (e.g., Astur, Taylor, Mamelak,

Philpott, and Sutherland, 2002; Bohbot, Kalina, Stepankova, Spackova, Petrides, and

Nadel, 1998; Frakey, 2005). Specifically, a network of structures involved in navigation,

including parahippocampal and extrahippocampal regions has been identified. Using

fMRI, Astur and colleagues (2005) demonstrated bilateral BOLD signal changes in the

hippocampus when navigating a virtual radial arm maze. Kumaran and Maguire (2005)

found preferential engagement of the hippocampus, parahippocampal, restosplenial,

and posterior parietal cortices when participants navigated within their city on a

computer task. Lesion studies have shown that damage to the posterior parietal cortex,

hippocampus, and parahippocampal gyrus cause significant spatial impairment

(Barrash, 1998). Although both egocentric and allocentric navigation recruit common

networks of brain areas, allocentric wayfinding appears to be more sensitive to

hippocampal and parahippocampal function (see Roche, Mangaoang, and Cummings,

2005 for review) and posterior parietal regions appear to be critical for egocentric

navigation (Barrash, Damasio, Adolphs, 2000).









These findings are relevant to age-related changes in allocentric navigation in that

MRI and post mortem studies of normal individuals have revealed age related

decreases in brain weight and brain volume (Dekaban & Sadowsky, 1978, Hubbard &

Anderson, 1981, Good, Johnsrude, Ashburner, Henson, Friston, Frackowiak, 2001,

Resnick, Pham, Kraut, Zonderman, and Davatzikos, 2001). More specifically, age-

related losses in the hippocampus are significantly accelerated relative to gray matter

losses elsewhere in the brain (e.g. Jernigan, Archibald, Fennema-Notestine, Gamst,

Bonnere, and Hesslink, 2001). Given this data, atrophy of the medial temporal lobe

structures that is seen in aging may explain why older adults experienced diminished

spatial memory and could find it increasingly difficult to learn and navigate (find their

way) in unfamiliar environments.

Numerous age-related anatomical changes in the visual system including the lens,

pupil, and at the neural level, might also contribute to age differences in spatial abilities.

In fact, some researchers have claimed that these sensory changes are largely

responsible for a variety of cognitive impairments in older adults (Scialfa, 2002).

However, while there are a large number of age-related changes in sensory and

perceptual functioning, these changes are more likely to affect tests of attention,

memory, and learning, and not complex navigation (Park et al., 2007).

Adaptations of the MWM, or computer-simulated human analogues, have been

developed and results obtained from humans appear to be comparable to those

obtained from rats; older adult participants demonstrate impaired performance on this

task compared to young adults (e.g. Newman and Kaszniak, 2000; Moffat, Zonderman,

and Resnick, 2001). Driscoll and colleagues (2003) demonstrated age-related deficits in









a virtual MWM task that were associated with decreased hippocampal volume as

measured by structural MRI, as well as decreased hippocampal metabolite levels

(NAA/CRE ratios) as measured by proton magnetic resonance imaging spectroscopy. In

an fMRI study, Moffat and colleagues, (2006) found that hippocampal,

parahippocampal, and extrahippocampal regions (parahippocampal gyrus and

retrosplenial cortex) showed reduced activation in older adults when navigating a virtual

MWM task. More recently Moffat and colleagues (2007) demonstrated robust age-

related differences in virtual MWM performance that were associated with larger

hippocampal volumes (as measured by MRI) in young, but not old participants. High

performance was also associated with larger volume of the caudate nucleus and

prefrontal gray and white matter in both groups.

The research study described in this proposal utilizes a computer generated

adaptation of the MWM, called the Computer Generated Arena (CG Arena; Jacobs,

Laurance, and Thomas, 1997; Thomas, Hsu, and Laurance, 2001). Previous studies of

CG Arena suggest that data obtained from humans in this virtual environment are

analogous to those obtained from rats in the MWM. As in animals, normal subjects use

distal spatial cues to navigate to the target, and rearrangement of these cues produces

profound impairment in ability to navigate to the hidden target (Jacobs, Thomas,

Laurance, and Nadel, 1998). Subsequent findings from studies of the CG Arena with

clinical populations have demonstrated that patients who had suffered mild to moderate

TBI showed impaired performance when compared to normal matched controls

(Skelton, Bukach, Laurance, Thomas, and Jacobs, 2000) and older adults demonstrate

impaired performance when compared to younger adults. Specifically, younger adults









navigate to the hidden target in less time over trials and spend a greater proportion of

time in the correct quadrant during the probe trial than older adults (Thomas, Laurance,

Luczak, and Jacobs, 1999; Laurance, Thomas, Newman, Kaszniak, Nadel, and Jacobs,

2002). Data from an fMRI study measuring brain activation patterns and CG-Arena have

suggested that environmental learning and spatial memory are related to activation of

the right hippocampus in normal healthy controls (Thomas et al., 2001). Recently,

research in our lab provided additional evidence that older adults demonstrate poorer

performance on the CG Arena than do their younger counterparts (King, 2006). These

data confirm the feasibility of using the CG Arena task in an older population and speak

to the possible utility of using this measure in the clinical setting.

In clinical practice, neuropsychologists commonly measure "spatial ability," but

rarely engage the patient in actual navigational tasks of any sort. Computer-generated

environments (e.g. CG Arena) allow for navigation through space without losing

experimental control while preserving features of real-world environments that are

lacking in traditional neuropsychological tests. Virtual environment technology also

permits accessibility to more diverse populations who may otherwise be incapable of

participation (e.g., non-ambulatory individuals). While the claim has been made that

these methods are more ecologically valid than traditional tests, and translate into real

world abilities, such real-world correspondence has not been adequately established.

There have been a number of studies that have investigated the acquisition of spatial

knowledge in real-world situations such as streets (e.g. Titov & Knight, 2005), buildings

(e.g. Barrash, 1998; Delpolyi, Hamilton, Petropoulos, Yeo, Brooks, Baumgartner, et al.,

2007; Ruddle, Payne, and Jones, 1997) and towns (e.g. Maguire, Burke, Philips, and









Staunton, 1996). In one such study, investigation of navigation in a virtual and real-

world 2-level outlet mall, found that when compared to unexposed controls, both young

and old participants were able to transfer understanding of the spatial layout of a virtual

representation of the outlet mall to successful performance in the real world equivalent

(Foreman, Stanton-Fraser, Wilson, Duffy, and Parnell, 2005). In addition, younger

experimental participants remembered the location of specific targets better than did

older participants. While this study demonstrated virtual technology is not a barrier to

examining the ecological validity of computer navigation tasks in the elderly, there have

been few studies that examine the ecological validity of virtual human analogs of animal

models (e.g. virtual MWM) and even fewer that study environments that older adults

frequently encounter.

As the focus of assessment in the elderly population has shifted from a diagnostic

to a more functional approach (e.g. answering questions about living independently,

making financial decisions, driving, or continuing at work), some considerable problems

with the generalizibilty of the testing situation to the real world have come to light. These

include, but are not limited to the sterility of the testing environment, the obstruction of

compensatory strategies, non-cognitive factors that may interfere with testing

performance, and the abilities that the tests are designed to measure might not

represent skills that are used in everyday functioning (Sbordone, 1996). In a limited

number of studies, age-related differences in real-world spatial navigation have been

investigated and normal older individuals experience significant deficits. Results from a

real-world supermarket navigation study (Kirasic, 1991) indicated that while younger

adults acquire spatial information in a novel environment more quickly than do older









adults, no age-related differences were found in the execution of a familiar route. In

contrast, Monacelli and colleagues (2003) found significant age differences on a more

complicated hospital route execution task in a familiar setting. In another route-learning

study in a novel environment, older participants had relatively greater difficulty retracing

the route and ordering landmarks through a two-level medical setting, but were equally

good at recognizing the landmarks occurring on the route (Wilkniss, Jones, Korol, Gold,

and Manning, 1997).

To further validate human analogs of the MWM, several real-world large scale

designs have been tested. Older adults have been found to demonstrate impaired

performance when compared to younger adults (Newman & Kaszniak, 2000) and

patients with lesions to the medial temporal lobes (selective thermocoagulation to

alleviate intractable epilepsy) demonstrate that the right parahippocampal cortex is

critical for successful performance on a "dry" MWM task. More recently, Kalov and

colleagues (2005) found that similar results can be obtained from a real-world MWM

and a map view computer version when comparing performance between normal

controls and individuals with AD. While performance on these real-life analogue tasks

can aid in drawing conclusions from rat performance, they have not yet firmly

established links with "real-life" abilities. Overall then, investigation into allocentric

navigation age differences in natural real-world settings is still needed.

Our previous research correlated CG Arena performance with real life navigation

performance (hospital route learning task) and demonstrated a significant positive

relationship of CG Arena performance and real world performance (King, 2006).

However, as with the studies mentioned above, this task was a measure of egocentric









or route-learning ability. A better way to establish the ecological validity of the CG Arena

would be to demonstrate that performance on this task is similar to performance on a

real-life measure of allocentric navigation. This is a key aim of the current investigation.

Logistical factors are largely responsible for the lack of research evaluating age

related differences in spatial navigation in everyday settings (i.e. other than hospitals or

office buildings). By not evaluating performance in naturalistic environments that older

adults are ikely to encounter in everyday life, we may be missing decrements in

cognitive functioning that have a great impact on real life functioning. In contrast, we

may be overemphasizing age differences that are found in the laboratory (especially

with computerized tasks) that actually are not critical in everyday life. In keeping with the

emphasis on application of spatial skill (function), instead of description (ability), the

House Navigation Task (HNT: See methods section for details) was developed. The

HNT provides a real world natural measure of allocentric spatial navigation within a

home environment. The HNT incorporates key procedural conditions of supermarket

tasks developed by Kirasic (1991), but extends the focus to measuring map-based

(allocentric) wayfinding, as opposed to route-based navigation. It also incorporates

aspects of the Apartment Test or Memory in Reality Test (MIR: Johansson, 1988/1989),

an ecologically valid alternative to declarative memory tasks, by using everyday items

that are placed within a meaningful context.

The HNT has more face validity than navigation tasks commonly used in research

protocols. It requires participants to participate in a real life scenario of packing for a trip

and remembering the location of things in their house. As a result, older adults may be

more inclined to participate (with maximum effort) in the HNT as it is more relevant to









everyday functioning and contains a rich array of stimuli. In addition, it not only employs

isolated components of abilities (i.e. attention, language, etc.) but also draws on a

combination of skills simultaneously (e.g. object memory, spatial memory, navigation,

spatial mapping, etc.).

One other advantage of measuring spatial ability in a naturalistic environment is

that participants can use compensatory or adaptive strategies they normally use in

tasks of this type. Typical standardized testing does not allow for use of compensatory

strategies that older adults may utilize in the real world, and thus whatever age

differences exist in underlying abilities may be magnified. Therefore, traditional

standardized tests may not accurately be measuring the degree of cognitive age

differences (or lack thereof) that older adults are experiencing in everyday abilities. We

believe that the HNT is more generalizable to real-life navigation, because it is

conducted in a natural setting and participants are engaging in an activity that is a

common occurrence (packing for a trip to family's house, doctor's appointment, school,

work, vacation, etc.)

The main goal of task development was to determine whether participants employ

the use of a created spatial map to remember the location of the objects in the house.

The objects and landmarks were designed to be more meaningful to older adults than

pictures on a wall (i.e. as in real-world MWM analogues) or arbitrary routes through

hospital corridors. The HNT measures how participants (young and old) learn and

remember (route vs. spatial map), as well as navigate in novel environments. The goal

of development was to further evaluate age differences that are found in virtual MWM









task performance that may be attributed to several other factors (e.g. cohort effects-

lack of experience with technology, thereby leading to motivational factors, etc.).

A large body of data (e.g., Nadolne & Stringer, 2000) suggests that traditional

psychometric tests of spatial ability (e.g. tools that measure visualization, spatial

orientation, mental rotation, and map reading) tap into domains of visuospatial skills that

are not necessarily crucial to spatial navigation ability (as above, the ability to move

around a new environment and learn the layout). For instance, Sonnenfeld (1985)

demonstrated that paper and pencil spatial performance was not related to true

wayfinding ability. In this study, participants from Southeast Alaska, including

professional guides, fishing boat captains and pilots, were found to have some of the

poorest performances on a battery of paper and pencil spatial tests, yet were expert

navigators in real life. Kirasic (1991) also demonstrated no correlation between

cognitive task performance in a laboratory and performance in a real-world novel

environment, as well as no significant relationship between performance on traditional

paper- and pencil-based psychometric measures and navigation ability in elderly

individuals (Kirasic, 1989). More recently Hegarty and colleagues (2006) found relatively

low positive correlations of paper and pencil measures of spatial abilities with measures

of learning from direct experience with the environment (campus building route

execution task). Delpolyi and colleagues (2007) found that while a real-world route

execution task through an ambulatory care center could distinguish AD and MCI

individuals from normal controls, standard neuropsychological measures could not

differentiate those who "got lost" from those who did not.









Relationships among virtual environments and paper and pencil measures have

also been examined. Moffat and colleagues (1998) found significant positive

correlations between scores on psychometric tests of mental rotation, map learning, and

spatial orientation, and performance on egocentric virtual maze learning in humans. Our

previous work (King, 2006) demonstrated that while some psychometric paper-and-

pencil measures were significantly correlated with CG Arena performance, a majority of

the measures were not. The significant correlations that were found were of modest

proportions, and suggest that while some tests may tap some overlapping spatial ability,

CG Arena appears to measure a unique skill that is not otherwise accounted for by our

current clinical measures (King, 2006). The results of our previous work also

demonstrated significant positive correlations between performance on the mental

rotation task and overall CG Arena performance, suggesting that the two tests share

overlapping information processing demands. This result is consistent with findings that

demonstrate significant correlations with the probe trial on a virtual MWM task and

mental rotation (Astur, Tropp, Sava, Constable, and Markus, 2004) and suggests that

age related differences in computer navigation might also be explained by changes in

other cognitive processes.

Influences on spatial navigation from outside the medial temporal lobe have been

investigated and evidence that virtual MWM tasks are mediated by frontal systems has

been found. In one study, Moffat and colleagues (2007) found that gray and white

matter volumes of the prefrontal cortex was positively associated with task performance

and two measure of executive functioning were also associated with virtual MWM. In

addition, measures of executive functioning have been shown to make unique (and









independent) contributions to decline in everyday functioning in older adults (Farias,

Cahn-Weiner, Harvey, Reed, Mungas, Kramer, et al., 2009). Specifically, this suggests

that age related differences in real world abilities can be somewhat accounted for by

cognitive measures of executive functioning.

The relationship between performance on a virtual MWM and age-related

structural brain changes has been demonstrated in several studies, and is not the

purpose of this investigation. Successful Morris water maze performance is dependent

on the hippocampus and surrounding medial temporal lobe structures in animal studies.

In humans, older adults with age-related hippocampal atrophy have consistently been

found to demonstrate age-related deficits on virtual MWT and individuals with structural

temporal lobe damage also demonstrate impaired performance. Therefore, we can draw

conclusions that vMWM task performance is also sensitive to medial temporal lobe

insult. Based on these findings, we would therefore expect the underlying brain changes

seen in aging to be associated with poorer real world allocentric navigation performance

as well. Older adults demonstrate spatial navigation impairment in a laboratory setting,

and we know that they demonstrate impaired performance when compared to younger

adults in the real world egocentric navigation (e.g. route learning). However, we still do

not have adequate information about group differences in real-world spatial mapping

tasks (allocentric navigation), and we know very little about the correlation between

laboratory MWM tasks and real world performance. While the use of virtual

environments has made it possible to study wayfinding performance in a controlled

setting, and capture abilities that are not otherwise measured by standard clinical

psychometric instruments, to our knowledge, there is no research looking at how









performance on virtual MWM tasks translates to real-world allocentric navigation

behavior. The CG Arena is so unlike everyday situations, we cannot really generalize

performance on this task to everyday life spatial navigation. Examining this relationship

is a crucial step that must be taken in the development of clinical virtual navigation

tasks. Therefore, this study will help lay the foundation for establishing the ecological

validity of virtual MWM tasks.

In sum, our previous research (King, 2006) sought to evaluate various forms of

spatial cognition and navigation in young and old adults and to examine the relationship

to real world measures of spatial navigation impairment. It served as an initial step in

characterizing particular abilities, and a few of the limitations will be addressed in this

study. The two major aims of the study are to empirically determine (1) whether there

are age-group differences in real-world natural spatial navigation performance and if so,

what cognitive factors mediate these differences; (2) the relationship with navigation in

real space with that in virtual space.



