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Behavioral and Brain Functional Correlates of the Proceduralization of Evaluation

Permanent Link: http://ufdc.ufl.edu/UFE0022702/00001

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Title: Behavioral and Brain Functional Correlates of the Proceduralization of Evaluation
Physical Description: 1 online resource (124 p.)
Language: english
Creator: Li, Hong
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: automatic, controlled, declarative, evaluation, fmri, learning, procedural
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: Our study concerned the effects of practice with evaluative judgments at both the behavioral and the brain functional levels. We hypothesized that evaluative practice increases the likelihood and efficiency of evaluative judgments. As predicted, Experiment 1 showed that participants who practiced evaluating visual stimuli became faster with time and were more likely to make evaluative judgments spontaneously at a later time. To explore the brain functional correlates of evaluation proceduralization, in Experiment 2 we used functional magnetic resonance imaging (fMRI) to compare the activity before and after participants practiced performing evaluative judgments of pictures. We found that practice in evaluation had effects on brain activation in various regions. Specifically, when evaluative judgments were proceduralized, brain activation increased at regions associated with automatic evaluative processing, including the amygdala, the insula, and the orbito-frontal cortex, and regions associated with controlled evaluative processing (e.g., the temporal pole, the anterior cingulate cortex, the frontal operculum), as well as late visual regions (e.g., the posterior fusiform, the superior occipital lobe, and the parietal occipital lobe). Moreover, evaluation proceduralization was reflected by increased activity in areas associated with procedural learning (e.g., the striatal regions, the lateral cerebellum, the precuneus, and the inferior frontal cortices), and decreased activity in areas associated with declarative learning (e.g., the medial temporal lobe, the ventromedial prefrontal cortex, the dorsolateral prefrontal cortex).
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 Hong Li.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Albarracin, Dolores.
Local: Co-adviser: Cottrell, Catherine.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022702:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022702/00001

Material Information

Title: Behavioral and Brain Functional Correlates of the Proceduralization of Evaluation
Physical Description: 1 online resource (124 p.)
Language: english
Creator: Li, Hong
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: automatic, controlled, declarative, evaluation, fmri, learning, procedural
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: Our study concerned the effects of practice with evaluative judgments at both the behavioral and the brain functional levels. We hypothesized that evaluative practice increases the likelihood and efficiency of evaluative judgments. As predicted, Experiment 1 showed that participants who practiced evaluating visual stimuli became faster with time and were more likely to make evaluative judgments spontaneously at a later time. To explore the brain functional correlates of evaluation proceduralization, in Experiment 2 we used functional magnetic resonance imaging (fMRI) to compare the activity before and after participants practiced performing evaluative judgments of pictures. We found that practice in evaluation had effects on brain activation in various regions. Specifically, when evaluative judgments were proceduralized, brain activation increased at regions associated with automatic evaluative processing, including the amygdala, the insula, and the orbito-frontal cortex, and regions associated with controlled evaluative processing (e.g., the temporal pole, the anterior cingulate cortex, the frontal operculum), as well as late visual regions (e.g., the posterior fusiform, the superior occipital lobe, and the parietal occipital lobe). Moreover, evaluation proceduralization was reflected by increased activity in areas associated with procedural learning (e.g., the striatal regions, the lateral cerebellum, the precuneus, and the inferior frontal cortices), and decreased activity in areas associated with declarative learning (e.g., the medial temporal lobe, the ventromedial prefrontal cortex, the dorsolateral prefrontal cortex).
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 Hong Li.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Albarracin, Dolores.
Local: Co-adviser: Cottrell, Catherine.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022702:00001


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BEHAVIORAL AND BRAIN FUNC TIONAL CORRELATES OF THE PROCEDURALIZATION OF EVALUATION By HONG LI A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008 1

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2008 Hong Li 2

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To my parents Yingchao Li a nd Jian Yang, without whose sacr ifices and unconditional love none of this would be possible 3

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ACKNOWLEDGMENTS My deepest gratitude is to Dr. Dolores Albarra cn, who not only served as my supervisor but also encouraged and guided me throughout my academic program. I thank her for holding me to a high research standard and helping me to focus on my ideas and overcome setbacks, thus teaching me how to achieve and become an objec tive scientist. The invaluable graduate school experience has been the one I will cherish forever. I express sincere appreciation to Dr. Yijun Liu for his guidance and insight throughout the rese arch. I also thank Dr. Catherine Cottrell for consenting to join my committee at the final stag es of my project; her suggestions were helpful and appreciated. The insightful comments and constr uctive criticisms from Drs. Ira Fischler and Dean Sabatinelli on various stages of the cu rrent research are gratefully acknowledged. In addition to my committee members, a great many people have been especially helpful in my research. Without their help, none of this would be possible. I am thankful to Dr. Rick Brown for his invaluable work on the early stage of this research project. I also thank the lab members in the Attitudes and Persuasion Lab at the Psychology Department at the University of Florida for discussions of the ideas reported in this dissert ation. I am also grateful to the members in the fMRI Lab at the McKnight Brain Institute with whom I have actively interacted during the course of this research. Particul arly, I would like to thank Dr. Pa ul Wright for his encouragement and numerous advices on how to understand and use neuroimaging data. I thank Dr. Guojun He, Dr. Zhenyu Zhou, Nelson Klahr, Tianyu Tang for th eir technical support w ith this research. I express thanks and appreciation to my husband Xiaoguang Zhen for his unconditional love, patience, and support during my graduate sc hool years. I thank my parents, Yingchao Li and Jian Yang, for their faith in me and givi ng me freedom to explore my dream to be a psychologist. 4

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TABLE OF CONTENTS page ACKNOWLEDGMENTS ............................................................................................................... 4LIST OF TABLES ...........................................................................................................................7LIST OF FIGURES .........................................................................................................................8ABSTRACT ...................................................................................................................... ...............9CHAPTER 1 INTRODUCTION ................................................................................................................ ..11Proceduralization of Evaluation .............................................................................................12Possible Brain Functional Correlates of Proceduralized Evaluations ....................................16Brain Functional Correlates of Proceduralization ...........................................................16Brain Functional Correlates of Evaluation ......................................................................22Hypothetical Brain Functional Changes in Evaluation Proceduralization .............................25The Present Research .......................................................................................................... ....282 EXPERIMENT 1 ................................................................................................................ ....31Method ........................................................................................................................ ............31Overview ...................................................................................................................... ...31Participants and Design ...................................................................................................31Procedures .................................................................................................................... ...32Materials and Measures ...................................................................................................32Results .....................................................................................................................................34Response Time ................................................................................................................34Responses to Tasks ..........................................................................................................36Spontaneous Thought Listing ..........................................................................................36Discussion .................................................................................................................... ...........373 EXPERIMENT 2 ................................................................................................................ ....45Method ........................................................................................................................ ............45Overview ...................................................................................................................... ...45Participants .................................................................................................................. ....46Procedures .................................................................................................................... ...46Functional Imaging Data Acquisition .............................................................................47 Materials and Measures ...................................................................................................48Results .....................................................................................................................................52 Behavioral Findings ........................................................................................................... .....52Response Time ................................................................................................................52Responses to Tasks ..........................................................................................................54 5

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6 Functional Imaging Data Analyses .........................................................................................56Tests of Evaluation Proceduralization .............................................................................57Findings in Regions Associ ated with Learning ...............................................................58Findings in Regions Associ ated with Evaluation ............................................................69Findings in Visual Areas .................................................................................................74Findings in Motor Areas ..................................................................................................76Correlations between Behavioral Perform ance and Brain Activities in Regions Associated with Learning and Evaluation ......................................................................78Discussion .................................................................................................................... ...........814 GENERAL DISCUSSION ...................................................................................................107Summary of Findings ...........................................................................................................107Contributions ................................................................................................................. .......108APPENDIX A INSTRUCTIONS AND CUES FOR EVALUATIVE AND NON-EVLALUTIVE TASKS (EXPERIMENT 2) ..................................................................................................112B CORRELATION COEFFICI ENTS OF ROI ACTIVITY CHANGES AND BEHAVIORAL PERFORMANCE CHANGE FROM THE PRETO THE POSTTRAINING RUN (EXPERIMENT 2) ..................................................................................113LIST OF REFERENCES .............................................................................................................116BIOGRAPHICAL SKETCH .......................................................................................................124

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LIST OF TABLES Table page 2-1 IAPS picture codes for trai ning tasks (Experiment 1) .......................................................392-2 IAPS picture codes for the thought listing task (Experiment 1) ........................................412-3 Mean response times for evaluative and non-evaluative tasks to new and repeated stimuli (Exper iment 1) .......................................................................................................4 22-4 Ratings of pleasant and unpleasant images in each practice block (Experiment 1) ..........433-1 IAPS picture codes, valence and arousal ratings for images presented in the pretraining run (Experiment 2) ...............................................................................................863-2 IAPS picture codes, description, valence, and arousal ratings of images presented in the training run (Experiment 2) ..........................................................................................883-3 IAPS picture codes, description, valence, and arousal ratings of images presented in the post-training run (Experiment 2) ..................................................................................923-4 Ratings of pleasant and unpleasant images in the pre-training and the post-training run (Experiment 2) .............................................................................................................943-5 Distribution of responses for evaluative and non-evaluative tasks in pre-training and post-training runs (Experiment 2) ......................................................................................953-6 List of references of regions-of interest selected in the fMRI study of evaluation proceduralization (Experiment 2) ......................................................................................963-7 Means of BOLD signals in ROIs previously linked to proc edural learning for evaluative and non-evaluative tasks in the pre-training and post-training runs (Experiment 2) ................................................................................................................ ...973-8 Means of BOLD signals in ROIs previ ously linked to declarative learning for evaluative and non-evaluative tasks in the pre-training and post-training runs (Experiment 2) ................................................................................................................ ...983-9 Means of BOLD signals in ROIs previ ously linked to evaluative processing for evaluative and non-evaluative tasks in the pre-training and post-training runs (Experiment 2) ................................................................................................................ ...99 3-10 Means of BOLD signals in ROIs previously linked to motor function and visual processing for evaluative and non-evaluative tasks in the pre-training and posttraining runs (Experiment 2) ............................................................................................1003-11 Correlation coefficients of BOLD signal changes (prevs. posttraining run) during evaluative tasks in regional clusters (Experiment 2) .......................................................101 7

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LIST OF FIGURES Figure page 1-1 Brain regions associated with evaluation proceduraliza tion and predicted effects. ..........302-1 Effects of task and practice bl ock on response time (Experiment 1) .................................443-1 Sample IAPS pictures used in Experiment 2. ..................................................................1023-2 Practice-related activa tion changes for evaluative tasks (Experiment 2). .......................1033-3 Maps for brain activity changes for ev aluative tasks in lear ning-related regions (Experiment 2). ............................................................................................................... .1043-4 Maps for brain activity changes for eval uative tasks in evaluation-related regions (Experiment 2). ............................................................................................................... .1053-5 Maps for brain activity changes for evalua tive tasks in regions related to visual and motor processing (Experiment 2) .....................................................................................106 8

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9 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy BEHAVIORAL AND BRAIN FUNC TIONAL CORRELATES OF THE PROCEDURALIZATION OF EVALUATION By Hong Li December 2008 Chair: Dolores Albarracin Cochair: Catherine Cottrell Major: Psychology Our study concerned the effect s of practice with evalua tive judgments at both the behavioral and the brain functiona l levels. We hypothesized that evaluative practice increases the likelihood and efficiency of evaluative judgments. As predicted, Experiment 1 showed that participants who practiced eval uating visual stimuli became fa ster with time and were more likely to make evaluative judgments spontaneous ly at a later time. To explore the brain functional correlates of evaluation proceduraliz ation, in Experiment 2 we used functional magnetic resonance imaging (fMRI) to compare the activity before and after participants practiced performing evaluative judgments of pictures. We found that practice in evaluation had effects on brain activation in va rious regions. Specifically, wh en evaluative judgments were proceduralized, brain activation increased at regions associated with automatic evaluative processing, including the amygdala, the insula, and the orbito-frontal cortex, and regions associated with controlled evaluative processi ng (e.g., the temporal pole the anterior cingulate cortex, the frontal operculum), as well as late visual regions (e.g., the posterior fusiform, the superior occipital lobe, and the parietal occipital lobe). Moreov er, evaluation proceduralization was reflected by increased activity in areas associat ed with procedural learning (e.g., the striatal