Specific Aim 1

To characterize differences in real-world spatial navigation performance between

two healthy age groups (20-35 and 65 and older). Based on previous real-world large

scale navigation studies (e.g. route execution in hospital corridors and supermarkets),

we predicted that elderly participants would show impaired map-based navigation

performance when compared to healthy young participants. Contributions of domain-

specific processing capacity (executive functioning and mental rotation) to spatial

navigation were also investigated.









Specific Aim 2

To examine the relationship between navigation in a natural environment with

navigation in a virtual environment (C-G Arena). It is predicted that better performance

on a real world navigation task will be associated with better performance on the C-G

Arena. Contributions of other cognitive processes to this relationship were also

examined.









CHAPTER 2
RESEARCH DESIGN AND METHODS

Participants

Participants were fifty individuals (27 aged sixty-five and older, and 23 aged twenty

to twenty-eight) recruited from Gainesville, Florida and surrounding areas. Recruitment

for the older group took place via community advertisements (e.g. newspaper, flyers,

and newsletters). Recruitment strategies for young healthy adults focused mainly on

University of Florida advertisements (e.g. flyers). The main goal of recruitment was to

obtain a heterogeneous and representative sample of community-dwelling older adults,

without cognitive impairments, and a sample of younger adults matched on the basis of

gender and education. Both the young and old groups were cognitively intact and had

no history of neurological insult or psychopathology.

All potential participants were screened via telephone in order to exclude individual

participants based on the following criteria: (1) dementing illness or other neurological

disease (e.g. Alzheimer's disease, Parkinson's disease, epilepsy), (2) history of

significant head injury, or a mild head injury with any loss of consciousness within the

past year (3) stroke or TIA, (4) heart attack or myocardial infarction within the past year,

(5) orthopedic or heart surgery within the past year (e.g. hip replacement, CABG), (6)

cancer treated with cranial radiation or chemotherapy, (7) history of learning disability or

developmental disability, (8) severe uncorrected vision or hearing impairments, (9)

history of inpatient psychiatric treatment, (10) history of drug or alcohol abuse sufficient

to affect health, work, or family functioning, (11) unwillingness to participate in two

testing sessions, or (12) inability or unwillingness to walk five minutes without rest, five

separate times within an hour time period. Participants gave written informed consent









according to university and federal regulations. All participants who completed the

research protocol received $50.

A total of fifty-one participants were initially enrolled in the study and completed all

study procedures. The data from one participant was not included in the final analysis

due to not meeting criteria for being cognitively intact (significantly impaired memory,

language, visuospatial, and executive functioning performance on testing).

Demographic variables for the remaining 50 participants (16 females and 11 males

in the older group and 14 females and 9 males in the younger group) are shown in

Table 2-1. The racial/ethnic composition of the sample was 17 Caucasian and 6

Hispanic males and females for the younger group and 100% Caucasian for the older

group. The two groups did not differ in education [t(48) = .402, p > .05] or IQ [t(48) =

.946, p > .05]. Groups did differ statistically in MMSE score [t(48) = .-2.68, p .01],

however, the mean difference is not large (29.39 in young group and 28.67 in old group)

and not clinically meaningful.

Table 2-1. Demographic characteristics of experimental participants by group
Older Adults Mean
Measure Young Adults Mean (SD) r A s
(SD)
Number of Participants N = 23 N = 27
Age 22.83 (2.25) 75.78 (7.55)
Education 15.26 (1.42) 15.51 (2.95)
IQ 109.52 (6.29) 111.41 (7.59)
MMSE 29.39 (.72) 28.67 (1.11)
Note: IQ= Intelligence quotient as measured by the Wechsler Test of Adult Reading (WTAR)


Experimental Procedures

Following the completion of the telephone screening and consent procedures, all

eligible participants were invited into the psychology laboratory. Testing was conducted

over two sessions.









First testing session. At this session, the participant's overall cognitive functioning

was assessed using the Neuropsychological Screening and Spatial Cognition battery

and the computer-generated navigation task described below. Additionally, participants

completed mood inventories and provided lists of their current medications.

Second testing session. Within one month of the first testing session, participants

completed a real-world navigation task, as well as self-report measures of spatial

navigation impairment and tests of spatial cognition.


Neuropsychological Screening and Spatial Cognition

This battery of tests consisted of standardized tests of neurocognitive functions in

several domains. The elements of the battery are listed below (Table 2-2) and are

described in Appendix A. All tests are from peer-reviewed sources, and each measure a

particular domain of neuropsychological function commonly recognized in the

neuropsychological literature. The composition of this test battery was determined with

three goals in mind. First, we wanted to be able to distinguish healthy older adults from

older adults with significant cognitive impairment, paying particular attention to verbal

and nonverbal memory functioning. Second, with recent findings of vMWM tasks being

somewhat dependent on contributions from frontal lobe areas and with numerous

findings of age-related changes in this region, tasks thought to utilize higher order

frontal/executive skills were included to mediate age related variance on navigation

performance. Third, the particular spatial cognition assessment instruments represent

either experimental measures specially developed to evaluate the domains of interest,

or are well established and widely used measures of those domains. Tests of mental

rotation were therefore included.












Table 2-2. Cognitive Test Battery
Measure
Mini Mental State Exam (MMSE)
Clinical Dementia Rating Scale (CDR)
Memory Assessment Centers-Questionnaire (MAC-Q)
Repeatable Battery for Assessment of Neuropsychological
Status (RBANS)
Wechsler Abbreviated Scales of Intelligence (WASI) -
Vocabulary, Similarities, Block Design, Matrix Reasoning

Wechsler Test of Adult Reading (WTAR)
Boston Naming Test-2nd Edition (BNT)
Controlled Oral Word Association (COWA)
D-KEFS Trail Making Test Condition
Finger Oscillation Test
Grooved Pegboard test
Wechsler Memory Scale-III -Spatial Span
Mental Rotation Test
Space Thinking-Flags
Neuropsychological Assessment Battery (NAB) Spatial
Module: Map Test
Geriatric Depression Scale (GDS)
Beck Depression Inventory -2nd Edition (BDI-II)


Source
Folstein et al., (1975)
Morris, (1993)
Crook et al., (1992)

Randolph, (1998)

Wechsler, (1999)

The Psychological Corporation, (2001)
Goodglass & Kaplan, (2001)
Spreen & Benton, (1977)
Delis, Kaplan, & Kramer, (2001)
Reitan (1969)
Klove (1963); Reitan (1969)
Wechsler, (1997)
Vandenberg & Kuse (1978)
Thurstone & Jeffrey (1984)

PAR, Inc. (2003)

Yesavage et al., (1983)
Beck, Steer & Brown, (1996)


Self-Report Environmental Spatial Ability and Computer Game Experience

In evaluating age differences in spatial cognition, it is important to take into

consideration the likelihood that age groups may differ in "spatial experience." This

difference in spatial ability could be due to a variety of other cohort factors such as

access to education, socio-economic status, and cultural expectancies (e.g. males

traditionally "trained" more in spatial abilities, mathematical, and engineering sciences,

etc.). By incorporating these measures, it may be possible to clarify some of the cohort

effects that may be present in the older group of individuals (e.g. lack of spatial

navigation and computer experience).









The Santa Barbara Sense of Direction Scale (SBSOD) is a 27-item self-report

measure of environmental spatial ability or "sense of direction." The scale has

demonstrated good psychometric properties (Hegarty, Richardson, Montello, Lovelace,

and Subbiah, 2002).

The Everyday Spatial Questionnaire (Skelton et al., 2000) is a 13-item self-report

measure that assesses the frequency of various problems in wayfinding (e.g. 'Do you

feel disoriented when you come out of an unfamiliar building?') and locating objects left

in the environment (e.g. 'Do you have trouble finding your car in a parking lot?').

Responses were rated on a 10-point scale anchored at opposite ends by 'Never' and

'Always'/'Every time.'

The frequency of playing computer-games was rated on a 4-point scale ("never",

"rarely", "often", "very often"). In addition, the questionnaire included a single item

worded "I have never played computer-games at all" which participants could check or

not (based on Quaiser-Pohl et al. 2006). Joystick experience was also rated using this

same 4-point scale.

House Navigation Task

The House Navigation Task (HNT) is a real-world natural environment navigation

task developed for use in this study. The HNT assesses learning the spatial layout of a

one-story, 3-bedroom, 2-bathroom home environment, previously unfamiliar to subjects,

and rich with visual cues. The home has a kitchen, den, living room, and back patio; and

is located in Gainesville, Florida (Figure 2-1). Participants located 16 items needed for a

hypothetic trip. Four of the items were placed in commonly found locations within the

house (e.g. sunscreen in medicine cabinet in bathroom), and 12 of the items were

placed in unusual locations (e.g., gauze pads in kitchen cabinet; See Appendix B for list









of items). Most of the items were not in plain view so the participants could not just

"bump" into them on their way to other items in the house. In addition, there were

several other "foil" items scattered throughout the house that were not on the list.
















Figure 2-1. Gator Tech Smart House, Gainesville, Florida (HNT)

At the beginning of the task, participants were told to imagine they are packing for

a trip, and that they will need to remember the location of a number of items. At a

leisurely pace, the examiner led the participants through the house giving a tour of the

layout and pointing out the items participants needed to locate for their trip. The

presentation of the items was conducted in such a way as to not represent the shortest

or most efficient route to reach all of the items; however it was not presented in a

manner that would confuse or trick participants. The examiner pointed out two items per

room/area of the house at a time starting in the master bedroom and continuing towards

the other end of the house. When the other end of the house was reached, the

examiner then led the participant through the remainder of the house to identify/point

out the remaining items needed for the trip. Participants were then given the list of items

presented in random order and asked to locate the items as efficiently as possible, as









the examiner would be following them with a contractor's measuring wheel. Participants

were also required to tell the experimenter the item or location they would be travelling

to next before going to get it. This task was repeated for a total of three trials, starting

from a different location around the house on each trial, and with the list of items in

different order. If the participant was unable to locate all of the items (using the list) on

any of the trials, the examiner showed the participant the location of any unlocated item

on each trial. Without being previously told about the delay, participants were asked to

repeat the task two more times 25-35 minutes after completion of the last trial. The first

delay trial was completed without the assistance of the list of items and the fifth trial (or

second delay) was completed with the use of the list. The starting points for all five trials

(learning and delay) were different. After completion of the entire task (including

recognition and reconstitution measures), participants were asked to walk through a

specific route throughout the house (one hundred feet in length) in order to obtain a trial

measuring baseline walking speed. This enabled us to reference speed of ambulation to

a common metric for each participant. HNT scoring was based on 1) number of

correctly identified locations 2) number of correctly identified objects and 3) Path length

(total distance ambulated in completing the task).

After the delay trial, participants were administered two cognitive measures related

to the HNT: a house object recognition task (HORT) and a house reconstitution map

task (HRMT). The house object recognition task (HORT) required participants to

correctly identify and distinguish photographs of objects that were in the house from

some that were not (Figure 2-2). Following completion of the HORT, participants were

then led into an area of the house where they had not yet been (the very large walk-in








master bedroom closet), and they completed the house reconstitution map task

(HRMT). The HRMT required participants to appropriately place laminated photographs

(approximately 1.5 squared inch in size) of the items on a large (approximately 3 feet by

4 feet) floor plan style map (Figure 2-3). To test whether the placement of the items was

based on a developed spatial map, the furniture/appliance arrangement on the HRMT

task map was altered so that it did not exactly match the actual arrangement in the

house (i.e. some of the furniture/appliances were not included and some were placed in

a different location within the room). Participants were asked to place the objects where

they were located in the house (including correct relation to other items) regardless of

the placement of the furniture and the appliances. The exact locations of the item

placements were recorded on a small version of the floor plan map by the examiner.



*EllHEiHH MINH 1=

3 4 12. j 2 3 a $



15 It 17 Ite 11 t a 3 30 4 1 41
Figure 2-2. House Object Recognition Task (HORT)











Figure 2-3. House Reconstitution Map Task (HRMT).










Computer-Generated Arena

The Computer-Generated Arena (CG Arena; Jacobs et al., 1997, 1998) is a

computer-based analogue of the Morris Water Maze task (MWM) that is administered

on a desktop or laptop computer. The participant is asked to use a joystick to navigate

through a virtual MWM in order to find a hidden target. The stimulus environment

consists of a circular arena wall located within a small square "room." This virtual room

is analogous to the circular tank placed within a square room used in Morris' original

experiments. Each of the room's four walls contains a unique item, such as a picture, a

door, a window, or a pattern that together serve as distal cues to assist the participant in

navigating toward the hidden platform target (Figure 2-4). The placement of the walls

relative to the target remains constant over all the experimental trials. The target is a

small square located on the floor of the Arena. The task itself is modeled after the

classic MWM paradigm. On each trial, the participant starts from a different point in the

circular arena.

The CG Arena protocol began with a set of practice trials. During these trials, the

target was visible, and the participant was asked to use the joystick to navigate to it as

quickly as possible. The target was in a different place in the room on each practice

trial. Over the course of the practice trials, participants were exposed to a minimum of

five minutes practice time in order to familiarize themselves with the use of the joystick.

Participants were administered the appropriate number of practice trials until the

examiner judged that all participants were starting the experimental trials with an

equivalent level of understanding of the task, as well as familiarity with the joystick.









Immediately after completing the set of practice trials, participants were

administered a set of acquisition trials. During these trials, the participants entered into

a new virtual room for eight trials. The target was invisible, but remained in the same

location across acquisition trials. During the first trial, the participant navigated through

the environment until the invisible target was found. Once this occurred, the target

became visible (Figure 2-5) and was paired with an auditory clicking-sound. The target

also "trapped" the participant and made it impossible to move off by use of the joystick,

forcing the participant to look around the Arena environment. This procedure was

repeated for the remaining seven trials. In the event that the participant was unable to

independently locate the target within 120 seconds, the examiner assisted the

participant. Such assistance was given only on the first two acquisition trials.

The starting position within the Arena was randomized for the first six acquisition

trials. In order to measure learning in the data analysis, Trial 7 had the same starting

position as Trial 2, and Trial 8 had the same starting position as Trial 1.

Immediately following the 8 acquisition trials, the participant was administered a

probe trial. On this trial, the hidden target was removed from the virtual room,

unbeknownst to the participant. This final trial is an analogue of the standard probe trial

in MWM research, in which the animal, knowing where the target "should be,"

repeatedly swims around the anticipated target location, searching for it. Upon

completion of the probe trial, the participant was presented with a blank screen,

indicating the end of the CG Arena portion of the testing session. Participants were then

immediately debriefed about the removal of the target on the probe trial.








For each trial, several dependent measures, including the length of the navigation

path, the latency to find the hidden target, the time spent in each quadrant of the arena,

and whether the target was actually found, were automatically recorded by the CG

Arena software.

After the probe trial, participants were administered two paper-and-pencil

measures related to the CG Arena task: an arena reconstitution task (ART) and an

object recognition task (ORT). The ART required participants to reconstruct the CG

experimental room by appropriately placing icons representing the four walls of the

room, the objects on those walls, and the target onto a sheet of paper. The ORT

required participants to correctly identify and distinguish objects that were on the walls

in the experimental room from a group of objects, some of which were in the room and

some of which were not. The entire CG Arena protocol, including the ART and ORT

tasks, took approximately 30-45 minutes to complete.



I I 7 -b



Northeast Quadrant Northwest Quadrant




I -

Southeast Quadrant Southwest Quadrant

Figure 2-4. Representations of the C-G experimental room as seen by the participants











4 --.--.



IIL





Figure 2-5. Representation of target once it is successfully acquired


Data Reduction

House Navigation Variables

The number of correct locations and number of correctly identified objects were

recorded. For each trial, a participant could score a total of 16 points for number of

correctly identified locations and number of correctly identified objects. This scoring

procedure was in place to distinguish memory for objects from memory for location in

space. Path length was measured in feet using a contractor's measuring wheel. The

examiner followed participants as they navigated through the task in order to collect

distance travelled. The "ideal" path length, or the distance of the length to acquire the

items as efficiently as possible, was calculated a priori from each starting point for

interpretive purpose use. This length was consistent across starting points and varied

by only 10 feet (278-288 feet). If all items were not located on a given trial, the path

length score was calculated as a proportion of the number of items found (X) so that

participants would not have shorter path lengths if they did not locate all items.

Number of items found 16

Path length X









The house object recognition task (HORT) consisted of 42 recognition items:

Sixteen of the items were the items located during the experiment, ten were items that

were in plain view throughout the house located near the target items, and sixteen items

were not located in the house. Two scores were derived for this task. One was a total

recognition score based on correctly distinguishing items that were in the house (both

critical and incidental) from those that were not. The second was based on correct

identification of incidental items.

Two scores were derived from the house reconstitution map task (HRMT). One

score was based on the following criteria: 1) item in correct room, 2) item in correct

relation to other items, and 3) item in correct location. A point was awarded for each

criterion for each item, for a total of 48 points. The second score for this task was based

on deviation of the item from the correct location, measured in centimeters.

In order to weigh the various HNT dependent variables equally, a composite

dependent variable (DV) was created from the mean of the z scores earned on each

variable (path length (trials 1-3), path length delay; correctly identified locations (trials 1-

3), correctly identified locations delay, correctly identified objects (trials 1-3), and

correctly identified objects delay, HORT total recognition and incidental score, HRMT

total score and deviation). The basic statistical procedures we used to create a

composite DV are outlined in Rosenthal (1991).

Computer Generated Arena Variables

Path Length was recorded by the distance travelled over the course of the trial,

either when the target was found or until the trial ended. The sum of target acquisitions

was recorded by counting the number of times the participant found the invisible target

over the course of the acquisition trials. Dwell time on the probe trial was created by









calculating the proportion of time the participant spent in the target quadrant (the

quadrant where the invisible target had previously been located) during the probe trial.

The ORT score was created by summing up the total number of correctly identified

objects and correctly discerning objects that were not located in the room. The ART

score was created by using a template that awards points to objects placed correctly in

the room relative to the actual location.

In order to weigh the various CG Arena dependent variables equally, a composite

dependent variable (DV) was also created from the mean of the z scores earned on

each variable (path length; total target acquisitions across invisible trials; dwell time on

probe trial; ORT score; ART score).

Cognitive Test Variables

A cognitive flexibility (frontal executive functioning) variable (FE) was created from

the mean of the z scores on DKEFS Letter Number Sequencing (switching-connecting

circles alternating between numbers and letters with DKEFS motor control portion time

substracted from time) and WAIS-III Digit Span-Backward (working memory-reciting

increasingly longer strings of numbers in reverse). Raw scores on each of these

measures were first converted to standardized scores based on the mean and standard

deviation of the entire group.

A psychomotor skill and speed variable was created using the same procedures as

above with the Finger Tapping dominant hand and the DKEFS motor control portion raw

scores.

Mental rotation was measured using Space Thinking Flags (Thurstone & Jeffrey,

1984). This test requires participants to view a picture of a flag and judge which of the six

alternative test figures are planar rotations of the flag. The score was based on the









number of items answered correctly within five minutes. The Mental Rotation Test

(Vandeberg & Kuse, 1978) was not used in calculation of this variable. Most of the old

adults and a few of the young adults were unable correctly answer any of the practice

items and therefore it is not considered an accurate representation of mental rotation

ability.









CHAPTER 3
RESULTS

Smart House Navigation Task Performance

We began data analysis by evaluating differences in young and old adult

performance on the real world navigation task. In examining age group differences

using a MANOVA, with age group as the independent variable and performance on the

House Navigation Task (HNT) as dependent variables, a significant main effect for age

was found, Wilks Lambda F(10,39) = 7.358, p < .001, q2 = .65. Univariate comparisons

confirm that the young group found the items in shorter distance (length) F (1,48) =

25.56, p < .001, q2 = .35, and more often (number of correctly identified locations F

(1,48) = 36.02, p < .001, r2 = .43, and number of correctly identified items, F(1,48) =

55.27, p < .001, q2 = .54, on the acquisition trials (1-3). Age differences were also seen

on the delayed recall trial, in which the young group again found the items in a shorter

distance, F(1,48) = 19.13, p < .001, r2 = .29, navigated to the location more often,

F(1,48) = 17.79, p < .001, q2 = .27, and correctly identified the item in that location more

often, F(1,48) = 23.21, p < .001, r2 = .33. The old and young group differed in their

performances on the House Object Recognition Task (HORT) total score, F(1,48) =

5.19, p = .016, r2 = .11, HORT incidental item score F(1,48) = 7.55, p = .008, r2 = .14,

on the House Reconstruction Map Task (HRMT), F(1,48) = 18.05, p < .001, r2 = .27,

total score, and on the HRMT Error Measurement score F(1,48) = 6.26, p = .016, r2 =

.12.

The composite HNT variable, which represented overall performance, was then

subjected to an independent samples t-test to test for group differences in allocentric

navigation. Significant group differences were found t(29.74) = -7.21, p < .001, d = -









2.74. Overall, older participants performed significantly worse on the HNT composite

than did their younger counterparts (Figure 3-1).


1.00




0
S0.00
z


old young

Figure 3-1. Mean HNT composite performance by group, t(29.74) = -7.21, p < .001, d= -2.74.


To further examine age group differences in House Navigation Task (HNT)

performance, the old group was broken into 'young old' (65-74) and 'old old' (75 and

older). Demographic variables for the 27 old participants are shown in Table 3-1.

Table 3-1. Demographic characteristics of old and old old groups
Measure Young Old Adults Mean Old Old Adults Mean
(SD) (SD)
Number of Participants N = 13 N = 14
Age 69.15 (2.45) 81.93 (4.90)
Education 15.00 (3.03) 16.00 (2.90)
IQ 109.31 (7.45) 113.36 (7.59)
MMSE 28.69 (.94) 28.64 (1.28)
Note: IQ= Intelligence quotient as measured by the Wechsler Test of Adult Reading (WTAR)

An analysis of variance (ANOVA) was used to test for differences among the three

age groups with the composite HNT variable as the dependent variable and age group

as the independent variable. Overall performance on the HNT differed significantly

across the three age groups, F(2, 47) = 39.18, p < .001, q2 = .63. There was a










significant linear trend, F(1, 47) = 77.83, p < .001, indicating as age group increased

(young
proportionately (Figure 3-2). Furthermore, the planned contrasts revealed that both old

groups demonstrated significantly decreased HNT performance compared to the young

group, t(29.84) = -8.19, p < .001, and the old old group demonstrated significantly

poorer performance when compared to the old group, t(24.98) = -3.20, p = .002.


1.00






0-.50-

00-r
a.L
E




-1.00


-1.50-

Young Old Old Old

Figure 3-2. Mean HNT composite performance by group, F(2, 47) = 39.18, p < .001, n2 = .63


Analyses of baseline time revealed expected significant age group differences in

ambulation speed, F(2, 47) = 6.86, p = .002, r2 = .140, with a significant linear trend,

F(1, 47) = 13.63, p = .001, indicating as age group increased (young
old), baseline time increased (i.e. slower speed) (M young = 34.43 seconds, SD = 5.66;

M old = 35.54 seconds, SD = 4.37; M old old = 40.50, SD = 6.60). Accordingly, even

though time was not a measure of HNT performance, an analyses of covariance

(ANCOVA) with baseline time as a covariate was conducted. Baseline speed was not a









significant covariate F(1, 47) = 1.43, p = .238, and therefore significance was

maintained when controlling for it F(2, 47) = 26.63, p < .001, r2 = .53.

Table 3-2 provides the mean scores for each of the HNT variables broken down by

age group. The mean scores are highest in the young participants, as expected, and a

ceiling effect is evident. Figure 3-3 shows the age differences in correctly identified

locations across the three trials and on the delay recall trial. All age groups showed

learning over trial, losing very little information on the delayed recall trial. A paired-

samples t-test was conducted to compare the number of correctly identified locations in

the delay recall trial to trial 3. There was a not a significant difference in any of the age

groups (Young: t(22) = -1.00, p = .3.28; Old; t(12) = 1.00, p = .337; Old Old: t(13) = -.79,

p = .444). In addition, the oldest old were able to remember an average of 92% of the

locations on the learning trials and remember 88% of the locations on delay recall. No

significant differences (p's >.05) between the delay recall trial and trial 3 were found in

any of the age groups for correctly identified items (Fig 3-4) or path length (Fig 3-5).


2000- Trial1 Trial Trial Tral3 Delay



15.00"




0
0-J



o.00-


Young Young Old Old Old

Figure 3-3. Correctly identified HNT locations by group
























Table 3-2. Mean scores for HNT variables
HNT Trial HNT Trial HNT Trial 4 HNT
HNT Trial 1-3 1-3 Correct 1-3 Correct HNT Trial 4 Correct HNT Trial 4 HNT ORT HNT HRMT
Age Group n Path Length Location Item Path Length Location Correct Item HNT ORT Incidental HRMT Error



Young 23 324.82 (20.95) 15.07 (.71) 14.86 (.91) 301.80 (12.04) 15.96 (.21) 15.78 (.67) 35.22 (2.76) 5.39 (2.08) 47.57 (.90) .98 (2.79)



Old 13 406.84 (93.06) 13.28 (2.05) 11.15 (2.52) 334.43 (32.40) 15.31 (1.03) 14.23 (2.09) 34.0 (2.42) 4.46 (2.67) 43.38 (7.08) 2.11(3.06)



Old Old 14 464.93 (109.89) 11.93 (1.47) 8.93 (3.96) 396.07 (82.58) 14.14 (1.51) 11.29 (3.07) 32.64 (2.84) 2.71(2.23) 34.79 (10.18) 3.83 (2.54)









Trial 1 Trial 2

15.00-

E


45-
O
1000-


I-
0
I.--





0.00- --
Young Young

Figure 3-4. Correctly identified HNT items by group


Trial 3 Delay








^I


Old Old Old


500.00-


400.00-


300.00-


Trial 3


K Delay


T T


200.00 1
Young Young Old

Figure 3-5. HNT path length for trial 3 and for delayed recall


Old Old









Smart House Navigation Task Performance and cognition

Mediation analyses were conducted to examine whether performance on the

neuropsychological screening and spatial cognition battery was associated with

performance on the HNT. In previous studies, mental rotation and executive functioning

(mental flexibility) abilities have been found to influence both virtual and real world

environmental layout learning.

First evaluating frontal/executive cognitive performance (FE), formal significance

tests of the indirect effect of the indirect effect of mental rotation were conducted by

means of the Sobel test and a bootstrap approach (explanation and macro can be found

in Preacher and Hayes, 2004). Results of both procedures indicated that

frontal/executive cognitive performance exerted a significant (p < .05) indirect

mediational effect on the relationship between age and HNT performance. To further

examine the degree of mediation, we employed a four-step, ordinary least squares

approach (Baron and Kenney, 1986). Step 1 indicated a significant total effect of age on

house navigation (HNT; 3 = -.73, p <.001); Step 2 indicated a significant effect of age of

FE (3 = -.58, p <.001); and step 3 indicated a significant effect of FE on HNT, while

controlling for age (3 = .21, p = .03). Thus, the first three steps in establishing mediation

were satisfied, supporting the results of the tests of the indirect effect. Step 4 revealed

that although HNT performance decreased with increasing age when controlling for FE,

it remained significant (3 = -.60, p <.001), indicating that FE partially mediates this

relationship. These results demonstrate that age and FE collectively account for 65% of

the variance (R2 = .65) in house navigation performance.

Formal significance tests of the indirect effect of mental rotation did not find a

significant indirect effect of age on navigation performance through mental rotation (p >









.05). A significant effect of age on mental rotation was found (3 = -22.13, p < .001).

When controlling for age, the significant effect of Mental Rotation on HNT did not remain

significant (3 = .0043, p = .1734). However, the significant negative relationship

between age and HNT (3 = -.73) does becomes smaller when controlling for mental

rotation (3 = -.63, p< .001)

CG Arena Performance

The next part of our statistical analysis examined differences in overall CG Arena

performance between older and younger adults. Using a MANOVA with age group as

the independent variable and individual variables of CG Arena performance as the

dependent variables, a significant effect of age group was found, Wilks' Lambda,

F(7,42) = 1120.02, p < .001, q2 = .83. Univariate analyses showed that the young group

found the target in shorter distance (path length), F(1,48) = 67.95, p < .001, q2 = .59,

more often (number of target acquisitions, F(1,48) = 50.59, p < .001, q2 = .51, and spent

a greater percentage of time in proximity to the target on the acquisition, F(1,48) =

141.97, p < .001, r2 = .75, and probe trials, F(1,48) = 49.19, p < .001, r2 = .51. On

measures administered after completion of the computer task, the young group

demonstrated significantly better overall performance on the Arena Reconstruction Task

(ART), F(1,48) = 13.87, p = .001, r2 = .22, and placed the target in the correct quadrant

more often than the old group, F(1,48) = 5.25, p = .023, q2 = .10. The old and young

groups did not differ in their performances on the Object Recognition Task (ORT),

F(1,48) = 1.01, p = .319; Myoung = 12.67, Mold = 13.17.

Both the young adult and older adult groups found the target consistently when the

target was visible on the practice trials. To exclude the possibility that the group

differences observed in CG Arena performance was secondary to greater joystick










experience and computer familiarity in the young group, a mean path length score was

calculated for all visible practice trials. This variable did not reveal reliable age group

differences, t(48) = .783, p = .438. Joystick experience as assessed by questionnaire

also did not reveal reliable age group differences, t(48) = -.384, p = .702. Computer

game experience as assessed by questionnaire did reveal reliable age group

differences, t(33.21) = -.4.50, p < .001, and was therefore used as a covariate in the

following analyses.

An analysis of covariance (ANCOVA) was performed with CG Arena composite

as the outcome variable, age group as the fixed factor, and computer game experience

as the covariate. The results of the ANCOVA indicated that computer game experience

was not a significant covariate, F(1,47) = .105, p = .747, and did not predict CG Arena

performance. There was a significant effect of age on overall CG Arena performance,

F(2, 47) = 47.41, p < .001, r2 = .74, in that older participants performed significantly

worse on the CG Arena composite than did their younger counterparts (Figure 3-6).


1 .00



0.50-

0
0

0.00-




-0.50



-1 .00 l I
old young
Figure 3-6. Mean CG composite performance by group F(2, 47) = 47.41, p < .001, n2 = .74.











To further examine differences in age group CG Arena performance, the old group

was again divided into 'young old' and 'old old.' An analysis of variance (ANOVA) was

used to test for differences among the three age groups with the composite CG variable

as the dependent variable and age group as the independent variable. Overall

performance on the CG Arena differed significantly across the three age groups, F(2,

47) = 65.89, p < .001, q2 = .74. There was a significant linear trend, F(1, 47) = 122.58, p

< .001, indicating as age increased (young
task performance decreased proportionately. (Figure 3-7). Furthermore, the planned

contrasts revealed that (old) age significantly decreased CG Arena performance

compared to young adults, t(44.45) = -11.08, p < .001, and the old old group

performance was significantly worse than the old group, t(24.46) = -3.07, p = .005.


1.00






U,
o0
CL 0,00"
E


-0.50-





-1.50'

Young Young Old Old Old

Figure 3-7. Mean Group CG Arena composite performance F(2, 47) = 65.89, p < .001, r2 = .74


To further account for age group differences that can be attributed to known age

group differences in motor skill (slowing) and accompanying joystick manipulation,









baseline motor testing was examined. Analyses of psychomotor skill and speed (MS)

revealed expected significant age group differences, F(2, 47) = 20.69, p < .001, with a

significant linear trend, F(1, 47) = 38.126, p <.001, indicating as age increased

(young
young = .59, SD = .54; Mold = -.29, SD = .65; Mold old = -.74, SD = .76). Accordingly,

an analyses of covariance (ANCOVA) with motor speed (MS) as a covariate and CG

Arena composite as the dependent variable was conducted. MS was not a significant

covariate F(1, 47) = 1.95, p = .169 and therefore significance was maintained when

controlling for it F(2, 47) = 29.17, p <.001, q2 = .565.


Relationship between navigation in CG Arena Space and Real Space

In line with our second aim, the next part of our statistical analysis examined the

relationships with real world spatial navigation (HNT performance) and computer

navigation performance. Preliminary analyses focused on bivariate correlations between

the HNT, CG Arena, and FE composite scores considered separately for each of the

three age groups. Table 3-3 and Figure 3-8 shows the mean scores obtained by each

group. When looking at the groups separately, no significant correlations exist, although

near significant correlations between HNT and FE and HNT and CG Arena exist in the

old group (Table 3-4). However, when combining the two old groups, there exists a

significant positive correlation between CG Arena and HNT (r= .439, p < .02),

accounting for 19% of the variance.










Table 3-3. Composite Scores by age group M (SD)
Frontal Executive HNT Composite CG Arena Composite
Age Group n Composite Score Score Score

Young 23 .44 (.65) .68 (.19) .66 (.32)

Old 13 -.02(.64) .008 (.61) -.28 (.47)

Old Old 14 -.73 (.80) -.79 (.68) -.82 (.43)


0



E
5 -1.00- --------------------


-1.50"


-2.00-
I I
Young Old
Figure 3- 8. Composite scores by age group





Table 3-4. Correlation of composite scores by group
Age Group HNT-Arena Arena-FE
Young 0.181 -0.128
Old 0.481* 0.258
Old Old -0.012 0.243
Note: p = .096, ** p = 0.066


Old Old


FE-HNT
0.074
0.525**
0.308









To determine the relative contribution of CG Arena, age, and FE to HNT

performance, this relationship was examined further by using hierarchical multiple

regression analyses. At the first step, we entered the composite CG Arena variable.