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regions, the lateral cerebellum, th e precuneus, and the inferior fr ontal cortices), and decreased activity in areas associated w ith declarative learning (e.g., th e medial temporal lobe, the ventromedial prefrontal cortex, the dorsolateral prefrontal cortex). 10

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CHAPTER 1 INTRODUCTION Evaluation, which is believed to be a fundame ntal dimension of meaning in all languages (Osgood, Suci, & Tannenbaum, 1957), entails mome ntary responses of favor or disfavor engendered by an object (Breckler & Wiggins, 1989; Schimmack & Crites, 2005). The ability to evaluate ones environment is lear ned at an early age, and childrens abilities to distinguish good from bad are critical in personality devel opment (Rhine, Hill, & Wandruff, 1967). Moreover, explicitly evaluating external stimuli is an impor tant component of learning from experience and adapting to a changing environment (Greenberg & Safran, 1987). For example, when a new set of behaviors results in unsatisfa ctory outcomes, people often evalua te and adjust their behavior in an explicit way. Extensive research has shown that evaluations are retrieved and made automatically in the presence of objects. Murphy and Zajonc ( 1993) supported the affective primacy hypothesis (Zajonc, 1980), which indicates that affective reactions can be elicited with minimal stimulus input and virtually no cognitive processing. Less extremely, Bargh and colleagues (1992) suggested a cognitive mediation framework, and demonstrated that most evaluations are stored in memory and become active automatically with the mere presence of the object. That is, evaluation can proceed without the intervention of conscious acts of w ill or guidance of the process (Bargh & Ferguson, 2000). Although this important past research suggests that evaluations are retrieved and made automatically there is surprisingly little evidence on how evaluations become more efficient and faster. Our purpose was to advance our understanding of this problem. 11

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Proceduralization of Evaluation Proceduralization is the acquisition of genera lized implicit knowledge about a skill that permits executing the skill wit hout requiring represen tation of declarative information (i.e., description of relevant facts, methods, and procedures) in working memory (Anderson, 1982; Neves & Anderson, 1981). Proceduralization has been confirmed for a variety of non-social skills. For example, one of the most common para digms used to study motor-skill learning is the serial response time task (Robertson, 2007), durin g which participants ar e trained to select appropriate responses whenever a visual cue appear s. With practice, perceptual-motor skills can become proceduralized, indicated by a gradua l reduction in the amount of time required to execute a task. Judgments of whether words contain a certain target sound can also be proceduralized with practice (S mith, 1989). Moreover, practicing judgments using formal logic rules speeds up this type of decision-making process (Carlson, Sullivan, & Schneider, 1989). Overall, this evidence suggests that various motor and cognitive skills can proceduralize. Judgment proceduralization has also been c onfirmed for various social domains. For instance, Smith, Branscombe, and Bormann (1988) indicated that trait inferences can be strengthened by practice, and that the effects of practice need not be cont ent-specific. Smith and colleagues (1992) further demonstrated that repeat edly determining whether a behavior implies a trait speeds up this judgment in a later time, even when there is no conscious awareness of practice. Smith, Fazio and Cejka (1 996) extended this work to th e area of social categorization, supporting the idea that judgments of a pers on become more accessibl e after practice in determining whether a person belongs to a certai n social category. Also related to the speeding up effects of social judgment practice, Bassili (1993) found that practi ce with judgments of whether a behavior implies a trait also rendered mo re spontaneous trait inferences later, in the absence of an explicit request. 12

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Despite the value of previous work on effect s of practice on judgmen ts, no prior research has directly investigated whether evaluative judgments can be proceduralized during learning/practice. Before discussi ng the possibilities of evaluation proceduralization, we describe how procedural knowledge is produced, and how it influences our learning process. Based on Fitts (1964) three-stage model of learning (i.e., cognitive, associative and autonomous learning), Anderson (1982) proposed that skill acquisition co mprises declarative, knowledge compilation, and procedural stages In addition, the de fining features of proceduralization through practi ce are increased efficiency (i.e ., speeding up of the performance) and removal of deliberative e fforts at executing responses. Specifically, as procedures are initially le arned and enacted in a deliberative fashion (Kolers & Roediger, 1984; Wyer & Srull, 1989 ), they are often inferred from declarative knowledge with frequent errors and verbal me diation (Anderson, 1982). For example, when one learns a certain task for the first time (e.g., ridi ng a bicycle), one must mentally rehearse the order of the movements to perform the tas k. Over time, however, reliance on declarative knowledge decreases and reliance on procedural knowledge increases, allowing the procedural knowledge to be applied to identical as well as different targets (Anderson, 1982; Smith, 1989, 1994; Smith & Lerner, 1986). That is, during the asso ciative stage, the single steps involved in a task are converted into a collec tion of inter-related procedures, and this collection of knowledge can be activated to guide beha vior automatically whenever necessary conditions are met. Proceduralization, the process of establishing direct condition-ac tion associations, is used to explain performance improvements (e.g., speedup in execution) during pract ice of a procedural skill1. 1 These features therefore distinguish proceduralization from habituation, which is an extremely simple form of nonassociative learning. Habituation is often characterized by a progressive diminution of behavioral or attentional 13

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Thus, although initially an evaluative pro cedure may be cognitively demanding, practice reduces the load of working memory by integratin g declarative descriptio ns of the procedure, thus making the execution of the procedure effortle ss. Learning to evaluate stimuli (e.g., good vs. bad) may be similar to the acquisition of othe r skills. Extant evidence supporting the notion of evaluation proceduralization comes largely from work on automatic evaluations. For example, using evaluative priming paradigms, Fazio a nd colleagues (1986) demonstrated that the automatic activation of evaluations is obtained primarily for attitude objects toward which people have highly accessible attitudes. Similarly, Devi ne (1989) has shown th at the activation of automatic evaluations (e.g., prejudice) occurs desp ite a persons willingness to control or ignore them. In addition, a study by Castelli and co lleagues (2004) supported that, once a person is categorized as a member of a given group, evaluations of the category are activated automatically. Thus, the greater the practice with certain evalua tions, the more automatic or spontaneous these evaluations become. Briefly, past research has estab lished that a specific positive or negative evaluation is often automatic. However, the empirical question addresse d in this research is whether the process of evaluation proceduralizes with practice. Specifi cally, although the activation of evaluative or affective responses to external stimuli may be automatic, making expl icit evaluations (e.g., ratings on Likert scales) is a voluntary act. For example, the activation of negative evaluative responses when seeing a snake may be automatic, but quantifying the extent to which this is unpleasant may be deliberate. In addition, previous research indicated that there are individual differences in chronic evaluati ve tendencies (Jarvis & Petty, 1996). For example, people who are responses with repetition of the same stimulus. For instance, after a period of exposure to continuously presented stimuli (e.g., strong odors, bright light, and noise), our sensory systems (e.g., the nose, the eyes, and the ears) stop responding. 14

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high in need to evaluate tend to make extreme fast evaluations, whereas people who are low in need to evaluate often make moderate or sl ow evaluations. Therefore, making evaluative judgments is more automatic for some pe ople, but more deliberate for others. A range of research has suppor ted the idea that e xplicit evaluative judgments are not as efficient as the evaluations measured with implic it measures, such as the implicit association test (IAT; Greenwald, McGhee, & Schwartz, 1998). Hence, studies of explicit evaluative judgments may offer ideal opportunities to observe evaluation procedural ization. People who practice applying an evaluative rating scale may speed up their judgments as time goes by. In fact, practice may speed up evaluative ju dgments to both repeated and nove l stimuli. That is, response times to repeated targets may decrease faster than those to novel targets, because earlier judgments of a target increase the accessibility of the earlier evaluation when a judgment is conducted again (Smith, 1989). In addition, evaluation proceduralization may influence the way in which people judge new targets by, for exam ple, increasing the chances of making other explicit evaluative judg ments (Fergusson, Bargh, & Nayak, 2005; see also, Bassili, 1993). For instance, applying a scale to evaluate images may lead to more evaluations of new images if one is later asked to write co mments about images without any further instruction. Another important question underlying this rese arch concerns which aspects of explicit evaluative judgments change during proceduralization. In particular, we want to investigate what specific processes contribute to the behavioral outcome (i.e., faster response time and better performance) usually present in procedurali zation studies. The evaluative-skill learning itself requires several stages of cognitive and evaluative operations, such as interpreting the situation depicted in stimuli, evaluating visceral respons es triggered by the target stimuli, comparing the experience of the presented stimulus with othe r stimuli, recalling appropriate anchors from the 15

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scale, and behaviorally assigning a value to th e stimuli. Thus, practic ing evaluative judgments may proceduralize the skills involved in expressing judgments and the evaluations themselves. These aspects among other processes were addre ssed by investigating cha nges in brain activity during evaluation proceduralization. Possible Brain Functional Correlates of Proceduralized Evaluations To detect which aspects of evaluation procedur alize with practice, we examined the effects of evaluation practice on brain regions associated with (a) procedural learning, (b) declarative learning, (c) automatic evaluation, and controlled evaluation, and (d) early and late visual processing, and primary motor processing.. Brain Functional Correlates of Proceduralization We predicted that practice with explicit ev aluations could trigger changes in several separate sets of regions. Specifi cally, before practice, evaluativ e skills are in the form of declarative learning. Therefore, we should observe activation in brain re gions associated with declarative learning at the early stage of prac tice. In contrast, when evaluative skills are transformed to procedural knowledge, activations in regions associated declarative learning should decrease while activations in regions associated with procedural learning should increase. 1. Brain regions associated with procedural learning. Based on prior research, several regions are involved in the proce ss of procedural learning. First, basal ganglia are the largest subcortical structures in the human forebrain, and the system influences multiple aspects of motor, affective, and cognitive behavior (Grayb iel, 2000). The basal ganglia system operates in close relation with the cerebella co rtex, and is part of the extens ive basal ganglia-thalamocortical circuits modulating the activity of the frontal co rtex. According to previo us research, the basal ganglia function as a system that helps the cortex to group learning into ha bits and routines in a way that facilitates accessing stored information (Graybiel, 2005). Important for our analysis, the 16

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basal ganglia are involved in the regulation of non-motor as well as motor sequence learning (Vakil et al., 2000). The basal ganglia consist of a set of interc onnected subcortical nuclei. The major input nucleus is the striatum, which consists of the caudate nucleus the putamen and the nucleus accumbens. The striatum is located close to the out put station of the basal ganglia the globus pallidus, which projects to most cortical areas of the frontal lobe (Alexander et al., 1990). During learning, the striatum interacts with the sensorimotor and frontal cort ices by recognizing known behavioral contexts and modula ting cortical activity to prod uce a response (Seger, 2006). Importantly, the striatum plays an essential role in non-declarative/procedural cognitive-skill learning (Poldrack et al., 1999; Seger & Cincotta, 2006) a nd implicit sequence learning (Peigneux et al., 2000). For example, the caudate nucleus has been shown to be more active during the procedural learning phase of a pr obabilistic classificati on task than during a perceptual-motor control task (P oldrack et al., 1999). Moreover, using a complex serial reaction time task, Peigneux et al. (2000) found that the striat um plays a critical role in implicit sequence learning. Specifically, the striat um (e.g., the caudate nucleus, the putamen) was significantly more active when stimuli were predictable and thus facilitated faster responses than when they were not. In addition, Destrebecquz and colleagues (2005) investigat ed the cerebral correlates of explicit and implicit knowledge in a serial reacti on time task. They showed that activity in the striatum is associated with the implicit compone nt of performing a learned task, whereas activity in the anterior cingulate and medi al prefrontal cortex are associat ed with the explicit component. Other research suggests that different parts of the striatum have different functions. For example, during motor sequence learning, the pu tamen is involved in the execution of welllearned movements by projecting to premotor and supplementary motor areas of the cortex. In 17

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contrast, the caudate nucleus is one of the ma in loci for the reward-processing aspect of behavioral learning (Doya, 2000; Haruno et al., 2004; Pack ard & Knowlton, 2002) and is involved while an individual is receiving feedback. Furthermore, Lehericy and colleagues (2005) suggested that distinct basal ga nglia regions are involved in early and advanced stages of motor sequence learning. Specifically, during motor sequence learning, the rostordor sal areas of the putamen decrease in activity with practice, whereas the activity of the caudoventral areas of the putamen increases. Furthermore, the nucleus ac cumbens plays a role in regulating emotions (Phan et al., 2004), and in processing reward s (Schoenbaum & Setlow, 2003) and personal relevance information (Lieberman et al., 2004; Ph an et al., 2004). For instance, Lieberman and colleagues (2004) found that the nucleus accumbens participat es in intuition-based selfknowledge judgments. That is, consistent with an implicit learning interpretation, the nucleus accumbens was more active when people judged th emselves on traits related to behaviors practiced frequently than when they judged th emselves on traits rela ted to behaviors they practiced infrequently (Lieberman et al., 2004). Similarly, other research found that activity in the nucleus accumbens increased with both increased emotional intensity and increased selfrelatedness of the stimulus targets (Phan et al ., 2004). Hence, the nucleus accumbens apparently participates in both intrinsic evaluative processi ng and self-relatedness processing. In sum, we expected to find increased involve ment of the striatal system in evaluations when they are proceduralized with practice. In addition to the basal ganglia, the cerebellum is also involved in a wide range of motor and cognitive tasks, including motor skill lear ning, mental imagery, sensory processing, planning, attention, and language (Doya, 2000; Doyon et al., 2003; Hikosaka, 2002; Torriero et al., 2004; Willingham et al., 2002). Despite these functional similarities, empirical evidence 18

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(Doya, 1999, 2000; Exner, Koschack, & Irle, 2002) suggested that the basal ganglia are associated with reward-based and reinforcemen t learning, whereas the cerebellum is associated with error-based and supervised learning. Specifi cally, the cerebellum plays a role in learning specific task associations and in monitoring the internal models of the body and the environment. In contrast, the basal ganglia are more responsib le for evaluating the general requirements of a task and selecting appropriate responses by predicting reward a nd feedback. Furthermore, prior research suggested that the cerebellum interact s with the basal ganglia. The output stage of cerebellar processing (i.e., the dentate nucleus) has a direct in fluence over the input stage of the basal ganglia processing (i.e., the striatum) (Hoshi et al., 2005). Thus, the cerebellum may contribute to procedural learning (Torriero et al., 2004) by adaptiv ely adjusting the basal ganglia activity on the basis of internal info rmation and error signals (Doya, 2000). Areas within the parietal lobe have also been proposed to play these active roles. Extensive neuroimaging and neuropsychology evid ence suggests that the posterior parietal lobe plays a vital role in working memory retrieval (Berryhi ll & Olson, 2008). There ar e two separate regions within the posterior parietal lobe, namely the s uperior parietal lobe (B A 7 and precuneus) and the inferior parietal lobe (BA 39 and 40). Alt hough various studies have been conducted to distinguish the functions of th ese two regions in learning an d memory, the findings are not congruent. According to some studies, the inferior parietal cortex is more active during implicitskill learning (Mallol et al., 2007; Willingham et al., 2002), whereas th e superior parietal lobe is more active during declarative-skill learning (Willingham et al., 2002). However, other studies have suggested that the superior parietal lobe is more involved in procedural learning, whereas the inferior parietal lobe is more active in declarative learning. For example, Nadel and colleagues (2007) detected increased activity at the superior pari etal lobe (BA7 and precuneus) 19

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with repeated memory retrievals. In additi on, Poldrack and his colleagues (1999) found that, although the bilateral inferior pa rietal lobe (BA 40) was more active during procedural learning (vs. control tasks), its activation increased when the task relied on declarative memory. Another piece of evidence suggests that the inferior parietal lobe may be sensitive to the level of task difficulty, being more active for difficult (vs. ea sy) tasks (Klingberg et al., 1997). Therefore, the activation at the inferior pariet al lobe might decrease when task difficulty decreases with practice. Nonetheless, the curren t study would provide further ev idence of the specific roles of the superior and inferior parietal lobe in di fferent learning stages. Besides the above regions that were proposed to be relate d to learning, we were also interested in the effects of eval uative proceduralization at the infe rior part of the frontal lobe. Previous research suggests that the inferior frontal gyri are commonly recrui ted across various tasks, including learning (Seger & Cincotta, 2006; Willingham et al., 2002). For instance, the inferior frontal gyrus exhibited greater activity during implicit (v s. random) learning condition in the serial response time task (Willingham et al., 2002) and during rule learning compared to the rule application process (Seger & Cincotta, 2006 ). Similarly, the inferi or frontal cortex was involved in the rehearsal stage but not the storage stage of th e phonological learning task (Baldo & Dronkers, 2006). Therefore, the inferior frontal cortex is related to pr ocedural learning. Thus, we expected an increase in the activation of the inferior frontal region when evaluations proceduralize. 2. Brain regions associated with declarative learning. In contrast to the striatum, the cerebellum, the parietal lobe, and the inferior frontal lobe, the medial temporal lobe system, which is composed of the hippocampus and the parahippocampal region, is believed to be the center of declarative learning (Gab rieli et al., 1997; Poldrack et al., 1999; Squire et al., 2004). 20

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For example, in an explicit rule-l earning task, the striatum is recruited while the medial temporal lobe is suppressed (Seger & Cincotta, 2006). Si milarly, Poldrack and his colleagues (2001) found that, when the task is proceduralized, the striat um shows increased activation whereas the medial temporal lobe shows decreased act ivation. Conversely, when the task in declarative, the striatum is less active whereas the medial temporal lobe is more active. Thus, similar patterns may be observed when practice renders individuals more efficient at eval uating stimuli. That is, as the process of making evaluative judgments of new s timuli is being proceduralized (i.e., faster evaluations), the activation in the hippocampus and related medi al temporal regions decrease. Hence, in the current study, a decrease in the activ ation of the medial tem poral lobe is expected when evaluations proceduralize. Moreover, skill development is typically conceptu alized as a decrease in need for explicit control over performance as time goes by. In neuroimaging studies, the prefrontal cortex has been regarded as the center of high-level information processi ng because of its relation to working memory, action planni ng, action inhibition, and decisi on making. For example, the dorsolateral prefrontal cortex (dlPFC) is active during a wide range of cognitive tasks, such as a number of problem-solving, probabilistic-learnin g, and reasoning tasks, as well as workingmemory tasks (Poldrack et al., 1999). For instan ce, the activation of dlPFC decreases in people who learn skills faster (Willingham et al., 2002). Along with the hippocampus and the posterior parietal cortex, the dlPFC also appears to be involved in controlled/demanding (vs. intuitive/automatic) trait judgments (Lieberman et al., 2004). Similarly, dlPFC activation seems to be directly associated with task difficult y, as judged by its higher activation during complex tasks than during simple tasks (Klingberg et al., 1997). In addition to the dlPFC region, the ventromedial prefrontal cortex (vmPFC) is involved in higher level decision making (Bechara et 21

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al., 1999), particularly by integrating all somatic state information trig gered by brain regions associated with evaluation (e.g., the amygdala and the anterior cingula te cortex) and then deciding on the appropriate action. Furthermore, successful activation of the vmPFC region when surrounding stimuli are potentially aversive seems to contribute to psychological wellbeing, presumably because of its influence on good decision-making (Reekum et al., 2007). Based on the findings in prior re search, we expected lower activ ation in both the dorsolateral prefrontal cortex and the ventro medial prefrontal cortex when evaluations are proceduralized than when they are not. Brain Functional Correlates of Evaluation We also expected that practice would influen ce the activation of regions associated with evaluative processing. Previous neuroscience res earch has demonstrated that some regions are involved in automatic evaluations and others are involved in exp licit evaluations. Therefore, we hypothesized that practice with evaluations would change brain activities in automatic evaluation regions regardless of whether explicit evaluations are required. However, practice with evaluations would change brain ac tivities in regions associated w ith controlled evaluation only when explicit evaluations are required. 1. Brain regions associated with automatic evaluation. A great deal of research strongly supports that the amygdala an almond-shaped group of neurons located deep in the medial temporal lobes of the brain, plays an importa nt role in evaluation learning (Hamann, Ely, Hoffman, & Kilts, 2002; Irwin et al., 1996). For exam ple, several studies correlated the neural responses in the amygdala with a ffective judgments of emotional pictures (Phan et al., 2004) and words (Cunningham, Raye, & Johnson, 2004), as well as pleasant and aversi ve odors (Royet et al., 2003). Moreover, previous re search suggested that the amygdala was more responsive to negative than positive images (Morris et al., 1996; Reekum et al., 2007) and to names of bad 22

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people than to names of good people (Cunningham et al., 2003). Furthermore, the amygdala plays a critical role in adva nced decision making by generating emotional states and attaching affective attributes to stimuli (Bechara et al., 1999). Evidence from both lesion (Adolphs, Tranel, & Damasio, 1998) and functional neuroimaging st udies (Winston, Strange, ODoherty, & Dolan, 2002) suggests that the amygdala plays a critical role in automatic processes of evaluation. For example, explicit requests of evaluations ar e not necessary to pr oduce amygdala activation (Cunningham et al., 2003; Lane et al., 1997; Wright et al., 2008). Moreover, the left and right amygdala have dissociable functions in different stages of memory for emotional material (Sergerie, Lepage, & Armony, 2006). Specificall y, the right amygdala is involved in the formation of emotional memories, whereas the left amygdala is involved in the retrieval of those memories. Making emotional evaluations also modulates brain responses to affective stimuli in other areas, such as the insula Functional imaging experiments have revealed that the insula plays an important role in the experience of pain and basic emotions such as anger, fear, disgust, happiness, and sadness. The insula has extens ive connections with the amygdala, and these connections enable these two structures to ope rate as a functional uni t in emotion induction (Taylor et al., 2003). For example, previous resear ch demonstrated that evaluating responses to pleasant or aversive odors elicited activation in the amygdala, the anterior cingulate cortex, and orbito-frontal cortex, as well as the insula (Royet et al., 2003). More specifically, like the amygdala, the insula is involved in automatic ev aluation, as it responds to the valence (i.e., pleasantness vs. unpleasantness) of target stim uli regardless of intention (Cunningham et al., 2004). 23