Second, we entered age. Based on the evidence of frontal executive functioning (FE)

partially mediating the effects of age on HNT, we also entered FE in the second step.

Using this method, a significant model emerged in the first step with CG Arena

predicting HNT, F(1, 48) = 55.03, p < .001. The second model also significantly

predicted the outcome variable, HNT, F(3, 46) = 30.084, p < .001, accounting for 67% of

the variance in HNT performance. Once age and FE were added to the model, CG

Arena no longer significantly predicted HNT performance. This, along with the

significant large correlation of age and FE (r= -.58, p < .001), suggests that FE does a

better job of predicting real world navigation than does computer navigation. Results are

given in Table 3-5.


Table 3-5. Summary of multiple regression analyses examining contribution of CG
Arena, age, and FE on HNT performance.
b SEb p
Step 1
Constant .094 .076

CG Arena .763 .103 .731***

Step 2
Constant .482 .143

CG Arena .184 .170 .176

Age Group -.473 .155 -.513**

Frontal Executive .204 .098 .218*
Note. R2 = .534 for Step 1: A R2 = .134 for Step 2 (p < .05). p < .05 ** p < .01 *** p < .001









CHAPTER 4
DISCUSSION

The broad aim of this study was to evaluate changes in spatial memory and

cognition associated with normal aging. The results of the present study demonstrate

that real world environments can be used to assess age effects in spatial navigation

performance. We used a 3 bedroom, 2 bathroom house to develop an ecologically valid

task modeled after theories of allocentric spatial navigation (House Navigation Task;

HNT). This environment was easily understood, well organized, and thereby was

predicted to elicit optimal performance. The performance of young, old, and old old

adults improved with each trial, each locating more items with shortened distance

travelled over trials. These results provide evidence that real world environmental

support may enhance memory performance (especially in older adults). However,

despite improvement in performance over time and retention of this information on

delayed recall, the two groups of older adults were clearly impaired in overall

performance relative to the young group, with increasing impairment in the old-old group

compared to their young-old counterparts. Moreover, on the delayed recall portion of the

HNT, 96% of young adults, 65% of old adults, and 14% of old old adults were able to

correctly identify all 16 locations.

When examining individual variables of the HNT, some interesting patterns

emerged. As represented by overall path length, an increase in age was accompanied

by more frequent repeat visits to locations already visited within a trial and less efficient

acquisition of the items (i.e. going back and forth between rooms). Older adults would

frequently back track between rooms when they realized they had forgotten an item (or

not gone to a location) in a room they had already visited.









This effect was further illustrated when looking at performance on number of

correctly identified locations and correctly identified items. Scoring for HNT was based

on the well documented relationship between aging and decline in memory functioning

including spatial memory (e.g., Park, et al., 2007) and verbal memory (e.g. Salthouse,

1998), as well as evidence that spatial and object memory are two different systems

(e.g. Courtney, Ungerleider, Keil, & Haxby, 1996). It was expected that older adults

would not be able to remember the objects as well as younger adults, but memory for

location in a real world setting was of interest. While there was a significant age group

difference in the number of correctly identified locations, old adults on average correctly

remembered almost all (14-15 out of 16) of the locations (old: M = 15.31, SD = 1.03; old

old: M = 14.14, SD = 1.51). However, they did not efficiently navigate to these locations

resulting in longer path lengths. Contributions to this inefficiency were explored even

further using a mediation model approach.

The significant age group differences found in path length, correctly identified

locations, and correctly identified items even on Trial 1 further suggest that age

differences in navigational ability are not restricted to learning and memory. These gaps

narrow over trial, but significant group differences still exist. These age differences in

our study then could not simply be due to differences in general spatial ability, but can

be explained by other factors as well.

The data also demonstrated group differences in the house object recognition task

(HORT), an independent paper-and-pencil recognition task administered after the

completion of the delay trials. While significant group differences were found, closer

investigation reveals that these differences were fairly small with no participant (young









or old) correctly recognizing or differentiating all 48 items (Total HORT score young: M =

35.22, SD = 2.76; old: M = 34.00, SD = 2.42; old old: M = 32.64, SD = 2.84). The finding

of significant group effects on correct identification of incidental items was opposite to

what was predicted. Based on the relationship between age and distraction (or inability

to suppress irrelevant information) (e.g. Andres, Parmentier, & Escera, 2006; Chao &

Knight, 1995), it was predicted that the older adults would be more accurate in the

identification of items placed in the house that were not needed for the experiment (i.e.

items not located on the list needed to "pack for the trip"). In other words, they would

attend to incidental items as much as they would to critical items. This did not prove to

be the case and in fact the opposite was found. Younger adults performed best on

accurate identification of incidental items, followed by old adults and then the old old

adults (Total HRMT score young: M = 47.57, SD = .90; old: M = 43.38, SD = 7.08; old

old: M = 34.79, SD = 10.18). Although the HORT measures recognition of objects that

were located in the house, it does not measure reconstruction of the spatial

relationships among the objects, and likely taps different memory representations than

those used for successful spatial navigation.

Analysis of the house reconstitution map task (HRMT), which required participants

to place the 16 items used in the experiment on a floor plan of the house revealed

significant group differences. Younger adults placed the items in the correct room and in

correct relation to the other items more often than old adults. When looking at accuracy

(deviation measurement), young adults again performed significantly better. This, taken

together with the mean number of items correctly identified on the HORT suggests that

while older adults are able to recognize objects in their environments, they are unable to









reconstruct a cognitive map of the relationship of these items in the environment. On the

HRMT, older adults were frequently observed to place items on the misarranged pieces

of furniture that were not located in the correct room or in correct relation to other items

in the same vicinity. It could be the case that older adults were either encoding the

information using a verbal or visual object memory strategy rather than encoding the

information using a spatial map (i.e. "the keys are on the desk"). This strategy led to the

large discrepancy in deviation scores between groups and suggests older adults were

not effectively encoding the layout of the environment. Younger adults appear better

able to accurately remember the precise spatial layout of an environment than older

adults.

We then sought to examine other cognitive factors mediating the relationship

between age and spatial navigation. Specifically, based on previous research, we

predicted that the expected age-related differences in real world navigation would be

accounted for by mental rotation ability and executive functioning (cognitive flexibility).

We were able to demonstrate the causal relationship between aging and poorer real

world navigation (HNT) performance was partially mediated by executive functioning.

This finding suggests that executive functioning may be the driving critical cognitive

contributor to successful navigation, mostly independent of age. This result fits well with

the correlation of normal aging and accompanying age related effects on

neuropsychological tests of executive functioning (e.g. Salthouse, Atkinson, and Berish,

2003). Indeed, approximately 35% of the variance in the direct effect of age on HNT

remains unexplained. It is plausible that this relationship may be partially mediated by









other cognitive factors, or factors not measured in this study. Mental rotation was not

found to mediate the significant causal relationship between age and HNT.

Consistent with previous findings, results from this study clearly demonstrated that

overall, older adults do not navigate as effectively as younger adults in virtual space

(CG Arena). Performance on all visible practice trials was comparable, all participants

reported they understood the instructions, and all participants were trained until they

mastered the use of the joystick. Furthermore, young adults did not report significantly

more experience with use of a joystick than older adults.

Young adults did report significantly more computer game experience than older

adults. However, in the analyses, this experience did not have an impact on

performance. It can therefore be concluded that the age differences found were not a

function of lack of experience with the computer or joystick. While time was not included

in measuring age group differences, known age effects of psychomotor processing

speed were still considered. Despite the joystick control being relatively simple (gross

motor skill- forward, left, right), changes in motor skill that accompany aging, could

make it harder for older adults to manipulate the joystick, thereby impacting

performance. Therefore, the age group differences in psychomotor skill and speed were

examined and in this analyses did not have an impact on performance. In addition, no

age related differences were found on the practice trials of the experiment. We can

therefore conclude that this age difference found in CG Arena performance cannot be

accounted for by generalized psychomotor slowing associated with age.

We also examined CG Arena variables independently and our findings confirmed

previous work done in our laboratory. This data demonstrated that young adults found









the target more often and with shorter traveling distance than did older adults.

Additionally, on the probe trial, young adults spent a greater proportion of time

searching the target quadrant than older adults. Consistent with previous studies, this

pattern of results indicated that young adults learned, remembered, and navigated to

the invisible target more effect than older adults (King, 2006, Laurence et al., 2002).

Age group differences were also demonstrated on the arena reconstitution task

(ART), but not the object recognition test (ORT). Both are paper and pencil tests

administered after completion of the CG Arena. The older group placed the target in the

correct quadrant on the ART significantly less often than the younger group, further

supporting the idea that they did not have an accurate memory representation for the

location of the invisible target. These data suggest that older adults are not able to

reconstruct virtual cognitive maps of their environments as well as younger adults or

remember a particular place in that environment as accurately as younger adults.

However, they are able to recognize objects that were in the virtual environment as well

as younger adults (ORT).

A third important outcome of the present study emerged from examining the

relationship of performance on CG Arena with that of HNT. The focus of the present

study was to evaluate how virtual navigation predicts real world navigation, especially in

older adults. CG Arena turned out to have less impact on HNT performance than age

and executive functioning. While CG Arena has a significant positive correlation with

HNT, when age and executive functioning are entered into the model, this significant

correlation no longer exists. In addition, there is a large significant negative correlation

between age and executive functioning. This is all good evidence to suggest that tasks









with executive functioning demands are more powerful than computer navigation

performance and age in predicting real world navigation. Therefore, while significant

correlations exist between navigation in computer space and real space, this present

study expands on this finding even further by evaluating other factors contributing to this

relationship.

One explanation for the lack of impact from computer navigation is the cognitive

flexibility demands of HNT, as mentioned above. In addition to the spatial learning

components of the HNT, there exists a large working memory and planning component.

One must visualize the location of the items, and then decide the order of item location

so that the most efficient path is taken. If this is not done successfully, then it leads to

inefficiency (i.e. travelling back and forth between rooms). The high execution demands

of this real world navigation task could be contributing to the decrease in shared

variance with the CG Arena and both measures most likely rely on executive functions

systems. As reviewed earlier, it has been demonstrated that vMWM performance is

influenced by frontal systems and that cognitive measures that are thought to tap these

systems are correlated with vMWM performance (Moffat, et al., 2007).

The limitations of the current study will be addressed in further studies. First, one

recognized limitation of the design is that, by not performing the task in the person's

home and having it be so structured, it does not completely generalize to the individual

participant's environment (Burgess, Alderman, Forbes, Costello, Coates, Dawson, et al.,

2006). Despite the task not being "open ended" in nature, there are several

methodological gains that are believed to make this a valuable measure. These include

the ability to control the stimuli in the environment (e.g. familiarity and range of









exploration both of which could not be accomplished in an individual's home or

supermarket), standardization of the test demands, and safety and privacy of the

participants (Titov & Knight, 2005). Despite the benefits of methodological control, future

studies focusing on navigation in an individual's home or frequently encountered

environment, may provide a better gauge of difficulties experienced in everyday life.

Second, executive functioning has been conceptualized to be comprised of

several different abilities (components) and is frequently tested using a variety of tests.

For this study, we tested only two measures that fall into the working memory and

switching subcomponents. Therefore, the two measures used in this study may not be

representative of all aspects of the executive functioning construct. Future studies

focusing on more subcomponents of executive functioning, and with more tests may

provide additional information to the relative contribution of executive functioning

abilities to real world spatial navigation. In keeping, mental rotation ability was only

measured by one test in this project, and may therefore not be an accurate indicator of

the relative contribution to real world navigation performance.

Because this study focused on two healthy groups of participants, it was not

focused on demonstrating links between behavioral performance and the underlying

neural substrate for spatial cognition. Future studies focused on clinical populations may

include anatomic measures of MTMS pathology using structural MRI; demonstrating

sensitivity and specificity to anatomic losses would improve the task's clinical and

experimental utility. In addition, inclusion of individuals with dementia could assist in

examining the relationship of these neurobiological measures to performance.









While our current sample is nearly matched on IQ and education, the study was

conducted on a sample of healthy, predominately Caucasian adults, with high levels of

education. Therefore, caution must be exercised when generalizing to other

populations. Future direction would aim to recruit a more diverse sample that is more

representative of the population.

Due to ceiling effects in the young group, accompanying issues with restricted

range of scores, and small sample sizes overall, relationships between all cognitive

abilities and HNT performance could not be fully assessed. In future studies, it would be

useful to include either a large sample of either healthy older or young adults in this type

of study. If normal healthy younger adults were examined, then test demands should be

increased to reduce ceiling effects. Contributions of other cognitive factors could then

be further assessed. HNT and CG Arena performance should then be included in a

factor-analytic study of cognitive ability that includes neuropsychological measures from

many domains.

Present findings have shed some light on changes in real world spatial navigation

that occur with aging and how these abilities may actually be mediated by other factors.

Our findings show that real world navigation as measured by HNT is strongly influenced

by executive functioning (working memory and switching). The influence of aging on

executive functioning should be taken in consideration in future studies of spatial

cognition. The use of real world environments in future research with normal aging

individuals as well as young individuals with frontal lobe insult may help further our

understanding of spatial ability age differences and brain mechanisms involving spatial

navigation with greater clarity. We believe that a systematic analysis of the causal link









between executive functioning and real world navigation ability is an appropriate next

step in this line of research.

In conclusion, the present report provides additional evidence that older adults

demonstrate poorer performance on virtual and real world tasks of spatial learning and

memory than do their younger counterparts. These data confirm the feasibility of the

HNT task in an older population and speak to the possible utility of using more

ecologically valid measures in a clinical setting. Given the importance of environmental

demands when assessing cognitive functioning, both laboratory and real world tasks

need to be utilized for accurate assessment.









APPENDIX A
COGNITIVE TEST BATTERY


Overall Construct Measure Description of Test Source
Measure


General Mental
Status


Observer/ Interview
Ratings



Functional
Assessment


Mini Mental State
Exam (MMSE)


Clinical Dementia
Rating Scale (CDR)



Memory Assessment
Centers-
Questionnaire
(MAC-Q)


Estimation of dementia
severity


Disease severity rating:
informant & patient
subjective, objective
elements

Subjective memory
complaints


Folstein et al., (1975)


Morris, (1993)




Crook and Larrabee,
(1992)


Wechsler Predicts general
Abbreviated Scales of intelligence, verbal and
Intelligence (WASI 2 performance abilities
subtest) Vocabulary,
Matrix Reasoning


Wechsler Test of
Adult Reading
(WTAR)


Overall
Functioning/
Screening Battery




Language
Functioning


Estimation of premorbid
levels of intelligence


Repeatable Battery Brief core battery for
for the Assessment of detection of brain insult -
Neuropsychological dementia, head injury, or
Status (RBANS) stroke.



Boston Naming Test- Confrontation naming
2nd Edition (BNT) using large ink drawings

Controlled Oral Word Verbal fluency to alphabet
Association (COWA) letter (FAS).


Wechsler, (1999)






Wechsler, (2001)



Randolph (1998)






Goodglass & Kaplan,
(2001)

Spreen & Benton,
(1977)


Intellectual
Functioning









Overall Construct Measure Description of Test Source
Measure


Frontal/Executive
Skills- Attention,
Processing Speed,
Problem Solving,
Abstract thinking


WAIS-III Digit Span Attention span


D-KEFS Trail Making
Test Condition


Visuomotor speed, visual
scanning, sequencing,
cognitive flexibility


Wechsler, (1997)


Delis, Kaplan, & Kramer,
(2001)


Finger Oscillation
Test
Grooved Pegboard
test


motor speed

speeded fine motor
dexterity


Reitan (1969)

Klove (1963); Reitan
(1969)


Spatial
Skills/Cognition


Wechsler Memory
Scale-Ill -Spatial
Span


visuospatial short-term
and working memory


Wechsler, (1997)


Mental Rotation Test Mental Rotation

Space Thinking-Flags


Neuropsychological Local Navigation
Assessment Battery Strategy/Map Reading
Spatial Module: Map
Test (NAB)


Vandenberg & Kuse
(1978)
Thurstone & Jeffrey
(1959)

Stern and White, (2003)


Geriatric Depression
Scale (GDS)


Self evaluation assessing
elements of depression


Beck Depression Self evaluation assessing
Inventory -2nd Edition elements of depression
(BDI-11)


Yesavage, Brink, Rose,
Lum, Huang, Adey, and
Leirer, (1983)

Beck, Steer & Brown,
(1996)


Motor


Mood









APPENDIX B
HOUSE NAVIGATION TASK ITEM LIST

Packing for a trip out of town and need to locate the following items.... Colors represent
items that were located in the same room or close to one another.

Six of the proposed items were based on the MIR task (not used-glass, scissor,
matchbox, comb)

1. pencil

3. ring
4. watch
5. pill bottle
6. sunglasses
7. toothbrush
8. crackers
9. wallet
10. sunscreen
11.socks



15. granola bar









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BIOGRAPHICAL SKETCH

Emily King graduated from the University of Florida with a bachelor's degree in

health science. She then spent a year working as a psychometrist at Mayo Clinic in

Jacksonville, Florida and three years as a research project coordinator for various

studies with investigators from Harvard Medical School in Boston, Massachusetts. She

earned a master's degree in clinical and health psychology at the University of Florida in

2006 and then began her doctoral studies in the same program, with a concentration in

clinical neuropsychology. Ms. King concluded her doctoral training with an internship at

the James A. Haley Veterans' Medical Center in Tampa, Florida.





PAGE 1

1 AGE DIFFERENCES AND SPATIAL NAVIGATION IN NOVEL VIRTUAL AND REAL WORL D ENVIRONMENTS By EMILY GREEN KING A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENT S FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010

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2 2010 Emily Green King

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3 To my husband, Adrian, for his absolute love and encouragement

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4 TABLE OF CONTENTS page TABLE OF CONTENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 5 LIST OF FIGURES ................................ ................................ ................................ .......... 6 ABSTRACT ................................ ................................ ................................ ..................... 7 CHA PTER 1 INTRODUCTION ................................ ................................ ................................ ...... 9 Specific Aim 1 ................................ ................................ ................................ ......... 22 Specific Aim 2 ................................ ................................ ................................ ......... 23 2 RESEARCH DESIGNS AND METHODS ................................ ............................... 24 Participants ................................ ................................ ................................ ............. 24 Experimental Procedures ................................ ................................ ........................ 25 Neuropsychological Screening and Spatial Cognition ................................ ...... 26 Self Report Environmental Spatial Ability and Computer Game Experience .... 27 House Navigation Task ................................ ................................ .................... 28 Computer Generated Arena ................................ ................................ ............. 32 Data Reduction ................................ ................................ ................................ ....... 35 House Navigation Variables ................................ ................................ ............. 35 Computer Generated Arena Variables ................................ .......................... 36 Cognitive Test Variables ................................ ................................ ................... 37 3 RESULTS ................................ ................................ ................................ ............... 39 Smart House Navigation Task Performance ................................ .................... 39 Smart House Navigation Task Performance an d cognition .............................. 45 CG Arena Performance ................................ ................................ .................... 46 Relationship between navigation in CG Arena Space and Real Space ............ 49 4 DISCUSSION ................................ ................................ ................................ ......... 52 APPENDIX COGNITIVE TEST BATTERY ................................ ................................ ....................... 62 HOUSE NAVIGATION TASK ITEM LIST ................................ ................................ ...... 64 LIST OF REFERENCES ................................ ................................ ............................... 65 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 72

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5 LIST OF TABLES Table page Table 2 1. Demographic characteristics of experimental participants by group ............ 25 Table 2 2. Cognitive Test Battery ................................ ................................ .................. 27 Table 3 1. Demographic characteristics of old and old old groups ................................ 40 Table 3 2. Mean scores for HNT variables ................................ ................................ .... 43 Table 3 3. C omposite Scores by age group M (SD) ................................ ...................... 50 Table 3 4. Correlation of composite scores by group ................................ .................... 50 Table 3 5. Summary of multiple regressio n analyses examining contribution of CG Arena, age, and FE on HNT performance. ................................ ......................... 51

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6 LIST OF FIGURES Figure page 2 1 Gator Tech Smart House, Gainesville, Florida (HNT) ................................ ......... 29 2 2 House Object Recognition Task (HORT) ................................ ............................ 31 2 3 House Reconstitution Map Task (HRMT). ................................ .......................... 31 2 4 Representations of the C G experimental room ................................ ................. 34 2 5 Representation of target once it is successfully acquired ................................ ... 35 3 1 Mean HNT composite performance by group ................................ .................... 40 3 2 Mean HNT composite performance by group ................................ ..................... 41 3 3 Correctly identifi ed HNT locations by group ................................ ....................... 42 3 4 Correctly identified HNT items by group ................................ ............................. 44 3 5 HNT path length for trial 3 and for delayed recall ................................ ............... 44 3 6 Mean CG composite performance by group. ................................ ...................... 47 3 7 Mean Group CG Arena composite performance ................................ ................ 48 3 8 Composite scores by age group ................................ ................................ ......... 50

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7 ABSTRACT OF DISSERTA TION PRESENTED TO TH E GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL F ULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHI LOSOPHY AGE DIFFERENCES AND SPATIAL NAVIGATION IN NOVEL VIRTUAL AND REAL WORL D ENVIRONMENTS By Emily Green King August 2010 Chair: Russell M. Bauer Major: Psychology Normal aging is associated with a decline in a number of cognitive abilities and nume rous studies document the existence of age related changes in human spatial cognition and behavior. Recent studies using virtual navigation paradigms ha ve shown that performance on these tasks is correlated with performance on cognitive map based way findi ng tasks While the use of virtual environments has made it possible to study navigation performance in a controlled setting, there is limited research that evaluates how performance on virtual navigation tasks translates to real world allocentric navigati on behavior. The broad aims of this study were to empirically evaluate changes in laboratory and real world navigation associated with normal aging and to help lay the foundation for establishing the ecological validity of virtual navigation tasks. Twenty three healthy adults age 20 35 and twenty seven healthy community dwelling adults age 65 and older took part in this study. We used a 3 bedroom, 2 bathroom house to develop an ecologically valid navigation task that was based on theories of allocentric spa tial navigation, as well as computer task modeled after the Morris water maze We investigated group differences in navigation abilities and the

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8 relationship between performance on real world and computer generated navigation tasks. Additionally, each part icipant completed a neuropsychological test battery. Consistent with previous findings, results from this study clearly demonstrated that overall, older adults do not navigate as effectively as younger adults in virtual or real world space. These data are consistent with theories that aging impairs the formation/retrieval of spatial maps of novel environments and spatial knowledge acquired from direct experience in the environment. Second, we were able to demonstrate the relationship between aging a nd poor er real world navigation performance was partially mediated by executive functioning. Third, while significant correlations exist between navigation in computer space and real space, results suggest that tasks with executive functioning demands are more po werful than computer navigation performance and age in predicting real world navigation. Overall, the present report provides additional evidence that adults 65 years and older demonstrate poorer performance on virtual and real world tasks of spatial learn ing and memory tha n do their younger counterparts. This group difference appears to be markedly influenced by executive functioning As a result, age related changes in executive skills should be taken in consideration in future studies of spatial cognitio n. These data also confirm the feasibility of using a real world navigation task in adults over age 65 and emphasize the importance of utilizing real world measures for accurate assessment of cognitive functioning.

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9 CHAPTER 1 INTRODUCTION Normal aging i s associated with a decline in a number of cognitive abilities and numerous studies document the existence of age related changes in human spatial cognition and behavior. These age related changes include, but are not limited to, visuospatial working and l ong term memory (e.g., Park, Lautenschlager, Hedden, Davidson, Smith, and Smith, 2007), the learning of novel environmental layouts (e.g., Kirasic, 1991), the learning of routes (Barrash, 1994), and abilities on mental rotation and spatial visualization ta sks (Hertzog and Rypma, 1991). One complication in studying spatial cognition in aging humans is that many distinct constructs (e.g. wayfinding, landmark knowledge, spatial cognition, sense of direction, mental rotation, point localization, route based lea tests, real world natural environments, virtual environments, and self report sense of direction questionnaires, a re used to study these different constructs, and the conceptual and psychometric relationships among these methods has not yet been fully elucidated. Navigating through familiar and novel environments in order to arrive at a destination is a highly comple x skill that draws upon basic abilities such as learning and recalling the layout of the environment (mental visualization), visual perception (detection of landmarks), spatial perception (determining the direction to take), and map reading (Nadolne and St ringer, 2001). Two commonly described ways of learning the layout of a novel environments are wayfinding ( also referred to as cognitive mapping allocentric navigation or environment dependent navigation ) and egocentric navigation

PAGE 10

10 ( also referred to as rou te learning route following or viewer dependent navigation ). Egocentric navigation has its foundations in route based knowledge. In egocentric navigation, the animal (i.e. rat, human) follows a predetermined series of directions and turns with the goal o f moving toward a specific targeted location. In contrast, allocentric navigation relies on a viewer independent, external perspective (a map like or aerial view) that is thought to allow direct access to a representation or memory of the overall spatial l ayout (Shelton and Gabrieli 2001). Clearly, the ability to remember the location of important elements of the external world (e.g., food caches, locations of predators, shelter) provides great adaptive and survival value. In humans, such abilities contrib ute heavily to independent function in everyday environments (e.g. navigating unfamiliar buildings, streets, and cities). Understanding these abilities and their neuroanatomic substrates has been advanced by extensive investigations, and there exists an a bundant amount of literature speculating how organisms form and retrieve cognitive maps of novel environments and Nadel, 1978). One of the most reliable tests used to test allocentric spatial learning and navigation is the Morris Water Maze (MWM; Morris, 1981), a paradigm that has been mainly used with rodents. In the MWM, rats are placed in a circular pool of opaque liquid that contains a submerged platform that allows escape when the animal finds it and climbs on to it. Surrounding the circular p ool are four walls, each containing a distinctive visual cue that may provide the animal information about relative platform location. Healthy young rats learn to find the platform efficiently and accurately. In contrast, rats with hippocampal damage show severe impairments in the ability to find the platform when compared to sham operated rats (Morris Garrud, Rawlins, and

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11 198 3 ). Further, aged rats perform more poorly on the MWM than do their younger counterpar ts (Wilson Ikonen, McMahan, Gallagher, Eichenbaum, and Tanilla 2003). While age differences in rats have been found in allocentric spatial learning skills (circular MWM), age differences in performance have not been demonstrated on an egocentric spatial task (T shaped MWM; Begaga Cienfuegos, Rubio, Santin, Miranda, and Arias ., 2001). In humans, functional imaging and lesion studies suggest that the hippocampus and associated structures are involved in spatial memory (e.g., Astur Taylor, Mamelak, Philpo tt, and Sutherland 2002; Bohbot Kalina, Stepankova, Spackova, Petrides, and Nadel, 1998; Frakey, 2005) Specifically, a network of structures involved in navigation, including parahippocampal and extrahippocampal regions has been identified. Using fMRI Astur and colleagues (2005) demonstrated bilateral BOLD signal changes in the hippocampus when navigating a virtual radial arm maze Kumaran and Maguire (2005) found preferential engagement of the hippocampus, parahippocampal, restosplenial, and posterior parietal cortices when participants navigated within their city on a computer task. Lesion studies have shown that damage to the posterior parietal cortex, hippocampus, and parahippocampal gyrus cause significant spatial impairment (Barrash, 1998) Althoug h both egocentric and allocentric navigation recruit common networks of brain areas, allocentric wayfinding appears to be more sensitive to hippocampal and parahippocampal function (see Roche, Mangaoang, and Cummings, 2005 for review) and posterior parieta l regions appear to be critical for egocentric navigation (Barrash, Damasio, Adolphs, 2000).

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12 The se findings are relevant to age related changes in allocentric navigation in that MRI and post mortem studies of normal individuals have revealed age related d ecreases in brain weight and brain volume (Dekaban & Sadowsky, 1978, Hubbard & Anderson, 1981, Good Johnsrude, Ashburner, Henson, Friston, Frackowiak 2001, Resnick, Pham, Kraut, Zonderman, and Davatzikos 2001) More specifically, age related losses in t he hippocampus are significantly accelerated relative to gray matter losses elsewhere in the brain (e.g. Jernigan, Archibald, Fennema Notestine, Gamst, Bonnere, and Hesslink, 2001). Given this data, atrophy of the medial temporal lobe structures that is se en in aging may explain why older adults experienced diminished spatial memory and could find it increasingly difficult to learn and navigate (find their way) in unfamiliar environments. Numerous age related anatomical changes in the visual system includin g the lens, pupil, and at the neural level, might also contribute to age differences in spatial abilities. In fact, some researchers have claimed that the se sensory changes are largely responsible for a variety of cognitive impairments in older adults (Sci alfa, 2002). However, w hile there are a large number of age related changes in sensory and perceptual functioning, these changes are more likely to affect tests of attention, memory, and learning, and not complex navigation (Park et al., 200 7 ). Adaptations of the MWM, or computer simulated human analogues, have been developed and results obtained from humans appear to be comparable to those obtained from rats ; older adult participants demonstrate impaired performance on this task compared to young adults (e .g. Newman and Kaszniak, 2000; Moffat, Zonderman, and Re s nick, 2001). Driscoll and colleagues (2003) demonstrated age related deficits in

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13 a virtual MWM task that were associated with decreased hippocampal volume as measured by structural MRI, as well as de creased hippocampal metabolite levels (NAA/CRE ratios) as measured by proton magnetic resonance imaging spectroscopy In an fMRI study, Moffat and colleagues, (2006) found that hippocampal, parahippocampal, and extrahippocampal regions (parahippocampal gyr us and retrosplenial cortex) showed reduced activation in older adults when navigating a virtual MWM task. More recently Moffat and colleagues (2007) demonstrated robust age related differences in virtual MWM performance that were associated with larger hi ppocampal volumes (as measured by MRI) in young, but not old participants. High performance was also associated with larger volume of the caudate nucleus and prefrontal gray and white matter in both groups. The research study described in this proposal uti lizes a computer generated adaptation of the MWM, called the Computer Generated Arena (CG Arena; Jacobs Laurance, and Thomas 1997; Thomas Hsu, and Laurance 2001). Previous s tudies of CG Arena suggest that data obtained from humans in this virtual envir onment are analogous to those obtained from rats in the MWM. As in animals, n ormal subjects use distal spatial cues to navigate to the target, and rearrangement of these cues produces profound impairment in ability to navigate to the hidden target (J acobs, Thomas, Laurance, and Nadel 1998). Subsequent findings from studies of the CG Arena with clinical populations have demonstrated that patients who had suffered mild to moderate TBI showed impaired performance when compared to normal matched controls (Skel ton Bukach, Laurance, Thomas, and Jacobs 2000) and older adults demonstrate impaired performance when compared to younger adults Specifically, younger adults

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14 navigate to the hidden target in less time over trials and spend a greater proportion of time i n the correct quadrant during the probe trial than older adul ts ( Thomas, Laurance, Luczak, and Jacobs, 1999; Laurance, Thomas, Newman, Kaszniak, Nadel, and Jacobs 2002). Data from an fMRI stud y measuring brain activation patterns and CG Arena have suggest ed that environmental learning and spatial memory are related to activation of the right hippocampus in normal healthy controls (Thomas et al 2001). Recently, research in our lab provided additional evidence that older adults demonstrate poorer performan ce on the CG Arena than do their younger counterparts (King, 2006). These data confirm the feasibility of using the CG Arena task in an older population and speak to the possible utility of using this measure in the clinical setting. In clinical practice, rarely engage the patient in actual navigational tasks of any sort. Computer generated environments (e.g. CG Arena) allow for navigation through space without losing experimental control while pres erving features of real world environments that are lacking in traditional neuropsychological tests. Virtual environment technology also permits accessibility to more diverse populations who may otherwise be incapable of participation (e.g., non ambulatory individuals). While the claim has been made that these methods are more ecologically valid than traditional tests, and translate into real world abilities, such real world correspondence has not been adequately established There have been a number of stu dies that have investigated the acquisition of spatial knowledge in real world situations such as streets (e.g. Titov & Knight, 2005), buildings (e.g. Barrash, 1998; D eIpolyi, Hamilton, Petropoulos, Yeo, Brooks, Baumgartner, et al. 2007; Ruddle, Payne, an d Jones, 1997) and towns (e.g. Maguire, Burke, Philips, and

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15 Staunton 1996). In one such study, investigation of navigation in a virtual and real world 2 level outlet mall, found that when compared to unexposed controls, both young and old participants wer e able to transfer understanding of the spatial layout of a virtual representation of the outlet mall to successful performance in the real world equivalent (Foreman Stanton Fraser, Wilson, Duffy, and Parnell 2005). In addition, younger experimental part icipants remembered the location of specific targets better than did older participants While this study demonstrated virtual technology is not a barrier to examining the ecological validity of computer navigation tasks in the elderly, there have been few studies that examine the ecological validity of virtual human analogs of animal models (e.g. virtual MWM) and even fewer that study environments that older adults frequently encounter. As the focus of assessment in the elderly population has shifted from a diagnostic to a more functional approach (e.g. answering questions about living independently, making financial decisions, driving, or continuing at work), some considerable problems with the generalizibilty of the testing situation to the real world ha ve come to light. These include, but are not limited to the sterility of the testing environment, the obstruction of compensatory strategies, non cognitive factors that may interfere with testing performance, and the abilities that the tests are designed t o measure might not represent skills that are used in everyday functioning (Sbordone, 1996). In a limited number of studies, age related differences in real world spatial navigation have been investigated and normal older individuals experience significant deficits. Results from a real world supermarket navigation study (Kirasic, 1991) indicated that while younger adults acquire spatial information in a novel environment more quickly than do older