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Finally, previous research has indicated that the orbitofrontal cortex (OFC) participates in automatic emotional functioning (Cunningham et al., 2004). Specifically, the OFC receives extensive sensory input and sends output to areas that are importa nt for emotional processing and expression, such as the amygdala (Cunningham & Zelazo, 2007; Rempel-Clower, 2007). Moreover, in prior research, activity in the la teral OFC correlated with the degree to which participants tried to control their evaluative judgme nts of social concepts (e.g., happiness, murder) (Cunningham et al., 2004). In addition to its contribution to evaluative processing, the OFC is active during decision-making, expect ation formation, reward-based learning, and representation of the aff ective value of reinfor cers (e.g., food). In particular, the human OFC is thought to regulate planning behavi or associated with sensitiv ity to reward and punishment (Bechara et al., 1994). A large meta-analysi s of the existing neuroimaging evidence demonstrated that activity in medial parts of the OFC is related to the monitoring, learning, and memory of the reward value of reinforcers (K ringelbach & Rolls, 2004). The same meta-analysis showed that the activity in lateral or poster ormedial OFC is relate d to the evaluation of punishment and subsequent change in ongoing beha vior (see also Petrides, 2007). For example, the orbito-frontal cortex interacts extensively with the hippoc ampal memory system in the longterm declarative memory storing process (Ramus et al., 2007). By particip ating in both automatic affective and cognitive processing, the OFC plays an important role in behavioral regulation and cognition in general. 2. Brain regions associated with controlled evaluation. Contrary to the amygdala and the insula, the anterior cingulate cortex (ACC) is believed to be involved in controlled processes of evaluation (Cunningham et al., 2003; Cunningham et al., 2004; Taylor et al., 2003; Critchley, 2005). Activations of the anterior cingulate cortex, involving both the ventral ACC (BA 24) and 24

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the dorsal ACC (BA 32), have been observed in functional neuroimaging studies that span a wide range of cognitive contexts including selective attention and memory (Cabeza & Nyberg, 1997). Prior research implicates ACC in supporti ng conscious experience including emotional feeling states (Egan et al., 2003; Hariri et al., 2003; Lane, Chua, & Dolan, 1999; Papez, 1937). Reportedly, for example, the an terior cingulate cortex engage s in conscious evaluation and appraisal, together with the prefrontal cortex, by regulating amygdala activity (Hariri et al., 2003). Specifically, the anterior cingulate cortex was more ac tive during cognitive evaluation (e.g., evaluating whether content of a target picture is natural or ar tificial) than during perceptual processing (e.g., matching identical targets) of the same targ et. Moreover, Lane and his colleagues (1997) examined neural activity associated with selective a ttention to subjective emotional responses in a study in which participan ts viewed emotional pictures. They found that when evaluative (vs. non-evalua tive) judgments of visual stimuli were requested, activation increased in the ACC (as well as the temporal pole the frontal operculum ). In other words, the ACC is involved in controlled evaluation as its activation is higher when there is conscious awareness of evaluative tasks (see also Cunningham et al., 2003). Ther efore, we expected to find similar patterns of change in the ACC, the temporal pole, and the frontal operculum when evaluations proceduralize. Hypothetical Brain Functional Change s in Evaluation Proceduralization According to the above reviewed research evidence on memory and learning, we predicted that memory/knowledge of how to express explicit evaluations to stimuli is expected to become more proceduralized with practice. Therefore, brain regions in the basal ganglia system, the cerebellum, the inferior frontal cortex, and rela ted posterior parietal lobe may become more active after practice. Moreover, practice with explicit evaluative ta sks decreases activity in brain 25

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regions associated with declarative learning, including the medial temporal lobe, and prefrontal cortices (Figure 1-1A). Evaluative skill learning should also alter the level of control and monitoring exhibited in various frontal cortices (Figure 1-1A). For instance, regions involved in controlled processing of motor and cognitive skills (e.g., the dorsolateral and the ventromedial prefrontal cortices) may be less active for evaluative judgment tasks when eval uation is proceduralized. However, activation in the inferior frontal gyri duri ng evaluative judgments should increase due to its association with implicit skill learning. Although even a simple cognitive task performe d on emotionally salient stimuli can affect neural activation in emotion-asso ciated brain regions, a more im portant question is whether and how training in evaluation cha nges neural responses in evalua tion-related regions. On the one hand, repeated exposure to identical stimuli decreases neural re sponses in corresponding brain regions (Buchel, Coull, & Fris ton, 1999). This repetition suppr ession is thought to reflect a progressive optimization of neur onal responses elicited by the s timuli when behavioral learning performance, as well as the effective conn ectivity between corres ponding areas, increase. However, in past research on repetition suppres sion, the task stimuli used for participants practice were identical. Therefore, it is unclear whether brain activity may also decrease when the stimuli content changes duri ng learning. On the other hand, pr acticing a certain task can magnify the specific brain correlates of this ta sk (Grossman, Blake, & Kim, 2004). For example, signals in the critical regions involved in a motion detection task have been shown to be more active after extensive practice, and the magnitude of the increase was positively correlated with the degree of improvement in behavioral performa nce. Consistently, the left amygdala has been shown to be more active when making self-descriptiveness judgments of frequent vs. infrequent 26

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behaviors (Lieberman et al., 2004). Hence, in the current study, the ac tivation of evaluationrelated brain regions (e.g., the am ygdala, the anterior cingulate co rtex, the insula, the temporal pole, the frontal operculum, and the orbito-frontal cortex; Figure 1-1B) co uld either decrease or increase when evaluative judgments are procedur alized. In addition, we hypothesized that the pattern of activity change in regions associ ated with automatic evaluations and regions associated with controlled evaluations would be different. Spec ifically, evaluation proceduralization should have effects on auto matic evaluation regions regardless of task requirement, whereas on controlled evaluation re gions only during evaluations. Moreover, with evaluation practice, the magnitude of change in activities in evalua tive regions should be positively associated with the improvement in response times to evaluative judgments. Because evaluative judgments also require visual and motor systems, we are also interested in whether practice with evaluations influences br ain activity in visual a nd motor regions (Figure 1-1C). Several studies have suggested that practice with certain tasks does not influence the activation at early visual regions (e.g., a word classification task, Maccotta & Buckner, 2004; a picture priming task, Eddy et al., 2007) or pr imary motor regions (Maccotta & Buckner, 2004). In contrast, late visual regions are sensitive to practice with responses to visual targets (Eddy et al., 2007; Maccotta & Buckner, 2004). Therefore, in the current study, we predicted that practice with evaluative judgments may not influence th e activation in the earl y visual area (i.e., the calcarine) and the primary motor area (e.g., BA 4). In contrast, practice with evaluative judgments of visual stimuli s hould increase the activation of la ter visual areas such as the posterior fusiform and regions in the occipital lobe (e.g., the superior occipital gyrus, and the parietal occipital gyrus) (Grossman, Blake, & Kim, 2004). 27

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The Present Research Two studies were conducted to i nvestigate the influence and br ain functional correlates of practice with evaluative judgments. In Experi ment 1, we examined whether practice in evaluating pictures on a scale in creases the speed of evaluative judgments and the likelihood of making evaluative judgments of other visual stimuli. First, participants completed a practice task that entailed making either eval uative or non-evaluative responses to a set of pictures. After a series of filler tasks, particip ants were instructed to list th eir spontaneous thoughts about a new set of pictures. Hypothetically, ev aluative-skill learning should sp eed up evaluative judgments to visual images. In addition, pract ice in evaluative judgments s hould increase th e likelihood of making spontaneous evaluative judgments at a later time. To test the brain functional co rrelates of practice in evalua tive judgments, participants in Experiment 2 completed procedures similar to those used in Experiment 1 while in an MRI scanner. Specifically, participants brain activity was recorded both before and after they practiced making evaluative judgments (vs. non-evaluative judgments) to non-repeated visual images. Thus, by collecting brain images both befo re and after the practice, changes in brain activities may reveal the neural conseque nces of evaluation proceduralization. During evaluative skill learning, we expected in creased activation in the striatum (e.g., the caudate nucleus, the putamen, a nd the nucleus accumbens), the cerebellum, the inferior frontal gyri and the occipital cortex, but decreased activation in the medi al temporal lobe, and certain prefrontal cortices (i.e., the vent romedial prefrontal cortex and th e dorsolateral pre frontal cortex). In addition, if activation of the inferior parietal lobe increas es with evaluation proceduralization, that of the superior parietal lobe should decreas e, and vice versa. Furthermore, the activation in evaluation-related regions (e.g., the amygdala, the ACC, the insula, the temporal pole, the frontal operculum, and the OFC) may change (increase or decrease) due to practice in evaluation. In 28

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29 addition, activation change in regi ons related to automatic evaluation should occur regardless of whether explicit evaluations are re quired. In contrast, activation change in regions related to controlled evaluation should present only when explicit evaluations are requested. The hypothesized evaluation procedurali zation-induced changes in each brain region would be tested in clusters of interrelated regions. Moreover, correlational analyses would be conducted to examine the relations between regional changes in each cluster and behavioral performance.

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30 Figure 1-1. Brain regions associated with evaluation proceduraliza tion and predicted eff ects. Arrows indicate predicted directi ons of BOLD signal changes in each group of regions when evaluation proceduralization occurs. Up arrows indicate increases, down arrows indicate decreases, and right arrows indicate no change in BOLD signa ls of the group of regions on the right.

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CHAPTER 2 EXPERIMENT 1 Method Overview During Experiment 1, participants were seated at individual stations in front of a video monitor with stimulus presentation and instructio ns being controlled by a computer. They were told that they were participating in a study to explore the automaticity of social information processing, and that the procedure would involve a series of shor t computerized tasks. Based on random assignment, participants completed either evaluative or non-evaluative judgments of the same set of images selected from the Intern ational Affective Picture System (IAPS; NIMH Center for the Study of Emotion a nd Attention, Lang et al., 2001), pres ented in 4 blocks of trials. Specifically, participants in the evaluative-prac tice condition evaluated the pleasantness of the content of stimulus images using a scale provided for that purpose, wherea s participants in the non-evaluative-practice condition estimated the frequency with which images with content similar to the target image appear on televisi on, using a scale provided for that purpose. Next, participants completed a series of filler task s designed to reduce potentia l demand characteristics and to allow 30 minutes to elapse. Then, all pa rticipants completed a thought listing task in which they were asked to list their initial r eactions to each of four new stimulus images. Participants and Design A total of 48 undergraduate students (24 female s, 24 males) at the University of Florida participated in this study in exchange for partial fulfillment of requirements for course credit. The experimental design was a 2 (task: evaluativevs. non-evaluative practice) X 4 (block: first, second, third, or fourth) repeated-m easures factorial, with task be ing a between-subjects factor. 31

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Procedures After being welcomed by the experimenter, each participant was seated in front of a computer. Then, the experimenter informed the participants that the purpose of the current research was to study social information processing. After participants submitted signed informed consents, they were randomly assigned to either an evaluativeor a non-evaluative practice condition. In both of the conditions, part icipants were presented with a total of 168 images displayed on the computer screen, in f our blocks of 42 images each. In the evaluationpractice condition, participants were asked to report their evaluations of th e pleasantness of each image. In contrast, participants in the non-eval uation practice condition we re asked to report how frequently they thought that similar images a ppear on television. After completing the practice task, all participants completed a series of f iller tasks for 30 minutes to reduce potential demand characteristics. Towards the end of the experi ment, all participants completed a thought listing task in which they were asked to report their sp ontaneous reactions to each image of a new set in an open-ended response format. Finally, all partic ipants were debriefed, thanked, and dismissed. Materials and Measures Computer setting and stimulus images. The study was administered by IBM-compatible desktop computers using MediaLab software (Empirisoft Corporation, New York, USA). Computer display scr eens were set to 800 600 pixel resolution. The same stimulus set was used for the evaluativeand non-evaluative-practice c onditions. The practice contained 4 blocks, each comprised of 21 moderately pleasant and 21 mo derately unpleasant color images, pre-selected from the International Affective Picture System (IAPS; NIMH Center for the Study of Emotion and Attention, 2001)1 (see Table 2-1 for IAPS codes and imag e descriptions). Across the blocks, 1 There was no difference in the ratings of the IAPS pictures between each of the four blocks, pairwise comparison, p s > .10. 32

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4 (2 positivelyand 2 negatively-valenced) im ages were repeated to differentiate the proceduralization of evaluation on familiar and nove l targets. Finally, the stimulus set used to measure spontaneous thoughts af ter the practice consisted of 2 moderately pleasant and 2 moderately unpleasant color images, also pre-se lected from the IAPS (see Table 2-2 for IAPS codes). All images were resized to 410 307 pixels and digitized in 24-Bit RGB color. Evaluation practice. Participants randomly assigned to the evaluative-j udgment-practice condition were instructed as follows: This task involves evaluating the content of im ages. Please rate how pleasant (i.e., positive, good, pleasing, etc.) you find the content of each image using the scale provided. We are interested in both your evaluation and the speed with which you make it. Therefore, try to respond as quickly and as accurately as you can. The task consisted of four blocks of 42 trials Each trial presented the question, How pleasant do you find the content of this image? which was cen tered at the top of the display screen with an image presented in the center of the disp lay screen and a 7-point scale, ranging from 1 ( extremely unpleasant) to 7 ( extremely pleasant) presented vertically on the left side of the screen. Participants responded by clicking the le ft-mouse button on the ap propriate point on the scale. Stimulus images were presented in a random order during each block of 42 trials. Non-evaluation practice. Participants randomly assigned to the non-evaluative practice condition were instructed as follows: This task involves estimating th e frequency with which images of similar content appear on television. Please estimate the frequency using the scale provided. We are interested in both your estimate and the speed with which you make it. Therefore, try to respond as quickly and as accurately as you can. The task involved four blocks of 42 trials. Each trial presented the instruction, Please estimate the frequency with which images of similar content appear on te levision, which was centered at the top of the display screen with an image presen ted in the center of the display screen and a 7point scale, ranging from weekly to by the minute presented vertically on the left side of the 33

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screen. As in the evaluation pr actice condition, participants res ponded by clicking the left-mouse button on the appropriate point on the scale. Thought listing task. After the practice in evaluative or non-evaluative judgments, all participants were presented w ith a set of four new images. During the thought listing task, participants were asked to list their spontaneous thoughts of the c ontent of each stimulus image. These stimulus images were presented in a random order. To quantify the extent to which participants exhibited evaluati ve and frequency-related judgmen ts, each comment participants listed was coded as either evaluative or frequency-related by two independent coders, r = 0.81, p < .001. Specifically, comments such as it is di sgusting that people would live in such an environment were coded as evaluative, whereas comments such as it is common to see this on TV were coded as frequency-related, and comments such as it is a dog were coded as neither evaluative nor frequency-related. Results Response Time Because participants could spend as much time as they needed to respond to each stimulus, to correct for anticipatory responses and mome ntary inattention, the response time data from each participant were examined to eliminate out liers in the distribution of response times. Specifically, response times below 300 ms and above 3,000 ms were excluded. Thus, 1288 response times were regarded as missing data (16% of all responses). Then, the means and standard deviations of each participants response times were calculated. Then, for each participant, the response times beyond the range of [ M 2.5SD M + 2.5SD ] were also excluded (for similar data cleaning process, see Greenwald et al., 1998). Overall, 10 response times were regarded as outliers (0.15% of all valid responses). 34

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If the judgment practice was successful, partic ipants should have made quicker evaluative or non-evaluative judgments as the task progressed. Mean response times for each participant were computed for the four task blocks and ente red into a 2 (practice: evaluation vs. frequency) 4 (Task block: first, second, third, and four th) repeated-measures ANOVA, with practice being a between-subjects factor. The mean response times corresponding to this analysis appear in Figure 2-1. As expected, a signifi cant main effect of block re vealed that, with practice, participants became quicker at making judgments, F (3, 138) = 68.50, p < .001. No other main effect or interaction was detect ed. Moreover, there was a linear tr end in the decrease of response time over the four blocks, F (1, 46) = 148.70, p < .001. From the above results, we can infer that both evaluative and non-evaluativ e judgments of images pro ceduralized through practice. Moreover, the pattern of practice effects on repeated vs. new judgment targets was examined by a 4 (Task block: first, second, third, and fourth) 2 (Target: repeated vs. new) twoway repeated-measures ANOVA. Means of response times corresponding to this analysis appear in Table 2-3. As expected, participants made quicker ratings of images that they had rated previously ( M = 1412.90 ms) compared to new ones (M = 1609.88 ms) across blocks, F (1, 47) = 55.34, p < .001. Further analysis rev ealed that in Block 1, ther e was no difference between response times to the images repeated in the following blocks compared to images used only once (M = 1810.22 ms vs. 1786.92 ms, ns ). However, the response ti mes participants spent to rate previously-seen images decreased more with practice than the response times to new images across the practice blocks (block 2, 3, and 4), F (3, 141) = 17.56, p < .001. More importantly, as predicted, the response times to new images in the evaluative-practice co ndition also decreased significantly across the practice blocks, F (3, 75) = 31.09, p < .001, as well as those in the nonevaluative-practice condition, F (3, 63) = 21.81, p < .001. These findings suggest that response 35

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times of both previously seen and new images were influenced by the practice manipulation. No effects of practice condition (evaluative vs. non-ev aluative tasks) were found on response times or ratings to pictures. Responses to Tasks To examine the effects of prac tice on particip ants responses, a 4 (blo ck: one, two, three, vs. four) X 2 (valence: pleasant vs. unpleasant images) repeated measures ANOVA was performed on task ratings2 in both evaluative and non-evalua tive judgments practice conditions. Mean pleasantness and frequency ratings in each block appear in Table 2-4. During evaluation practice conditions, pictures selected as pleasant did receive higher pleasantness ( M = 5.48) ratings than pictures selected as unpleasant (M = 2.10), F (1, 25) = 100.11, p < .001. Moreover, there was a significant main effect of block on pleasantness ratings, F (3, 75) = 3.52, p < .05. That is, the pleasantness ratings decreased over the blocks. In contrast, there was no betweenblock difference in frequency ratings, F < 1. In addition, pleasant images ( M = 3.38) were rated as more frequently seen than unpleasant images ( M = 2.63), F (1, 21) = 6.15, p < .05. There was no interaction between block and valence in either the evaluative or the non-evaluative practice condition. Spontaneous Thought Listing To confirm that the evaluation practice task promotes greater spontaneous evaluative responding, the proportion of evaluativ e (frequency-related) judgments from each participant in the spontaneous thought listing task was computed as the percentage of number of evaluative (frequency-related) comments in the total number of comments of the corresponding participant. Then, we analyzed the proportion of coded evalua tive thoughts during the t hought listing task as 2 Only responses with valid response times were remained in the analysis. 36

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a function of the type of pract ice participants previously ha d. Specifically, a one-way ANOVA with judgment condition (evalu ative vs. non-evaluative judgme nts) as the between-subjects factor was conducted on the coded responses. Resu lts indicated that participants who completed the evaluative practice generated a greater proportion of evaluative comments ( M = 0.51) than participants who completed the non-evaluative practice ( M = 0.42), F (1, 46) = 3.90, p = .05. However, participants who completed the practice condition of frequency -related judgments (vs. evaluative judgments) did not generate a greater proportion of frequency-related thoughts towards new images, M = 0.004 vs. 0.003, F < 1. Thus, the evaluative practice appeared to elicit more spontaneous evaluations in response to new images, whereas practice in making nonevaluative judgments (e.g., frequency) did not increase the likeli hood of making similar judgments. No effects of valence of images were found, F < 1. Discussion The present findings suggest that, although eval uative responses are often automatic, there is still room for evaluative judgments to get fa ster. Specifically, in the current study, pa rticipants evaluative judgments of visual images became quicker with pract ice, and this effect was not content specific. Moreover, comp arable to Bassilis (1993) findi ngs that practice with general trait judgment increases the likelihood of subseque nt spontaneous trait in ferences, we found that practicing evaluative judgments (v s. frequency-related judgments) makes people more likely to make spontaneous evaluations at a later time. However, the proce duralization effects on frequency-related judgments did no t carry over to spontaneous res ponses to further targets. That is, although frequency-related judgm ents also speeded up with practice, practice did not make people more likely to make such responses spontaneously. This discrepancy in spontaneous responses might indicate that evaluations are mo re automatic than frequency-related judgments in the first place. Therefore, practice with frequency-related judgments did not lead to 37

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spontaneously thinking about something as specific as whether a picture is likely to appear on television. More research should be conducted to further examine the differences between evaluative and other respons es to novel targets. Based on the findings in Experiment 1 that evaluative judgments sp eeded up over practice (see also Smith, 1989), Experiment 2 was conducted to further explore what aspects of evaluation were proceduralized dur ing practice. Specifically, in E xperiment 2, we used an fMRI approach to investigate the brain activity underlying evaluation proceduralization. 38

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Table 2-1. IAPS picture codes for training tasks (Experiment 1) Block Positive pictures Negative pictures Code Description Code Description 1 5390 Boat 1300 Pit Bull 5875 Bicyclist 3230 Dying Man 1600 Horse 1301 Dog 1721 Lion 2299 Boy 1811 Monkeys 2688 Police 2095 Clowns 2692 Bomb 2165 Father 2715 Drug addict 2550 Couple 3160 Eye Disease 4255 Attractive Female 1200 Spider 5030 Flower 5971 Tornado 1463 Kittens 6212 Soldier 5600 Mountains 6213 Terrorist 5611 Mountains 6313 Attack 5731 Flowers 6570 Suicide 5760 Nature 6821 Gang 5780 Nature 6930 Missiles 1500 Dog 8231 Boxer 5994 Skyline 9090 Exhaust 7325 Watermelon 9230 Oil fire 7502 Castle 9390 Dishes 8500 Gold 9520 Kids 2 5390 Boat 1300 Pit Bull 5875 Bicyclist 3230 Dying Man 5811 Flowers 9530 Boys 4510 Attractive Man 9561 Sick kitty 4624 Couple 9280 Smoke 7286 Pancakes 9181 Dead cows 2311 Mother 9594 Injection 8502 Money 6350 Attack 7350 Pizza 2800 SadChild 8420 Tubing 3530 Attack 1590 Horse 9560 Duck in oil 7481 Food 9301 Toilet 8496 Water slide 6315 Beaten Female 5001 Sunflower 2722 Jail 1460 Kitten 3280 Dental Exam 5849 Flowers 9220 Cemetery 2071 Baby 1110 Snake 1603 Butterfly 9102 Heroin 1340 Women 2730 Native Boy 5594 Sky 9330 Garbage 8490 Roller coaster 9911 Car accident 3 5390 Boat 1300 Pit Bull 39

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Table 2-1. Continued Block Positive pictures Negative pictures Code Description Code Description 3 5875 Bicyclist 3230 Dying Man 2510 ElderlyWoman 9452 Gun 1942 Turtles 6230 Aimed Gun 1750 Bunnies 1932 Shark 7291 Chicken 3500 Attack 2209 Bride 2683 War 7340 Ice cream 2205 Hospital 4538 EroticMale 9584 Dental Exam 7470 Pancakes 9800 Skinhead 7270 IceCream 9042 Stick thru lip 2660 Baby 5970 Tornado 2655 Child 1201 Spider 7400 Candy 2751 Drunk driving 1731 Lion 2691 Riot 2092 Clowns 9342 Pollution 5990 Sky 1019 Snake 7289 Food 7359 PieW/bug 5626 HangGlider 2720 Urinating 8497 CarnivalRide 2710 Drug addict 1920 Porpoise 9417 Ticket 4 5390 Boat 1300 Pit Bull 5875 Bicyclist 3230 Dying Man 5779 Courtyard 9620 Shipwreck 8501 Money 9910 Auto accident 4610 Romance 2981 DeerHead 1640 Coyote 6020 Electric chair 2346 Kids 1280 Rat 4150 AttractiveFem 1310 Leopard 2310 Mother 9440 Skulls 1710 Puppies 2900 Crying boy 2391 Boy 9810 KKK rally 2030 Woman 9080 Wires 2345 Children 1112 Snake 1610 Rabbit 6415 DeadTiger 7330 Ice cream 8485 Fire 1602 Butterfly 9401 Knives 2650 Boy 9920 Auto accident 1440 Seal 9830 Cigarettes 2501 Couple 6830 Guns 4250 AttractiveFem 2702 BingeEating 7460 French fries 6300 Knife 40