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16 adults, no age related differences were found in the executi on of a familiar route. In contrast, Monacelli and colleagues (2003) found significant age differences on a more complicated hospital route execution task in a familiar setting In another route learning study in a novel environment, older participants had relatively greater difficulty retracing the route and ordering landmarks through a two level medical setting, but were equally good at recogni zing the landmarks o ccurring on the route (Wilkniss, Jones, Korol, Gold, and Manning, 1997). To further validate human analogs of the MWM, several real world large scale designs have been tested. Older adults have been found to demonstrate impaired performance when compared to younger adults (Newman & Kaszniak, 2000) and patients with lesions to the medial temporal lobes (selective thermocoagulation to alleviate intractable epilepsy) demonstrate that the right parahippocampal cortex is MWM task. More recently, Kalov and colleagues (2005) found that similar results can be obtained from a real world MWM and a map view computer version when comparing performance between normal controls and individuals with AD. While performance on these real life analogue tasks can aid in drawing conclusions from rat performance, they have n ot yet firmly navigation age differences in natural real world settings is still needed. Our previous research correlated CG Arena performance with real life navigat ion performance (hospital route learning task) and demonstrated a significant positive relationship of CG Arena performance and real wo rld performance (King 2006). However, as with the studies mentioned above, this task was a measure of egocentric

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17 or rout e learning ability. A better way to establish the ecological validity of the CG Arena would be to demonstrate that performance on this task is similar to performance on a real life measure of allocentric navigation This is a key aim of the current investi gation. Logistical factors are largely responsible for the lack of research evaluating age related differences in spatial navigation in everyday settings (i.e. other than hospitals or office buildings). By not evaluating performance in naturalistic enviro nments that older adults are ikely to encounter in everyday life, we may be missing decrements in cognitive functioning that have a great impact on real life functioning. In contrast, we may be overemphasizing age differences that are found in the laborato ry (especially with computerized tasks) that actually are not critical in everyday life. In keeping with the emphasis on application of spatial skill (function), instead of description (ability), the House Navigation Task (HNT: See methods section for deta ils) was developed. The HNT provide s a real world natural measure of allocentric spatial navigation within a home environment. The HNT incorporates key procedural conditions of supermarket tasks developed by Kirasic (1991), but extends the focus to measuri ng map based (allocentric ) wayfinding as opposed to route based navigation. It also incorporates aspects of the Apartment Test or Memory in Reality Test (MIR: Johansson, 1988/1989), an ecologically valid alternative to declarative memory tasks, by using everyday items that are placed within a meaningful context. The HNT has more face validity than navigation tasks commonly used in research protocols It requires participants to participate in a real life scenario of packing for a trip and remembering the location of things in their house. As a result, older adults may be more inclined to participate (with maximum effort) in the HNT as it is more relevant to

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18 everyday functioning and contains a rich array of stimuli. In addition, it not only employs isolate d components of abilities (i.e. attention, language, etc.) but also draws on a combination of skills simultaneously (e.g. object memory, spatial memory, navigation, spatial mapping, etc.). One other advantage of measuring spatial ability in a naturalistic envi ronment is that participants can use compensatory or adaptive strategies they normally use in tasks of this type. Typical standardized testing does not allow for use of compensatory strategies that older adults may utilize in the real world and thus whate ver age differences exist in underlying abilities may be magnified. Therefore, traditional standardized tests may not accurately be measuring the degree of cognitive age differences (or lack thereof) that older adults are experiencing in everyday abilitie s. We believe that the HNT is more generalizable to real life navigation, because it is conducted in a natural setting and participants are engaging in an activity that is a ool, work, vacation, etc.) The main goal of task development was to determine whether participants employ the use of a created spatial map to remember the location of the objects in the house. The objects and landmarks were designed to be more meaningful t o older adults than pictures on a wall (i.e. as in real world MWM analogues) or arbitrary routes through hospital corridors. The HNT measures how participants (young and old) learn and remember (route vs. spatial map), as well as navigate in novel environm ents. The goal of development was to further evaluate age differences that are found in virtual MWM

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19 task performance that may be attributed to several other factors (e.g. cohort effects lack of experience with technology, thereby leading to motivational f actors, etc.). A large body of data (e.g., Nadolne & Stringer, 2000) suggests that traditional psychometric tests of spatial ability (e.g. tools that measure visualization, spatial orientation, mental rotation, and map reading) tap into domains of visuosp atial skills that are not necessarily crucial to spatial navigation ability (as above, the ability to move around a new environment and learn the layout). For instance, Sonnenfeld (1985) demonstrated that paper and pencil spatial performance was not relate d to true wayfinding ability. In this study, participants from Southeast Alaska, including professional guides, fishing boat captains and pilots, were found to have some of the poorest performances on a battery of paper and pencil spatial tests yet were e xpert navigators in real life Kirasic (1991) also demonstrated no correlation between cognitive task performance in a laboratory and performance in a real world novel environment, as well as no significant relationship between performance on traditional p aper and pencil based psychometric measures and navigation ability in elderly individuals (Kirasic, 198 9 ). More recently Hegarty and colleagues (2006) found relatively low positive correlations of paper and pencil measures of spatial abilities with measur es of learning from direct experience with the environment (campus building route execution task). DeIpolyi and colleagues (2007) found that while a real world route execution task through an ambulatory care center could distinguish AD and MCI individuals from normal controls, standard neuropsychological measures could not

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20 Relationships among virtual environments and paper and pencil measures have also been examined. Moffat and colleagues (1998) fo und significant positive correlations between scores on psychometric tests of mental rotation, map learning, and spatial orientation, and performance on egocentric virtual maze learning in humans. Our previous work (King, 2006) demonstrated that while some psychometric paper and pencil measures were significantly correlated with CG Arena performance, a majority of the measures were not. The significant correlations that were found were of modest proportions, and suggest that while some tests may tap some ov erlapping spatial ability, CG Arena appears to measure a unique skill that is not otherwise accounted for by our current clinic al measures (King 2006). The results of our previous work also demonstrated significant positive correlations between performanc e on the mental rotation task and overall CG Arena performance, suggesting that the two tests share overlapping information processing demands. This result is consistent with findings that demonstrate significant correlations with the probe trial on a virt ual MWM task a nd mental rotation (Astur, Tropp, Sava, Constable, and Markus 2004) and suggests that age related differences in computer navigation might also be explained by changes in other cognitive processes. Influences on spatial navigation from outs ide the medial temporal lobe have been investigated and evidence that virtual MWM tasks are mediated by frontal systems has been found. In one study, Moffat and colleagues (200 7 ) found that gray and white matter volumes of the prefrontal cortex was positiv ely associated with task performance and two measure of executive functioning were also associated with virtual MWM. In addition, measures of executive functioning have been shown to make unique (and

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21 independent) contributions to decline in everyday functi oning in older adults (Farias, Cahn Weiner, Harvey, Reed, Mungas, Kramer, et al., 2009). Specifically, this suggests that age related differences in real world abilities can be somewhat accounted for by cognitive measures of executive functioning. The rel ationship between performance on a virtual MWM and age related structural brain changes has been demonstrated in several studies, and is not the purpose of this investigation. Successful Morris water maze performance is dependent on the hippocampus and sur rounding medial temporal lobe structures in animal studies. In humans, older adults with age related hippocampal atrophy have consistently been found to demonstrate age related deficits on virtual MWT and individuals with structural temporal lobe damage al so demonstrate impaired performance. Therefore, we can draw conclusions that vMWM task performance is also sensitive to medial temporal lobe insult. Based on these findings, we would therefore expect the underlying brain changes seen in aging to be associa ted with poorer real world allocentric navigation performance as well. O lder adults demonstrate spatial navigation impairment in a laboratory setting, and we know that they demonstrate impaired performance when compared to younger adults in the real world egocentric navigation (e.g. route learning). However, we still do not have adequate information about group differences in real world spatial mapping tasks (allocentric navigation), and we know very little about the correlation between laboratory MWM task s and real world performance While the use of virtual environments has made it possible to study wayfinding performance in a controlled setting, and capture abilities that are not otherwise measured by standard clinical psychometric instruments, to our kn owledge, there is no research looking at how

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22 performance on virtual MWM tasks translates to real world allocentric navigation behavior. The CG Arena is so unlike everyday situations, we cannot really generalize performance on this task to everyday life spa tial navigation. Examining this relationship is a crucial step that must be taken in the development of clinical virtual navigation tasks. Therefore, this study will help lay the foundation for establishing the ecological validity of virtual MWM tasks. In sum, our previous research ( King, 2006 ) sought to evaluate various forms of spatial cognition and navigation in young and old adults and to examine the relationship to real world measures of spatial navigation impairment. It served as an initial step in c haracterizing particular abilities, and a few of the limitations will be addressed in this study. The two major aims of the study are to empirically determine (1) whether there are age group differences in real world natural spatial navigation performance and if so, what cognitive factors mediate these differences; (2) the relationship with navigation in real space with that in virtual space. Specific Aim 1 To characterize differences in real world spatial navigation performance between two healthy age gr oups (20 35 and 65 and older). Based on previous real world large scale navigation studies (e.g. route execution in hospital corridors and supermarkets), we predicted that elderly participants would show impaired map based navigation performance when compa red to healthy young participants. Contributions of domain specific processing capacity (executive functioning and mental rotation) to spatial navigation were also investigated

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23 Specific Aim 2 To examine the relationship between navigation in a natural en vironment with navigation in a virtual environment (C G Arena). It is predicted that better performance on a real world navigation task will be associated with better performance on the C G Arena. Contributions of other cognitive processes to this relation ship were also examined.

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24 CHAPTER 2 RESEARCH DESIGN AND METHODS Participants Participants were fifty individuals (27 aged sixty five and older, and 23 aged twenty to twenty eight ) recruited from Gainesville, Florida and surrounding areas. Recruitment fo r the older group took place via community advertisements (e.g. newspaper, flyers, and newsletters). Recruitment strategies for young healthy adults focused mainly on University of Florida advertisements (e.g. flyers). The main goal of recruitment was to o btain a heterogeneous and representative sample of community dwelling older adults, without cognitive impairments, and a sample of younger adults matched on the basis of gender and education. Both the young and old groups were cognitively intact and had no history of neurological insult or psychopathology. All potential participants were screened via telephone in order to exclude individual participants based on the following criteria: (1) dementing illness or other neurological significant head injury, or a mild head injury with any loss of consciousness within the past year (3 ) stroke or TIA, (4) heart attack or myocardial infarction within the past year, (5) orthopedic or he art surgery within the past year (e.g. hip replacement, CABG), (6) cancer treated with cranial radiation or chemotherapy, (7) history of learning disability or developmental disability, (8) severe uncorrected vision or hearing impairments, (9) history of i npatient psychiatric treatment, (10) history of drug or alcohol abuse sufficient to affect health, work, or family functioning, (11) unwillingness to participate in two testing sessions, or (12) inability or unwillingness to walk five minutes without rest, five separate times within an hour time period. Participants gave written informed consent

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25 according to university and federal regulations. All participants who completed the research protocol received $50. A total of fifty one participants were initially enrolled in the study and completed all study procedures. The data from one participant was not included in the final analysis due to not meeting criteria for being cognitively intact (significantly impaired memory, language, visuospatial, and executive f unctioning performance on testing). Demographic variables for the remaining 50 participants (16 females and 11 males in the older group and 14 females and 9 males in the younger group) are shown in Table 2 1. The racial/ethnic composition of the sample wa s 17 Caucasian and 6 Hispanic males and females for the younger group and 100% Caucasian for the older group. The two groups did not differ in education [ t (48) = .402, p > .05] or IQ [ t (48) = .946, p > .05] Groups did differ statistically in MMSE score [ t (48) = 2.68, p .01], however, the mean difference i s not large (29.39 in young group and 28.67 in old group) and not clinically meaningful. Table 2 1 Demographic characteristics of experimental participants by group Measure Young Adults Mean (SD) Olde r Adults Mean (SD) Number of Participants N = 23 N = 27 Age 22.83 (2.25) 75.78 (7.55) Education 15.26 (1.42) 15.51 (2.95) IQ 109.52 (6.29) 111.41 (7.59) MMSE 29 3 9 (.72) 28.67 (1.11) Note: IQ= Intelligence quotient as measured by the Wechsler Test of Adult Reading (WTAR) Experimental Procedures Following the completion of the telephone screening and consent procedures, all eligible participants were invited into the psychology laboratory. Testing was conducted over two sessions.

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26 First testing sessi on. was assessed using the Neuropsychological Screening and Spatial Cognition battery and the computer generated navigation task described below. Additionally, participants completed mood inv entories and provided lists of their current medications. Second testing session. Within one month of the first testing session, participants completed a real world navigation task, as well as self report measures of spatial navigation impairment and test s of spatial cognition. Neuropsychological Screening and Spatial Cognition This battery of tests consisted of standardized tests of neurocognitive functions in several domains. The elements of the battery are listed below (Table 2 2) and are described in Appendix A. All tests are from peer reviewed sources, and each measure a particular domain of neuropsychological function commonly recognized in the neuropsychological literature. The composition of this test battery was determined with three goals in min d. First, we wanted to be able to distinguish healthy older adults from older adults with significant cognitive impairment, paying particular attention to verbal and nonverbal memory functioning. Second, with recent findings of vMWM tasks being somewhat de pendent on contributions from frontal lobe areas and with numerous findings of age related changes in this region, tasks thought to utilize higher order frontal/executive skills were included to mediate age related variance on navigation performance. Third the particular spatial cognition assessment instruments represent either experimental measures specially developed to evaluate the domains of interest, or are well established and widely used measures of those domains. Tests of mental rotation were there fore included

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27 Table 2 2. Cognitive Test Battery Self Report Environmental Spatial Ability and Computer Game Experience In evaluating age differences in spatial cognition, it is important to take into consideration the likelihood that age groups may difference in spatial ability could be due to a variety of other cohort factors such as access to education, socio economic status, and cultural expectancies (e.g. males mathematical, and engineering sciences, etc.). By incorporating these measures, it may be possible to clarify some of the cohort effects that may be present in the older group of individuals (e.g. lack of spatial navigation and computer experience).

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28 Th e Santa Barbara Sense of Direction Scale (SBSOD ) is a 27 item self report demonstrated good psychometric properties (Hegarty, Richardson, Montello, Lovelace, and Subbiah, 2002) The Everyday Spatial Questionnaire (Skelton et al., 2000) is a 13 item self report eft Responses were rated on a 10 The frequency of playing computer games was rated on a 4 point not (based on Quaiser Pohl et al. 2006). Joystick experi ence was also rated using this same 4 point scale House Navigation Task The House Navigation Task (HNT) is a real world natural environment navigation task developed for use in this study. The HNT assesses learning the spatial layout of a one story, 3 be droom, 2 bathroom home environment, previously unfamiliar to subjects, and rich with visual cues The home has a kitchen, den, living room, and back patio; and is located in Gainesville, Florida (Figure 2 1). Participants located 16 items needed for a hypo thetic trip. Four of the items were placed in commonly found locations within the house (e.g. sunscreen in medicine cabinet in bathroom), and 12 of the items were placed in unusual locations (e.g ., gauze pads in kitchen cabinet; See Appendix B for list

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29 of items). Most of the items were not in plain view so the participants could not just several other items scattered throughout the house that were not on the list. F igure 2 1. Gator Tech Smart House, Gainesville, Florida (HNT) At the beginning of the task, participants were told to imagine they are packing for a trip, and that they will need to remember the location of a number of items. At a leisurely pace, the exam iner led the participants through the house giving a tour of the layout and pointing out the items participants needed to locate for their trip. The presentation of the items was conducted in such a way as to not represent the shortest or most efficient ro ute to reach all of the items; however it was not presented in a manner that would confuse or trick participants. The examiner pointed out two items per room/area of the house at a time starting in the master bedroom and continuing towards the other end of the house. When the other end of the house was reached, the examiner then led the participant through the remainder of the house to identify/point out the remaining items needed for the trip. Participants were then given the list of items presented in ran dom order and asked to locate the items as efficiently as possible, as

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30 were also required to tell the experimenter the item or location they would be travelling to next before going to get it This task was repeated for a total of three trials, starting from a different location around the house on each trial, and with the list of items in different order. If the participant was unable to locate all of the items (using th e list) on any of the trials, the examiner showed the participant the location of any unlocated item on each trial. Without being previously told about the delay, participants were asked to repeat the task two more times 25 35 minutes after completion of t he last trial. The first delay trial was completed without the assistance of the list of items and the fifth trial (or second delay) was completed with the use of the list. The starting points for all five trials (learning and delay) were different. After completion of the entire task (including recognition and reconstitution measures), participants were asked to walk through a specific route throughout the house (one hundred feet in length) in order to obtain a trial measuring baseline walking speed. This enabled us to reference speed of ambulation to a common metric for each participant. HNT s coring was based on 1) number of correctly identified locations 2) number of correctly identified objects and 3) Path length (total distance ambulated in completing t he task) After the delay trial, participants were administered two cognitive measures related to the HNT: a house object recognition task (HORT) and a house reconstitution map task (HRMT). The house object recognition task (HORT) required participants to correctly identify and distinguish photographs of objects that were in the house from some that were not (Figure 2 2). Following completion of the HORT, participants were then led into an area of the house where they had not yet been (the very large walk in

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31 master bedroom closet), and they completed the house reconstitution map task (HRMT). The HRMT required participants to appropriately place laminated photographs (approximately 1.5 squared inch in size) of the items on a large (approximately 3 feet by 4 feet) floor plan style map (Figure 2 3). To test whether the placement of the items was based on a developed spatial map, the furniture/appliance arrangement on the HRMT task map was altered so that it did not exactly match the actual arrangement in the ho use (i.e. some of the furniture/appliances were not included and some were placed in a different location within the room). Participants were asked to place the objects where they were located in the house (includ ing correct relation to other items) regard less of the placement of the furniture and the appliances. The exact locations of the item placement s were recorded on a small version of the floor plan map by the examiner. Figure 2 2. House Object Recognition Task (HORT) Figure 2 3. House Recons titution Map Task (HRMT).