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41 Table 2-2. IAPS picture codes for the thought listing task (Experiment 1) Picture set Code Description Pleasure Arousal Positive 5628 Mountains 6.51 (1.95) 5.46 (2.09) 5891 Clouds 7.22 (1.46) 3.29 (2.57) Negative 2750 Bum 2.56 (1.32) 4.31 (1.81) 9341 Pollution 3.38 (1.89) 4.50 (2.10)

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Table 2-3.Mean response times for evaluative and non-evalua tive tasks to new and repeat ed stimuli (Experiment 1) Judgment type Stimuli Block One Two Three Four Evaluative Repeated 1862.92 ( 432.10) 1549.98 (463.63) 1284.83 (386.12) 1162.74 (285.55) New 1868.34 (325.72) 1697.75 (345.64) 1705.26 (394.33) 1514.17 (363.98) Non-evaluative Repeated 1747.94 ( 580.65) 1419.73 (644.01) 1180.04 (550.86) 1057.02 (482.55) New 1690.69 (527.61) 1566.16 (598.54) 1446.09 (590.37) 1327.65 (544.68) Data presented in the cells are response times in milliseconds. Da ta in the parentheses are standard deviations of corresponding cell means. 42

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43Table 2-4. Ratings of pleasant and unpleasant images in each practice block (Experiment 1) Judgment type Valence Block One Two Three Four Evaluative Pleasant 5.58 (0.87) 5.59 (0.91) 5.30 (0.95) 5.44 (0.79) Unpleasant 2.15 (0.94) 2.07 (1.02) 2.05 (0.98) 2.13 (1.02) Non-evaluative Pleasant 3.29 ( 1.26) 3.37 (1.34) 3.35 (1.41) 3.52 (1.50) Unpleasant 2.81 (1.06) 2.58 (1.08) 2.56 (1.24) 2.55 (1.09) Data presented in the cells are ratings to images presented in each block. Data in the parenthe ses are standard deviations of corresponding cell means.

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1200 1300 1400 1500 1600 1700 1800 1900 2000 1234 BlockMean Response Latency (ms) Evaluation Non-evaluation Figure 2-1. Effects of task and practi ce block on response time (Experiment 1) 44

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45 CHAPTER 3 EXPERIMENT 2 Method Overview The aim of Experiment 2 was to explore the e ffects of evaluation pro ceduralization at the brain level. The procedures used in Experiment 2 were similar to the ones in Experiment 1 with two exceptions. First, the partic ipants were placed in an MRI scanner while they were completing the practice tasks. Second, to test the effects of evaluation practice, the brain activities of each participant we re recorded during evaluative and non-evaluative ratings both before and after a training run in evaluative tasks. Specifically, in the pre-training run, we presented 60 images (30 for evaluative tasks, a nd 30 for non-evaluative tasks) to all participants. Then, in the training run, all pa rticipants were asked to make evaluative judgments of 150 IAPS images. Towards the end of the experiment, in the post-training run, participants reported evaluative judgments and non-evalua tive judgments of a new set of images. The brain activity of each participant was measured during both the pre-training and the post-training runs using functional magnetic resonance imag ing (fMRI). In addition, response times were recorded for all judgments. In both the pre-training and pos t-training runs, participants had to attend to pictures, generate ratings, and make motor responses for bot h types of tasks. However, only the evaluative skill was trained in the training run. Thus, by comparing neural re sponses during evaluative (vs. null trials) condition and non-evaluative (vs. nu ll trials) condition before and after evaluative skill learning, we could distinguish the unde rlying processes that are specific to proceduralization of evaluation.

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We hypothesized that the evaluation practi ce may influence the brain activity while participants make evaluative judgments. Specifi cally, after the practice run, activities in the amygdala and other evaluation rela ted regions for evaluative task s would either increase or decrease compared to activities prior to the evalua tive training run. Furthermore, increased regional activities should be observed in the proce dural learning regions an d late visual regions, whereas decreased activities should be detected in declarative learni ng related regions and prefrontal areas. Participants Sixteen male students4 at the University of Florida participated in Experiment 2 to fulfill the requirement of a general psychology class. One participant was excluded due to discrete head movements greater than 1mm duri ng the scanning. Due to technical reasons, data from another participant were missing. Thus, data from 14 partic ipants remained in the analysis. Participants ages ranged from 18 to 24 ( M = 19.71, SD = 1.68). Based on the results of screening tests and sa fety checks, none of the participants had a history of medical, neurological or psychiatri c disorder. Also, participants were not taking psychotropic medication and did not have a history of substance abuse. Also, all participants had normal visual acuity. Procedures The participants were recruited for participa tion in a social cognitive neuroscience study that involved MRI scanning while viewing pictures. After turning in the signed informed consent 4 Because there was no difference in evaluation procedur alization between male and female participants in Experiment 1, F < 1, and the greater safety of fMRI for males, on ly male participants were recruited in the fMRI study. 46

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forms, participants entered the scanning room. Th e scanning technician assisted participants to get ready for the scan. After completing a pre-training run on 30 ev aluative and 30 non-evaluative judgments, each participant completed an evaluation training r un as well as a post-training run. In these three runs of the study, participants were presented with pictures selected from the International Affective Picture System (IAPS; Center for th e Study of Emotion and Attention, 2001) which contains a diverse range of pi ctures that have been reliab ly coded along several continuous dimensions of emotionality (i.e., valence, and arousal) (see Table 3-1, 3-2, 3-3 for information of IAPS pictures presented in the pre-traini ng, training, post-training runs, respectively). Specifically, in the preand posttraining runs of the study, a cue below e ach picture instructed participants to make either an evaluative or a non-evaluative judgment of the picture being presented, while in the training run, only evaluative judgment cues were presented. The purpose of the training run was to proceduralize evaluation, and the post-traini ng run was performed to evoke neural responses to evaluative and non-evaluative judgments to provide comparisons of these responses with brain activities for evaluative and non-evaluative judgments in the pretraining run. Functional Imaging Data Acquisition Participants were scanned using a Siemens Allegra 3 Tesla scanner (Siemens, Munich, Germany) with a standard head coil. Anatom ic images were acquired using an MPRAGE sequence with TR = 1500 ms, TE = 4.38 ms, and flip angle = 8. In the axial plane, 160 slices were acquired (thickness 1.0.2 mm, according to the he ight of the brain) with in-plane field of view of 240 mm 180 mm and a matrix size of 256 192 voxels. Functional images covering the whole brain were acquired using echo-plan ar imaging sensitive to blood-oxygenation level dependent (BOLD) effects, with TR = 3000 ms, TE = 30 ms, and flip angle = 90. In the axial 47

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plane, 38 slices with a thickness of 3.8 mm were ali gned with the plane of the intercommissural line and had a 240 240 mm in-plane field of view and a matrix size of 64 64 voxels. The functional tasks were presented using an Inte grated Functional Imaging System (IFIS, MRI Devices, Inc., Waukesha, WI) with a 7-inch LCD screen at 64 0 480 pixel resolution, mounted over the participants head and viewed using a fixed prism mirror. The screen subtended approximately 14 11 of the visual field. A PC running E-Prime (Psychology Software Tools, Pittsburgh, PA) presented each task trial in synchronization with the first RF pulse of each scan. Responses were collected using a MRI-compatible button glove attached to the participants right hand5. Materials and Measures Image selection. The images selected from the IAPS picture system were moderately intensive according to normative valence and arousal ratings obtained from a pilot study. Pleasant pictures included images of a blue sky, food, and flower s, whereas unpleasant pictures included stimuli such as images of a snake, a pit bull, and garbage. Different sets of stimuli were used in the pre-training (see Table 3-1), the tr aining (see Table 3-2), and the post-training (see Table 3-3) runs of the study. In the pre-training run, pictures selected as pleasant images had higher normative ratings in pleasantness [ M = 6.39, SD = 0.64; t (29) = 11.93, p < .001], compared to the neutral point of the 9-point scale used in IAPS system, whereas pictures in the unpleasant set had lower pleasantness scor es than the neut ral scale point, M = 3.85, SD = 1.15, t (29) = 5.45, p < .001. The same pattern was found for images selected for the post-training. That is, pictures selected as pleasant received higher scores than the neutral point of the scale ( M 5 All participants are right-handed, accor ding to the pre-screening self-reports. 48

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= 6.79, SD = 0.83; t (29) = 11.76, p < .001), whereas pictures sele cted as unpleasant received lower scores than the ne utral point of the scale, M = 3.82, SD = 0.89; t (29) = 7.24, p < .001. More importantly, the IAPS picture sets sele cted for Experiment 2 were matched in valence and arousal distribution across judgment type (evaluative vs. non-evaluative tasks) and run (prevs. posttraining runs). Specifically, in the pre-traini ng run, the mean valence (pleasant vs. unpleasant) score was 5.08 (SD = 0.64) for evaluative tasks and 5.16 ( SD = 0.64) for nonevaluative tasks, pairwise comparison, ns and the mean arousal (exciting vs. calm) score was 4.44 ( SD = 0.79) for evaluative tasks and 4.76 ( SD = 0.79) for non-evaluative tasks, pairwise comparison, ns In the post-training run, the mean vale nce (pleasant vs. u npleasant) score was 5.30 ( SD = 0.60) for evaluative tasks and 5.31 ( SD = 0.60) for non-evaluative tasks, pairwise comparison, ns and the mean arousal (exciting vs. calm) score was 4.98 ( SD = 0.90) for evaluative tasks and 4.68 ( SD = 0.90) for non-evaluative ta sks, pairwise comparison, ns In addition, for evaluative tasks, there were no sign ificant differences in pleasantness ratings ( M = 5.08 vs. 5.30, ns prevs. posttraining r un) or arousal ratings ( M = 4.44 vs. 4.98, ns prevs. posttraining run) between images presented in the preand posttraini ng run. Also, there were no between run differe nces in valence ( M = 5.16 vs. M = 5.31, ns prevs. posttraining run) and arousal (M = 4.76 vs. M = 4.68, ns prevs. posttraining run) ra tings in pictures selected for non-evaluative tasks. This similarity of input im ages should ensure that different brain responses are due to evaluation proceduraliz ation or judgment type, but not to the images themselves. Parameters of images presentation. Unlike in Experiment 1, we did not use a betweensubjects design in the preand post-training sessions. Instead, we alternated the presentation of the evaluative and non-evaluative tasks in a rand om order to avoid confounding training effects with anticipation effects. Specifically, in the pr eand posttraining runs each participant was 49

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presented with 60 images (30 for evaluative judg ments, and 30 for non-evaluative judgments) for 3 seconds each, along with 30 null trials, duri ng which a fixation cross was displayed for 3 seconds. Thus, the preand post-training runs ea ch lasted 4 minutes and 30 seconds. Null trials were included in the random sequence in orde r to jitter the stimulus onset asynchrony (SOA) between trials, and to increase the variance in the resulting fMRI response and make the response to rapid stimuli (SOA < 15 seconds) detectable (Burock et al., 1998). Moreover, jittering the SOA with randomly interspersed null trials crea tes a geometric distribution of SOAs, which is believed to be more efficient than unifor m randomization (Serences, 2004). To minimize response attenuation when repeating images while maximizing the number of trials in the pretraining and post-training sessi ons (Soon, Venkatraman, & Chee, 2003), the resulting mean SOA was set to 4.5 seconds, with a minimum of 3 seconds. In the evaluative skill training run, 150 evaluative trials were presented for 2 seconds each in ten 30-second blocks of 15 images, which we re separated by 12-second rest blocks during which a fixation cross was displayed. Thus, th e training run lasted 7 minutes and 12 seconds. The number of positive and negative trials was ba lanced in every two blocks. That is, if 7 positive and 8 negative trials were presented in the previous block, then 8 positive and 7 negative trials were presented in the following block. The trials were presented more rapidly during training than testing to induce proceduralization. Evaluative and non-evaluative ratings. In the pre-training and th e post-training sessions, both evaluative and non-evaluative judgment cues were used (see Appendix A for instructions). In the evaluation-training session, only evaluati ve judgments were requested. Specifically, in Experiment 2, the evaluative-judgment cue was How pleasant do you find the content of this image? (see Figure 3-1 for sample images), wh ich was followed by a 4-point scale ranging from 50

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extremely unpleasant to extremely pleasant In contrast, the non-evaluative-judgment cue read, How frequently do images with similar cont ent appear on television ? (see Figure 3-1 for sample images) and was followed by a 4-point scale ranging from rarely/never to always The brain activities during evaluative and non-evaluative ratings were recorded as the dependent measures. Regions-of-interest selection. In the current research, a regions-of-interest (ROI) approach was employed to test our hypothesized e ffects of practice of ev aluative skills on brain activity. There are advantages to an ROI approach. When subtra cting two conditions in a wholebrain group-analysis, the significan ce of all voxels is determined using a Bonferroni correction for the number of voxels. Therefor e, arguably, the correct ion for the number of all voxels is too strict, as only a few regions are of real interest. T hus, regional analyses afford considerable power by reducing the number of multiple comp arisons and averaging multiple voxels within each region, thereby increasing signal-to-noise ratios (Poldrack, 2007). In addition, because regions are derived a priori, th eir signal estimates are unbias ed (Maccotta & Buckner, 2004). Our regional analyses explored signal magnitude estimates within a set of a priori regions that spanned the basal ganglia system, prefrontal cortex, evaluative proce ssing regions, early and late visual cortex, and the motor cortex. First, pe ak locations of these regions were derived from prior research on related tasks. For example, regions related to evaluative processing were mainly derived from previous work on social evaluation and subjective emotional responses, and regions related to learning were selected based on previous research on motor and cognitive skill learning. Then, three-dimensional regions of intere st were defined by including all voxe ls within 5 mm of each activation peak (Etkin et al., 2006; Maccotta & Buckner, 2004). This method of creating small ROIs at the peaks of activation cl usters based on previous research has been 51

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regarded as particularly suitable for complex desi gns, such as factorial de signs, and it can depict signal patterns in specific regions within larger anatomical cl usters (Poldrack, 2007). Results Our study was aimed at exploring the brain activity during evaluation procedualization, using null trials as the comparison condition. We hypothesized that practice with evaluation changes activation patterns in majo r brain regions related to aff ect and evaluation, such as the activation patterns in the amygdala the insula, the orbito-frontal cortex, the anterior cingulate cortex, the temporal pole, and the frontal ope rculum. In addition, we expected that task instructions (evaluativ e vs. non-evaluative task instructions ) would influence the pattern of activation change in regions associated with c ontrolled (vs. automatic) evaluation. The direction of the difference was an empirical question that we hoped analyses would resolve. Also, because evaluative judgments are procedur alized during the training run, ev aluative judgments (vs. null trials) may produce differential activation in the pr ocedural learning regions (declarative learning regions) more (less) in the post-trai ning run than in the pre-training run. Behavioral Findings Response Time Response times for evaluative and non-evaluativ e tasks were recorded during both the pretraining and the post-training runs. Unlike in Experiment 1, responses to images in Experiment 2 had a 3000ms window in both pre-tr aining and post-training runs. Consequently, 47 responses (3.22% of all responses) faster th an 300 ms were regarded as mi ssing data as they most likely reflected late responses to the previously pres ented images. In addition, response times outside the range of [ M 2.5SD M + 2.5SD ] for each individual in each run were deleted as outliers. Overall, 20 response times were regarded as outliers (1.37% of all responses). 52

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Response times were analyzed as a function of run (prevs. post-training) and task (evaluative vs. non-evaluative) us ing a mixed model with linear and quadratic trends in time as covariates. The analysis revealed a significant main effect of run on response time, F (1, 1387) = 14.99, p < .001. Specifically, participants responded to images faster in the post-training run than in the pre-training run, Mdiff = 94.44 ms. In addition, there was a significant main effect of task, F (1, 1387) = 32.72, p < .001. That is, across the preand posttraining runs, participants responded faster to evaluations than to frequency judgments, Mdiff = 139.55 ms. Moreover, there was a marginal interaction betw een run and task in response time, F (1, 1387) = 2.98, p < .10. Participants reported evaluations faster during the post-training run ( M = 1866.87 ms) than the pre-training run ( M = 2003.41 ms), p < .001. Although response times for non-evaluative tasks tended to be faster in the post-training run than in the pretraining run, the effect was not significant, M = 2048.51 vs. 2100.86 ms, p > .10. In sum, these findi ngs suggest that trained evaluative responses speeded up, whereas untrained non-evalua tive responses did not. A manipulation check was also performed to examine whether evaluative and nonevaluative judgments were proce duralized in the pre-training run. For this purpose, separate mixed models of the effects of experiment time on response time were used for evaluative tasks and non-evaluative tasks in the pre-training r un. Results showed no linear trend or quadratic trend in response times for either evaluative or non-evaluative tasks in the pre-training run, F s < 1. Furthermore, the same analysis was perfor med for the post-training run to see whether evaluative judgments received furt her proceduralization in the post-training run. Results showed no speeding up in evaluations during the post-training run, F < 1, confirming that evaluations were proceduralized during the evaluative skill learning session. In contrast, in the post-training run, the linear trend in response times for non-eval uative judgments was marginally significant, 53

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F (1, 21.85) = 3.68, p = .07. This manipulation check conf irmed that there were no training effects on either evaluative or non-evaluative tasks in the pre-tr aining run. In contrast, evaluative tasks were proficiently proceduralized in the evaluation training sessi on as the speed was not improved in the post-training run. However, b ecause non-evaluative task s were not practiced between the preand posttraining runs, we observed practice effects on non-evaluative tasks during the post-training run. Responses to Tasks Ratings of visual stimuli that were made w ith invalid response times were regarded as missing ratings. Then, to examine the effects of training on evaluative judg ments on participants ratings of pleasantness, a 2 (run: pre-training vs. post-training run) X 2 (valence: pleasant vs. unpleasant images) ANOVA was performed on task ra tings. Means of responses related to this analysis appear in Table 34. Results indicated a significan t main effect of valence on pleasantness ratings, F (1, 174) = 208.69, p < .001. Not surprisingly, pictures selected as pleasant did receive higher pleasantness ( M = 3.84) ratings than pictures selected as unpleasant ( M = 2.86). Moreover, there was a significant in teraction between run and valence, F (1, 24.70) = 29.64, p < .001. That is, the difference in pleasant ness ratings of positive and negative images was greater in the post-training run ( M = 3.99 vs. 2.64, positive vs. negative images, pairwise comparison, p < .001) than in the pre-training run ( M = 3.69 vs. 3.08, positive vs. negative images, pairwise comparison, p < .001). Furthermore, tests of simple effects showed that pleasant images were rated as more pleasant in the post-training run than in the post-training run, M = 3.99 vs. 3.69, p < .01. In addition, unpleasant images were rated as more unpleasant after the evaluation training, M = 2.64 vs. 3.08, post-traini ng vs. pre-training run, p < .001. No main effect of run was found on evaluative responses, p < .30. 54

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A comparison between non-evaluative (frequency -related) ratings in pre-training and posttraining runs was examined by conducting a 2 (ru n: pre-training vs. pos t-training run) X 2 (valence: pleasant vs. unpleasant images) ANOVA. M eans of responses relate d to this analysis appear in Table 3-4. First, the ma in effect of run was significant, F (1, 4.87) = 6.54, p < .05. That is, images presented in the post-training run ( M = 3.20) were rated as less frequently seen on TV than the ones presented in the pre-training run ( M = 3.37). Moreover, we found a marginal 2-way interaction between run and valence, F (1, 2.26) = 3.04, p = .08. Specifically, there was no difference in frequency ratings between positive ( M = 3.36) and negative images ( M = 3.37) in the pre-training run, ns However, in the post-trai ning run, negative images (M = 3.09) were regarded as seen less often than positive images ( M = 3.31), p < .05. In other words, frequency ratings to pleasant images did not differ acro ss runs, whereas those to unpleasant images decreased. No main effect of va lence on frequency ratings was found, p > .10. Furthermore, the distribution of ratings for both types of tasks in the preand posttraining run appears in Table 3-5. Speci fically, participants made use of all four buttons during both evaluative and non-evaluative tasks, rather than simplifying the task by using only the extreme ratings. Importantly, although participants rated po sitive images as more pleasant than negative images to a greater extent in the post-training (vs. pre-training) run, an equality test indicated that the distribution of the frequency of responses di d not change from the pr eto the posttraining runs, K-S Z < 1, ns However, the frequency of using different buttons for non-evaluative tasks was distributed differently in the post-training run than in th e pre-training run, K-S Z = 1.65, p < .01. In addition, as another indicator of improve d performance across runs the percentage of omitted trials for each type of task decreased sign ificantly in the post-training (vs. pre-training) 55

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run (see Table 3-5 for number of omitted trials for each condition). Specifically, for evaluative tasks, the percentage of omitted trials dropped si gnificantly from the pre(rate = 19.50%) to the post-training run (rate = 6.90%), 2 (1, N = 840) = 29.16, p < .001. Reduced omit rate was also found for non-evaluative tasks (26.70% vs. 10.95%, pre-training vs. post-training run, respectively), 2 (1, N = 840) = 33.96, p < .001. Functional Imaging Data Analyses The functional imaging data of each particip ant were analyzed using BrainVoyager 1.7.6 (Brain Innovations, Maastricht, Holland) and SP SS 13. The functional images were coregistered with anatomic images, and normalized to Talair ach space for each participant. Functional data were processed with 3D motion correction, linear trend removal, slice scan time correction, and spatial smoothing. The pre-traini ng and post-training runs underwe nt Gaussian spatial smoothing using a kernel of 5.7 mm (1.5 voxels) full-width half-maximum (FWHM). Task-related activity was mapped using a voxelwise general linear modeling analysis. For event-related analyses, the BOLD responses we re estimated using a standard hemodynamic model (Friston, Josephs, Rees, & Turner, 1998). Th e estimated responses were fit to the MR signal for each individual to generate a beta weig ht. Then, in the preand post-training runs, BOLD responses in each ROI at each time poi nt were obtained, including evaluative, nonevaluative, and null trials. After that, for each pre-training and post-traini ng run event, percentage signal change was calculated. Specifically, voxels within each ROI we re averaged to create a single time series (containing 90 time points) for each participan t during each run. With in each run, percent changes in BOLD signal associated with evaluati ve (non-evaluative) responses were calculated as the difference between the magnetic resonanc e signal during the evaluative (non-evaluative) tasks and the signal during the nul l trials, divided by the averag e BOLD signal during the null 56

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trials. At last, a mixed model w ith linear and quadratic time tre nds as covariates was used to estimate the effects of task (e valuative vs. non-evaluative judg ments) and run (prevs. posttraining). Tests of Evaluation Proceduralization Regions-of-interest were crea ted for regions proposed to be involved in evaluation proceduralization (see Table 3-6 fo r relevant coordinates of each ROI). As ROIs were defined a priori, the same ROI template wa s applied to all participants. For each individual participant, separate estimates of the hemodynamic response were generated for each condition at each voxel using a deconvolution analysis. Al l voxels within 5mm around the centers of the ROIs in Table 3-6 were included in the analysis Specifically, the per centage of BOLD signa l changes in these ROIs at each second in the pre-training and the post-training runs were calculated as the difference between the BOLD signal during the (e valuative, non-evaluative ) tasks and the BOLD signal during the fixation points (i.e., the null trials), divided by the BOLD signal in fixation points in the corresponding run. For exam ple, a BOLD signal change of 0.50 ( 0.50) at a certain time point indicates a regional ac tivity of 50% percentage high er (lower) than the baseline activity of that individual during the corresponding run (p reor posttraining run). Mixed model analysis. In the current study, the evaluati ve and non-evaluative tasks, as well as the null trials, were randomly presented in both the preand th e posttraining runs. Therefore, for each participant, the time interv als between evaluative-task trials and between non-evaluative-task trials were not constant. In this situation, autocorrelation coefficients can only be obtained directly by fitting a smoothed a pproximation to the time series. Therefore, we fitted time series using the mixed-model modul e of SPSS. This model incorporates betweensubjects variability and allows modeling c ovariances that need to be considered. 57