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32 Computer Generated Arena The Computer Generated Arena (CG Arena; Jacobs et al., 1997, 1998) is a computer based analogue of the Morris Water Maze task (MWM) that is administered on a desktop or laptop computer. The participant is asked to use a joystick to navigate through a virtual MWM in order to find a hidden target. The stimulus environment is analogous to the circular tank placed within door, a window, or a pattern that together serve as distal cues to assist the participant in navigati ng toward the hidden platfor m target (Figure 2 4). The placement of the walls relative to the target remains constant over all the experimental trials. The target is a small square located on the floor of the Arena. The task itself is modeled after the classic MWM paradigm. On each t rial, the participant starts from a different point in the circular arena. The CG Arena protocol began with a set of practice trials. During th e se trials, the target was visible, and the participant was asked to use the joystick to navigate to it as quick ly as possible. The target was in a different place in the room on each practice trial. Over the course of the practice trials, participants were exposed to a minimum of five minutes practice time in order to familiarize themselves with the use of the joys tick. Participants were administered the appropriate number of practice trials until the examiner judged that all participants were starting the experimental trials with an equivalent level of understanding of the task, as well as familiarity with the joys tick.

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33 Immediately after completing the set of practice trials, participants were administered a set of acquisition trials. During th e se trials, the participants entered into a new virtual room for eight trials. The target was invisible, but remained in th e same location across acquisition trials. During the first trial, the participant navigated through the environment until the invisible target was found. Once this occurred, the target became visible (Figure 2 5) and was paired with an auditory clicking s ound The target forcing the participant to look around the Arena environment. This procedure was repeated for the remaining seven trials. In the event that the parti cipant was unable to independently locate the target within 120 seconds, the examiner assisted the participant. Such assistance was given only on the first two acquisition trials. The starting position within the Arena was randomized for the first six acqu isition trials. In order to measure learning in the data analysis, Trial 7 had the same starting position as Trial 2, and Trial 8 had the same starting position as Trial 1. Immediately following the 8 acquisition trials, the participant was administered a probe trial. On this trial, the hidden target was removed from the virtual room, unbeknownst to the participant. This final trial is an analogue of the standard probe trial repeate dly swims around the anticipated target location, searching for it. Upon completion of the probe trial, the participant was presented with a blank screen, indicating the end of the CG Arena portion of the testing session. Participants were then immediately debriefed about the removal of the target on the probe trial.

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34 For each trial, several dependent measures, including the length of the navigation path, the latency to find the hidden target, the time spent in each quadrant of the arena, and whether the tar get was actually found, were automatically recorded by the CG Arena software. After the probe trial, participants were administered two paper and pencil measures related to the CG Arena task: an arena reconstitution task (ART) and an object recognition ta sk (ORT). The ART required participants to recon struct the CG experimental room by appropriately placing icons representing the four walls of the room, the objects on those walls, and the target onto a sheet of paper. The ORT required participants to corre ctly identify and distinguish objects that were on the walls in the experimental room from a group of objects, some of which were in the room and some of which were not. The entire CG Arena protocol, including the ART and ORT tasks, took approximately 30 4 5 minutes to complete. Figure 2 4. Representations of the C G experimental room as seen by the participants

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35 Figure 2 5. Representation of target once it is successfully acquired Data Reduction House Navigation Variables The number of correct locatio ns and number of correctly identified objects were recorded. For each trial, a participant could score a total of 16 points for number of correctly identified locations and number of correctly identified objects. This scoring procedure was in place to dist inguish memory for objects from memory for location in examiner followed participants as they navigated through the task in order to collect ath length, or the distance of the length to acquire the items as efficiently as possible, was calculated a priori from each starting point for interpretive purpose use. This length was consistent across starting points and varied by on ly 10 feet (278 288 feet). If all items were not located on a given trial, the path length score was calculated as a proportion of the number of items found (X) so that participants would not have shorter path lengths if they did not locate all items. Number of items found 16 ___________________ = ______ Path length X

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36 The house object recognition task (HORT) consisted of 42 recognition items: Sixteen of the items were the items located during the experiment, ten were items that were in plain vie w throughout the house located near the target items, and sixteen items were not located in the house. Two scores were derived for this task. One was a total recognition score based on correctly distinguishing items that were in the house (both critical an d incidental) from those that were not. The second was based on correct identification of incidental items. Two scores were derived from the house reconstitution map task (HRMT). One score was based on the following criteria: 1) item in correct room, 2) i tem in correct relation to other items, and 3) item in correct location. A point was awarded for each criterion for each item, for a total of 48 points. The second score for this task was based on deviation of the item from the correct location, measured i n centimeters. In order to weigh the various HNT dependent variables equally, a composite dependent variable (DV) was created from the mean of the z scores earned on each variable ( path length ( trials 1 3), path length delay; correctly identified locations ( trials 1 3), correctly identified locations delay, correctly identified objects ( trials 1 3), and correctly identified objects delay, HORT total recognition and incidental score, HRMT total score and deviation ). The basic statistical procedures we used t o create a composite DV are outlined in Rosenthal (1991). Computer Generated Arena Variables Path Length was recorded by the distance travelled over the course of the trial, either when the target was found or until the trial ended. The sum of target ac quisitions was recorded by counting the number of times the participant found the invisible target over the course of the acquisition trials. Dwell time on the probe trial was created by

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37 calculating the proportion of time the participant spent in the targe t quadrant (the quadrant where the invisible target had previously been located) during the probe trial. The ORT score was created by summing up the total number of correctly identified objects and correctly discerning objects that were not located in the room. The ART score was created by using a template that awards points to objects placed correctly in the room relative to the actual location. In order to weigh the various CG Arena dependent variables equally, a composite dependent variable (DV) was als o created from the mean of the z scores earned on each variable ( path length; total target acquisitions across invisible trials; dwell time on probe trial; ORT score; ART score ). Cognitive Test Variables A cognitive flexibility (frontal executive functio ning) variable (FE) was created from the mean of the z scores on DKEFS Letter Number Sequencing (switching connecting circles alternating between numbers and letters with DKEFS motor control portion time substracted from time ) and WAIS III Digit Span Backw ard (working memory reciting increasingly longer strings of numbers in reverse). Raw s cores on each of these measures were first converted to standardized scores based on the mean and standard deviation of the entire group. A psychomotor skill and speed v ariable was created using the same procedures as above with the Finger Tapping dominant hand and the DKEFS motor control portion raw scores. Mental rotation was measured using Space Thinking Flags (Thurstone & Jeffrey, 1984). This test requires particip ants to view a picture of a flag and judge which of the six alternative test figures are planar rotations of the flag. The score was based on the

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38 number of items answered correctly within five minutes. The Mental Rotation Test (Vandeberg & Kuse, 1978) was not used in calculation of this variable. Most of the old adults and a few of the young adults were unable correctly answer any of the practice items and therefore it is not considered an accurate representation of mental rotation ability.

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39 CHAPTER 3 RE SULTS Smart House Navigation Task Performance We began data analysis by evaluating differences in young and old adult performance on the real world navigation task. In examining age group differences using a MANOVA, with age group as the independent variab le and performance on the House Navigation Task (HNT) as dependent variables, a significant main effect for age was found Wilks Lambda F (10,39) = 7.358, p < .001, 2 = .65. U nivariate comparisons confirm that the young group found the items in shorter distance (length) F (1,48) = 25.56, p < .001, 2 = .35, and more often (number of correctly identified locations F (1,48) = 36.02, p < .001, 2 = .43, and number of co rrectly identified items, F (1,48) = 55.27, p < .001, 2 = .54, on the acquisition trials (1 3). Age differences were also seen on the delayed recall trial, in which the young group again found the items in a shorter distance, F (1,48) = 19.13, p < .001, 2 = .29, navigated to the location more often, F (1,48) = 17.79, p < .001, 2 = .27, and correctly identified the item in that location more often, F (1,48) = 23.21, p < .001, 2 = .33. The old and young group differed in their performances on the House Object Recognition Task (HORT) total score, F (1,48) = 5.19, p = .016, 2 = .11, HORT incidental item score F (1,48) = 7.55, p = .008, 2 = .14, on the House Reconstruction Map Task (HRMT), F (1,48) = 18.05, p < .001, 2 = .27, total score, and on the HRMT Error Me asurement score F (1,48) = 6.26, p = .016, 2 = .12. The composite HNT variable, which represented overall performance, was then subjected to an independent samples t test to test for group differences in allocentric navigation. Significant group differen ces were found t (29.74) = 7.21, p < .001, d =

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40 2.74. Overall, older participants performed significantly worse on the HNT composite than did their younger counterparts (Figure 3 1). Figure 3 1. Mean HNT composite performance by group, t (29.74) = 7.21, p < .001, d = 2.74. To further examine age group differences in House Navigation Task (HNT) older). Demographic variables for the 27 old participants are shown in Table 3 1. Table 3 1 Demographic characteristics of old and old old groups Measure Young Old Adults Mean (SD) Old Old Adults Mean (SD) Number of Participants N = 13 N = 14 Age 69.15 (2.45) 81.93 (4.90) Education 15.00 (3.03) 16.00 (2.90) IQ 109.31 (7.45) 113.36 (7.59) MMSE 28.69 (.94) 28.64 ( 1.28 ) Note: IQ= Intelligence quotient as measured by the Wechsler Test of Adult Reading (WTAR) An analysis of variance (ANOVA) was used to test for differences among the three age groups with the composite HNT vari able as the dependent variable and age group as the independent variable. Overall performance on the HNT differed significantly across the three age groups, F (2, 47) = 39.18, p < .001, 2 = .63. There was a

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41 significant linear trend, F (1, 47) = 77.83, p < 001, indicating as age group increased (young
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42 significant covariate F (1, 47) = 1.43, p = .238, and therefore significance was maintained when controlling for it F (2, 47) = 26.63, p < .001, 2 = .53. Table 3 2 provides the mean scores for each of the HNT variables broken down by age group. The mean scores are highest in the young participants, as expected, and a ceiling effect is evident. Figure 3 3 shows the age differences in correctly iden tified locations across the three trials and on the delay recall trial. All age groups showed learning over trial, losing very little information on the delayed recall trial. A paired samples t test was conducted to compare the number of correctly identifi ed locations in the delay recall trial to trial 3. There was a not a significant difference in any of the age groups (Young: t (22) = 1.00, p = .3.28; Old; t (12) = 1.00, p = .337; Old Old: t (13) = .79, p = .444). In addition, the oldest old were able to r emember an average of 92% of the locations on the learning trials and remember 88% of the locations on delay recall. No any of the age groups for correctly identifi ed items (Fig 3 4) or path length (Fig 3 5). Figure 3 3. Correctly identified HNT locations by group

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43 Table 3 2. Mean scores for HNT variables

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44 Figure 3 4. Correctly identified HNT items by group Figure 3 5 HNT path length for tria l 3 and for delayed recall

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45 Smart House Navigation Task Performance and cognition Mediation analyses were conducted to examine whether performance on the neuropsychological screening and s patial cognition battery was associated with performance on the HNT. In previous studies, m ental rotation and executive functioning (mental flexibility) abilities have been found to influence both virtual and real world environmental layout learning. First evaluating frontal/executive cognitive performance (FE), formal si gnificance tests of the indirect effect of the indirect effect of mental rotation were conducted by means of the Sobel test and a bootstrap approach (explanation and macro can be found in Preacher and Hayes, 2004). Results of both procedures indicated that fron t al/executive cognitive performance exerted a significant (p < .05) indirect mediational effect on the relationship between age and HNT performance. To further examine the degree of mediation, we employed a four step, ordinary least squares approach ( Baron and Kenney, 1986). Step 1 indicated a significant total effect of age on .73, p <.001); Step 2 indicated a significant effect of age of .58, p <.001); and step 3 indicated a significant effect of FE on HNT, while p = .03). Thus, the first three steps in establishing mediation were satisfied, supporting the results of the tests of the indirect effect. Step 4 revealed that although HNT performance decreased with increasing age when contr olling for FE, .60, p <.001), indicating that FE partially mediates this relationship. These results demonstrate that age and FE collectively account for 65% of the variance ( R 2 = .65) in house navigation performance. Formal significance tests of the indirect effect of mental rotation did not find a significant indirect effect of age on navigation performance through mental rotation (p >

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46 22.13, p < .001). Whe n controlling for age, the significant effect of Mental Rotation on HNT did not remain p = .1734). However, the significant negative relationship between age and .73) does becomes smaller when controlling for mental rotati .63, p< .001) CG Arena Performance The next part of our statistical analysis examined differences in overall CG Arena performance between older and younger adults. Using a MANOVA with age group as the independent variable and individual variabl es of CG Arena performance as the F (7,42) = 1120.02, p < .001, 2 = .83. Univariate analyses showed that the young group found the target in shorter distance (path length), F ( 1,48) = 67.95, p < .001, 2 = .59, more often (number of target acquisitions, F (1,48) = 50.59, p < .001, 2 = .51, and spent a greater percentage of time in proximity to the target on the acquisition, F (1,48) = 141.97, p < .001, 2 = .75, and probe trials, F (1,48) = 49.19, p < .001, 2 = .51. On measures administered after completion of the computer task, the young group demonstrated significantly better overall performance on the Arena Reconstruction Task (ART), F (1,48) = 13.87, p = .001, 2 = .22, and pla ced the target in the correct quadrant more often than the old group, F (1,48) = 5.25, p = .023, 2 = .10. The old and young groups did not differ in their performances on the Object Recognition Task (ORT), F (1,48) = 1.01, p = .319; M young = 12.67, M old = 13.17. Both the young adult and older adult groups found the target consistently when the target was visible on the practice trials. To exclude the possibility that the group differences observed in CG Arena performance was secondary to greater joystick

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47 e xperience and computer familiarity in the young group, a mean path length score was calculated for all visible practice trials. This variable did not reveal reliable age group differences, t (48) = .783, p = .438. Joystick experience as assessed by question naire also did not reveal reliable age group differences, t (48) = .384, p = .702. Computer game experience as assessed by questionnaire did reveal reliable age group differences, t (33.21) = .4.50, p < .001, and was therefore used as a covariate in the fo llowing analyses. An analysis of covariance (ANCOVA) was performed with CG Arena composite as the outcome variable, age group as the fixed factor, and computer game experience as the covariate. The results of the ANCOVA indicated that computer game expe rience was not a significant covariate, F (1,47) = .105, p = .747, and did not predict CG Arena performance. There was a significant effect of age on overall CG Arena performance, F (2, 47) = 47.41, p < .001, 2 = .74, in that older participants performed si gnificantly worse on the CG Arena composite than did their younger counterparts (Figure 3 6). Figure 3 6. Mean CG composite performance by group F (2, 47) = 47.41, p < .001, 2 = .74.

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48 To further examine differences in age group CG Arena performance, the old group was again divided used to test for differences among the three age groups with the composite CG variable as the dependent variable and age group as the independent variable. Over all performance on the CG Arena differed significantly across the three age groups, F (2, 47) = 65.89, p < .001, 2 = .74. There was a significant linear trend, F (1, 47) = 122.58, p < .001, indicating as age increased (young
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49 baseline motor testing was examined. Analyses of psychomotor skill and speed (MS) revealed expected significant age group differences, F (2, 47) = 20.69, p < .001, with a significant linear trend, F (1, 47) = 38.126, p < .001, indicating as age increased (young
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50 Table 3 3. Composite Scores by age gr oup M (SD) Figure 3 8. Composite scores by age group Table 3 4. Correlation of composite scores by group Age Group HNT Arena Arena FE FE HNT Young 0.181 0.128 0.074 Old 0.481* 0.258 0.525** Old Old 0.012 0.243 0.308 Note: p = .096, ** p = 0.066

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51 To determine the relative contribution of CG Arena, age, and FE to HNT performance, this relationship was examined further by using hierarchical multiple regression analyses. At the first step, we entered the composite CG Arena variable. Second, we entered age. Based on the evidence of frontal executive functioning (FE) partially mediating the effects of age on HNT, we also entered FE in the second step. Using this method, a significant model emerged in the first step with CG Arena predic ting HNT, F (1, 48) = 55.03, p < .001. The second model also significantly predicted the outcome variable, HNT, F (3, 46) = 30.084, p < .001, accounting for 67% of the variance in HNT performance. Once age and FE were added to the model, CG Arena no longer s ignificantly predicted HNT performance. This, along with the significant large correlation of age and FE ( r = .58, p < .001), suggests that FE does a better job of predicting real world navigation than does computer navigation. Results are given in Table 3 5. Table 3 5. Summary of multiple regression analyses examining contribution of CG Arena, age, and FE on HNT performance. Note. R 2 R 2 = .134 for Step 2 (p < .05). p < .05 ** p < .01 *** p < .001

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52 CHAPTER 4 DISCUSSION The broa d aim of this study was to evaluate changes in spatial memory and cognition associated with normal aging. The results of the present study demonstrate that real world environments can be used to assess age effects in spatial navigation performance. We used a 3 bedroom, 2 bathroom house to develop an ecologically valid task modeled after theories of allocentric spatial navigation (House Navigation Task; HNT). This environment was easily understood, well organized, and thereby was predicted to elicit optimal performance. The performance of young, old, and old old adults improved with each trial, each locating more items with shorte ne d distance travelled over trials. These results provide evidence that real world environment al support may enhance memory perform ance (especially in older adults). However, despite improvement in performance over time and retention of this information on delayed recall, the two groups of older adults were clearly impaired in overall performance relative to the young group with incr easing impairment in the old old group compared to their young old counterparts. Moreover, on the delayed recall portion of the HNT, 96% of young adults, 65% of old adults, and 14% of old old adults were able to correctly identify all 16 locations. When ex amining individual variables of the HNT, some interesting patterns emerged. As represented by overall path length, an increase in age was accompanied by more frequent repeat visits to locations already visited within a trial and less efficient acquisition of the items (i.e. going back and forth between rooms). Older adults would frequently back track between rooms when they realized they had forgotten an item ( or not gone to a location ) in a room they had already visited.

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53 This effect was further illustrate d when looking at performance on number of correctly identified locations and correctly identified items. Scoring for HNT was based on the well documented relationship between aging and decline in memory functioning including spatial memory (e.g., Park, et al., 2007) and verbal memory (e.g. Salthouse, 1998), a s well as evidence that spatial and object memory are two different systems (e.g. Courtney, Ungerleider, Keil, & Haxby, 1996) It was expected that older adults would not be able to remember the object s as well as younger adults, but memory for location in a real world setting was of interest. While there was a significant age group difference in the number of correctly identified locations, old adults on average correctly remembered almost all (14 15 o ut of 16) of the locations (old: M = 15.31, SD = 1.03; old old: M = 14.14, SD = 1.51). However, they did not efficiently navigate to these locations resulting in longer path lengths. Contributions to this inefficiency were explored even further using a med iation model approach. The significant age group differences found in path length, correctly identified locations, and correctly identified items even on Trial 1 further suggest that age differences in navigational ability are not restricted to learning a nd memory. These gaps narrow over trial, but significant group differences still exist. These age differences in our study then could not simply be due to differences in general spatial ability, but can be explained by other factors as well. The data also demonstrated group differences in the house object recognition task (HORT), an independent paper and pencil recognition task administered after the completion of the delay trials. While significant group differences were found, closer investigation reveal s that these differences were fairly small with no participant (young

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54 or old) correctly recognizing or differentiating all 48 items (Total HORT score young: M = 35.22, SD = 2.76; old: M = 34.00, SD = 2.42; old old: M = 32.64, SD = 2.84). The finding of sig nificant group effects on correct identification of incidental items was opposite to what was predicted. Based on the relationship between age and distraction (or inability to suppress irrelevant information) (e.g. Andres, Parmentier, & Escera, 2006; Chao & Knight, 1995), it was predicted that the older adults would be more accurate in the identification of items placed in the house that were not needed for the experiment (i.e. ey would attend to incidental items as much as they would to critical items. This did not prove to be the case and in fact the opposite was found. Younger adults performed best on accurate identification of incidental items, followed by old adults and then the old old adults (Total HRMT score young: M = 47.57, SD = .90; old: M = 43.38, SD = 7.08; old old: M = 34.79, SD = 10.18). Although the HORT measures recognition of objects that were located in the house, it does not measure reconstruction of the spatia l relationships among the objects, and likely taps different memory representations than those used for successful spatial navigation. Analysis of the house reconstitution map task (HRMT), which required participants to place the 16 items used in the exper iment on a floor plan of the house revealed significant group differences. Younger adults placed the items in the correct room and in correct relation to the other items more often than old adults. When looking at accuracy (deviation measurement), young ad ults again performed significantly better. This taken together with the mean number of items correctly identified on the HORT suggests that while older adults are able to recognize objects in their environments, the y are unable to

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55 reconstruct a cognitive map of the relationship of these items in the environment. On the HRMT, older adults were frequently observed to place items on the misarranged pieces of furniture that were not located in the correct room or in correct relation to other items in the same vicinity. It could be the case that older adults were either encoding the information using a verbal or visual object memory strategy rather than encoding the large discrepancy in deviation scores between groups and suggests older adults were not effectively encoding the layout of the environment. Younger adults appear better able to accurately remember the precise spatial layout of an environment than older adults. We then sought to examine other cognitive factors mediating the relationship between age and spatial navigation. Specifically, based on previous research, we predicted that the expected age related differences in real world navigation would be accounted f or by mental rotation ability and executive functioning (cognitive flexibility). We were able to demonstrate the causal relationship between aging and poorer real world navigation (HNT) performance was partially mediated by executive functioning. This find ing suggests that executive functioning may be the driving critical cognitive contributor to successful navigation, mostly independent of age. This result fits well with the correlation of normal aging and accompanying age related effects on neuropsycholog ical tests of executive functioning (e.g. Salthouse, Atkinson, and Berish, 2003). Indeed, approximately 35% of the variance in the direct effect of age on HNT remains unexplained. It is plausible that this relationship may be partially mediated by

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56 other co gnitive factors, or factors not measured in this study. Mental rotation was not found to mediate the significant causal relationship between age and HNT. Consistent with previous findings, results from this study clearly demonstrated that overall, older a dults do not navigate as effectively as younger adults in virtual space (CG Arena). Performance on all visible practice trials was comparable, all participants reported they understood the instructions, and all participants were trained until they mastered the use of the joystick. Furthermore, young adults did not report significantly more experience with use of a joystick than older adults. Young adults did report significantly more computer game experience than older adults. However, in the analyses, thi s experience did not have an impact on performance. It can therefore be concluded that the age differences found were not a function of lack of experience with the computer or joystick. While time was not included in measuring age group differences, known age effects of psychomotor processing speed were still considered. Despite the joystick control being relatively simple (gross motor skill forward, left, right), changes in motor skill that accompany aging, could make it harder for older adults to manipul ate the joystick, thereby impacting performance. Therefore, the age group differences in psychomotor skill and speed were examined and in this analyses did not have an impact on performance. In addition, no age related differences were found on the practic e trials of the experiment. We can therefore conclude that this age difference found in CG Arena performance cannot be accounted for by generalized psychomotor slowing associated with age. We also examined CG Arena variables independently and our findings confirmed p revious work done in our laboratory. This data demonstrated that young adults found

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57 the target more often and with shorter traveling distance than did older adults. Additionally, on the probe trial, young adults spent a greater proportion of tim e searching the target quadrant than older adults. Consistent with previous studies, this pattern of results indicated that young adults learned, remembered, and navigated to the invisible target more effect than older adults (King 2006, Laurence et al., 2002). Age group differences were also demonstrated on the arena reconstitution task (ART), but not the object recognition test (ORT). Both are paper and pencil tests administered after completion of the CG Arena. T he older group placed the target in the correct quadrant on the ART significantly less often than the younger group, further supporting the idea that they did not have an accurate memory representation for the location of the invisible target. These data suggest that older adults are not able to reconstruct virtual cognitive maps of their environments as well as younger adults or remember a particular place in that environment as accurately as younger adults. However, they are able to recognize objects that were in the virtual environment as well as younger adults (ORT) A third important outcome of the present study emerged from examining the relationship of performance on CG Arena with that of HNT. The focus of the present study was to evaluate how virtual navigation predicts real world navigati on, especially in older adults. CG Arena turned out to have less impact on HNT performance than age and executive functioning. While CG Arena has a significant positive correlation with HNT, when age and executive functioning are entered into the model, th is significant correlation no longer exists. In addition, there is a large significant negative correlation between age and executive functioning. This is all good evidence to suggest that tasks

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58 with executive functioning demands are more powerful than com puter navigation performance and age in predicting real world navigation. Therefore, while significant correlations exist between navigation in computer space and real space, this present study expands on this finding even further by evaluating other facto rs contributing to this relationship. One explanation for the lack of impact from computer navigation is the cognitive flexibility demands of HNT, as mentioned above. In addition to the spatial learning components of the HNT, there exists a large working m emory and planning component. One must visualize the location of the items, and then decide the order of item location so that the most efficient path is taken. If this is not done successfully, then it leads to inefficiency (i.e. travelling back and forth between rooms). The high execution demands of this real world navigation task could be contributing to the decrease in shared variance with the CG Arena and both measures most likely rely on executive functions systems. As reviewed earlier, it has been de monstrated that vMWM performance is influenced by frontal systems and that cognitive measures that are thought to tap these systems are correlated with vMWM performance (Moffat, et al., 2007). The limitations of the current study will be addressed in furth er studies First, one recognized limitation of the design is that, home and having it be so structured, it does not completely generalize to the individual urgess, Alderman, Forbes, Co stello, Coates, Dawson, et al. methodological gains that are believed to make this a valuable measure. These include the ability to control the stimuli in the environment (e.g. fa miliarity and range of

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59 exploration supermarket), standardization of the test demands, and safety and privacy of the participants (Titov & Knight, 2005). Despite the benefits of methodolog ical control, future ed environment, may provide a better gauge of difficulties experienced in everyday life. Second, executive functioning has been conceptualized to be compris ed of several different abilities (components) and is frequently tested using a variety of tests. For this study, we tested only two measures that fall into the working memory and switching subcomponents. Therefore, the two measures used in this study may not be representative of all aspects of the executive functioning construct. Future studies focusing on more subcomponents of executive functioning, and with more tests may provide additional information to the relative contribution of executive functionin g abilities to real world spatial navigation. In keeping, mental rotation ability was only measured by one test in this project, and may therefore not be an accurate indicator of the relative contribution to real world navigation performance. B ecause this study focused on two healthy groups of participants, it was not focused on demonstrating links between behavioral performance and the underlying neural substrate for spatial cognition. Future studies focused on clinical populations may include anatomic mea sures of MTMS pathology using structural MRI; demonstrating experimental utility. In addition, inclusion of individuals with dementia could assist in examining the relatio nship of these neurobiological measures to performance.

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60 W hile our current sample is nearly matched on IQ and education, the study was conducted on a sample of healthy, predominately Caucasian adults, with high levels of education. Therefore, caution must b e exercised when generalizing to other populations. Future direction would aim to recruit a more diverse sample that is more representative of the population. Due to ceiling effects in the young group, accompanying issues with restricted range of scores, and small sample sizes overall, relationships between all cognitive abilities and HNT performance could not be fully assessed. In future studies, it would be useful to include either a large sample of either healthy older or young adults in this type of st udy. If normal healthy younger adults were examined, then test demands should be increased to reduce ceiling effects. Contributions of other cognitive factors could then be further assessed. HNT and CG Arena performance should then be included in a factor analytic study of cognitive ability that includes neuropsychological measures from many domains. Present findings have shed some light on changes in real world spatial navigation that occur with aging and how these abilities may actually be mediated by ot her factors. Our findings show that real world navigation as measured by HNT is strongly influenced by executive functioning (working memory and switching). The influence of aging on executive functioning should be taken in consideration in future studies of spatial cognition. The use of real world environments in future research with normal aging individuals as well as young individuals with frontal lobe insult may help further our understanding of spatial ability age differences and brain mechanisms invol ving spatial navigation with greater clarity. We believe that a systematic analysis of the causal link

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61 between executive functioning and real world navigation ability is an appropriate next step in this line of research. In conclusion, the present report provides additional evidence that older adults demonstrate poorer performance on virtual and real world tasks of spatial learning and memory than do their younger counterparts. These data confirm the feasibility of the HNT task in an older population and s peak to the possible utility of using more ecologically valid measures in a clinical setting. Given the importance of environmental demands when assessing cognitive functioning, both laboratory and real world tasks need to be utilized for accurate assessme nt.

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62 APPENDIX A COGNITIVE TEST BATTE RY Overall Construct Measure Description of Test Measure Source General Mental Status Mini Mental State Exam (MMSE) Estimation of dementia severity Folstein et al., (1975) Observer/ Interview Ratings Clinical De mentia Rating Scale (CDR) Disease severity rating: informant & patient subjective, objective elements Morris, (1993) Functional Assessment Memory Assessment Centers Questionnaire (MAC Q) Subjective memory complaints Crook and Larrabee (1992) Intellectual Functioning Wechsler Abbreviated Scales of Intelligence (WASI 2 subtest) Vocabulary, Matrix Reasoning Predicts general intelligence, verbal and performance abilities Wechsler, (1999) Wechsler Test of Adult Reading (WTAR) Estimation of p remorbid levels of intelligence Wechsler (2001) Overall Functioning/ Screening Battery Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) Brief core battery for detection of brain insult dementia, head injury, or stroke. Ra ndolph (1998) Language Functioning Boston Naming Test 2 nd Edition (BNT) Confrontation naming using large ink drawings Goodglass & Kaplan, (2001) Controlled Oral Word Association (COWA) Verbal fluency to alphabet letter (FAS). Spreen & Benton, (1977 )

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63 Overall Construct Measure Description of Test Measure Source Frontal/Executive Skills Attention, Processing Speed, Problem Solving, Abstract thinking WAIS III Digit Span Attention span Wechsler, (1997) D KEFS Trail Making Test Condition Visuomot or speed, visual scanning, sequencing, cognitive flexibility Delis, Kaplan, & Kramer, (2001) Motor Finger Oscillation Test motor speed Reitan (1969) Grooved Pegboard test speeded fine motor dexterity Klove (1963); Reitan (1969) Spatial Skill s/Cognition Wechsler Memory Scale III Spatial Span visuospatial short term and working memory Wechsler, (1997) Mental Rotation Test Mental Rotation Vandenberg & Kuse (1978) Space Thinking Flags Thurstone & Jeffrey (1959 ) Neuropsycho logical Assessment Battery Spatial Module: Map Test (NAB) Local Navigation Strategy/Map Reading Stern and White, (2003) Mood Geriatric Depression Scale (GDS) Self evaluation assessing elements of depression Yesavage, Brink, Rose, Lum, Huang, Adey, and Leirer, (1983) Beck Depression Inventory 2 nd Edition (BDI II) Self evaluation assessing elements of depression Beck, Steer & Brown, (1996)

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64 APPENDIX B HOUSE NAVIGATION TAS K ITEM LIST Packing for a trip out of town and need to locate the following items items that were located in the same room or close to one another. Six of the proposed items were based on the MIR task (not used glass, scissor, matchbox, comb) 1. pencil 2. keys 3. ring 4. watch 5. pill bottle 6. sunglasses 7. toothbrus h 8. crackers 9. wallet 10. sunscreen 11. socks 12. water bottle 13. camera 14. peppermint 15. granola bar 16. gauze pads

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72 BIOGRAP HICAL SKETCH ree in health science. She then spent a year working as a psychometrist at Mayo Clinic in Jacksonville, Fl orida and three years as a research project coordinator for vari ous studies with investigators from Ha rvard Medical School in Boston, Massachusetts. She earned a master s degree in clinical and health psychology at the University of Florida in 2006 and then began her doctoral studies in the same program with a concent ration in clinical neuropsychology Ms. King concluded her doctoral training with an internship at the James A. Haley Veterans Medical Center in Tampa, Florida.