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To test the significance of signal change, regi onal amplitude estimates in each region were entered into the mixed model module of SPSS. Sp ecifically, the mixed mo del entailed practice run (prevs. posttraining), valence (pleasant vs. unpleasant), and ta sk (evaluative vs. nonevaluative) as factors, with linear and quadrat ic time trends as covariates. The linear and quadratic time trends were included to model th e time series. All factors were fixed, and an autoregressive residual covariance structure (AR1 ) was used to account for the autocorrelation in the time series data. In additi on, correlations between linear (quadratic) time trend and other factors (i.e., task, valence, and r un) were included in the analysis The degrees of freedom in the mixed model were calculated using the Satterthwaite appr oximation, which produces data dependent degrees of freedom. St atistical thresholds for mixed-model analyses for each ROI were set at p < .05. Cluster analysis. To test our hypotheses of activity cha nge in regions of similar functions, estimated regional BOLD signals of ROIs with si milar predicted functions were congregated into clusters before entered to mixed models. Correction of alpha errors was applied at the cluster level. Findings in Regions Associated with Learning Regions associated with procedural learning. To confirm our hypotheses that evaluation proceduralization produces increas ed activities in brain regions previously associated with procedural learning, we centered the caudate nu cleus on 15, 4 (Poldrack et al., 1999), the putamen on 18, 1, 15 (Mallol et al., 2007) and 27, 8, 4 (located in an independent study, N = 8), the nucleus accumbens on 4, 6, 2 (Lieberman et al., 2004), the cerebellum on 31, 65, 18 (Willingham et al., 2002), the precuneus on 12, 67, 50 (Willingham et al., 2002), the inferior parietal lobe on 50, 26, 36 (Poldrack et al., 1999) and 34, 46, 56 (Mallol et al., 2007), and the inferior frontal gyri on 17, 24 and 3, 32 (Maccotta & Buckner, 2004) (see Table 3-6 58

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for a list of information for each ROI). These regi ons were included in the cluster of procedural learning in the cluster analysis. Caudate nucleus. The right caudate nucleus area (loc ated around 9, 15, 4; Poldrack et al., 1999) showed a significan t main effect of run, F (1, 1310) = 9.46, p = .002. The activation of the right caudate nucleus was highe r in the post-training run than in the pre-training run, Mdiff = 0.11, t (1310) = 3.08, p = .002. This between-run effect was present in the ev aluative tasks ( M = 0.14, 95% CI = 0.04, 0.24, vs. M = 0.29, 95% CI = 0.18, 0.39; pre-tr aining vs. post-training run, p < .01), but not in non-evaluative tasks ( M = 0.21, 95% CI = 0.10, 0.32, vs. M = 0.28, 95% CI = 0.18, 0.38; pre-training vs. post-training run, p > .15). However, the inte raction between run and task did not reach significance, F (1, 1559) = 1.11, p > .20. Also, there were no simple effects of different types of task on the right caudate nucleus in either the pre-trai ning or the post-training run, both ps > .30. We found a similar BOLD signal change pattern in the left caudate nucleus (centered on 9, 15, 4). Specifically, the effect of different run (pre-training vs. post-training run) was marginally significant, F (1, 1293) = 3.45, p = .06. The left caudate nucleus was more active in the post-training run ( M = 0.25, 95% CI = 0.14, 0.36) than in the pre-training run ( M = 0.19, 95% CI = 0.08, 0.30). Moreover, the intera ction between run and task r eached a marginal significance, F (1, 1534) = 3.66, p = .06. As in the right caudate nucleus the activation of the left caudate nucleus for evaluative tasks was hi gher in the post-training run ( M = 0.28, 95% CI = 0.16, 0.40) than in the pre-training run ( M = 0.15, 95% CI = 0.03, 0.27; p < .01), but there was no betweenrun difference in the activation for non-evaluative tasks ( M = 0.23, 95% CI = 0.11, 0.36, vs. M = 0.23, 95% CI = 0.11, 0.35, pre-trai ning vs. post-training run, ns ). Again, we found no betweentask differences in the left caudate nucleus either before or after the training run, both ps > .05. 59

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Because the findings in the left and right cauda te nucleus were similar, BOLD signals were combined to test the overall effects of evaluati on proceduralization in th e caudate nucleus region. Results of the mixed model s howed a main effect of run, F (1, 1297) = 6.94, p < .01. That is, the bilateral caudate nucleus was more active in the post-training run ( M = 0.27, 95% CI = 0.17, 0.37) than the pre-training run ( M = 0.18, 95% CI = 0.09, 0.28). In addition, evaluative tasks triggered higher activation in the bilateral caudate nucleus in th e post-training run ( M = 0.28, 95% CI = 0.18, 0.39) than in the pre-training run ( M = 0.14, 95% CI = 0.04, 0.25), p < .01. In contrast, the activation in the bilateral caudate nucleus for non-evaluative tasks did not change ( M = 0.22, 95% CI = 0.11, 0.33 vs. M = 0.26, 95% CI = 0.15, 0.37, pretraining vs. post-training run, ns ). However, the interaction between ta sk and run did not reach significance, F (1, 1541) = 2.44, p = .12. No main effect of task was found, F < 1. Putamen. There were main effects of run and ta sk on the caudoventral regions of putamen (centered on 18, 1, 15; Mallol et al., 2007). Specifically, activation in this putamen area was higher in the post-training run ( M = 0.27, 95% CI = 0.21, 0.33) th an the pre-training run ( M = 0.18, 95% CI = 0.11, 0.24), F (1, 1410) = 12.67, p < .001. Moreover, this increased activation in the caudoventral putamen was found for both evaluative ( M = 0.21, 95% CI = 0.14, 0.28, vs. M = 0.29, 95% CI = 0.22, 0.36; prevs. posttraining run, p < .05) and non-evaluative tasks ( M = 0.14, 95% CI = 0.07, 0.22, vs. M = 0.25, 95% CI = 0.18, 0.32; prevs. posttraining run, p < .01). In addition, evaluative tasks ( M = 0.25, 95% CI = 0.19, 0.31) triggered more brain activity in the caudoventral putamen than non-evaluative tasks ( M = 0.20, 95% CI = 0.13, 0.26), F (1, 1625) = 4.63, p < .05. No other main effects or interactions were found, F s < 1. The rostrodorsal regions of the putamen (centered on 27, 8, 4; located in an independent study, N = 8) exhibited a different pattern reflec ted in a significant main effect of run, F (1, 60

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1392) = 18.36, p < .001. Specifically, activation in the ro strodorsal putamen decreased with practice, both for evaluative tasks ( M = 0.33, 95% CI = 0.18, 0.48, vs. M = 0.22, 95% CI = 0.07, 0.37, pre-training vs. post-training run, p < .01) and non-evaluative tasks ( M = 0.35, 95% CI = 0.20, 0.49, vs. M = 0.20, 95% CI = 0.05, 0.35, pre-tr aining vs. posttraining run, p < .001). Nucleus accumbens. There was a significant main effect of run on the left nucleus accumbens area (located around 4, 6, 2; Lieberman et al., 2004), F (1, 1322) = 54.61, p < .001. Specifically, the activation of the nucleus accumbens was higher in the post-training run than in the pre-training r un, for both evaluative tasks ( M = 0.30, 95% CI = 0.12, 0.47, vs. M = 0.11, 95% CI = 0.28, 0.07, post-training vs. pre-training run, p < .001) and non-evaluative tasks ( M = 0.34, 95% CI = 0.17, 0.52, vs. M = 0.01, 95% CI = 0.17, 0.19, post-training vs. pretraining run, p < .001). There was no difference in nuc leus accumbens activation between different types of task within either th e pre-training or th e post-training run, ns Furthermore, we found a significant interacti on between task and valence in the left nucleus accumbens, F (1, 1562) = 4.08, p < .05. Specifically, when tasks were evaluative, the average BOLD signal change in the nucleus accumbens was 0.11(95% CI = 0.07, 0.29) for pleasant images and 0.06 (95% CI = 0.11, 0.24) for unpleasant images, ns In contrast, when tasks were non-evaluative, the average BOLD signal change was higher for unpleasant images ( M = 0.26, 95% CI = 0.08, 0.44) than for pleasant images ( M = 0.11, 95% CI = 0.07, 0.29), p < .05. No other main effects or interactions were significant, ps > .10. Cerebellum. There was a main effect of run on the activation of the right cerebellum area (located around 31, 65, 18; Willingham et al., 2002), F (1, 1217) = 15.01, p < .001. That is, the cerebellum was more activ e in the post-training run ( M = 1.54, 95% CI = 1.30, 1.79) than was in the pre-training run ( M = 1.29, 95% CI = 1.05, 1.54), p < .001. There was also a 61

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significant interaction between run and task, F (1, 1499) = 4.52, p < .05. Specifically, the cerebellum was more active for evalua tive tasks in the post-training run ( M = 1.59, 95% CI = 1.35, 1.85) than in the pre-training run ( M = 1.20, 95% CI = 0.93, 1.45), p < .001. In contrast, there was no between-run difference in the ce rebellum activation for non-evaluative tasks, M = 1.39 (95% CI = 1.13, 1.66) vs. M = 1.50 (95% CI = 1.24, 1.76), pre-training vs. post-training, ns In addition, during the pre-trai ning run, the cerebellum was more active for non-evaluative tasks ( M = 1.39) than for evaluative tasks ( M = 1.20), p < .05. However, the activation of the cerebellum in the post-training run di d not differ across non-evaluative ( M = 1.50) and evaluative tasks ( M = 1.59), ns No other main effects or interactions were found, ns Superior parietal lobe. We found a significant main effect of run on the precuneus area (BA 7; located around 12, 67, 50; Willingham et al., 2002), F (1, 1185) = 12.37, p < .001. In general, the right BA 7 was more active after the training run ( M = 0.42, 95% CI = 0.28, 0.55) than before the training run (M = 0.28, 95% CI = 0.15, 0.41). More over, there was a significant interaction between run and task, F (1, 1493) = 4.32, p < .05. Specifically, the precuneus activation for evaluative tasks increased significantly in the post-training run ( M = 0.44, 95% CI = 0.29, 0.58) compared to the activat ion in the pre-training run (M = 0.22, 95% CI = 0.08, 0.36), p < .001. In contrast, the activati on in the precuneus did not ch ange for non-evaluative tasks ( M = 0.34, 95% CI = 0.19, 0.49 vs. M = 0.39, 95% CI = 0.25, 0.54, pre-tr aining vs. post-training run, ns ). In addition, the activity of the precuneus was higher for non-evaluative tasks (vs. evaluative tasks) in the pre-training run, p < .05, but did not differ for the post-training run, ns No other main effects or interactions were found. Inferior parietal lobe. In addition to the superior part of the parietal lobe, we tested effects of run, task, and valence on the righ t inferior parietal lobe (centered on 50, 26, 36; 62

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Poldrack et al., 1999). Results showed a margina lly significant interaction between run and task in this region, F (1, 1595) = 3.14, p = .08. Specifically, the right infe rior parietal lobe was less active for evaluative tasks in the post-training run ( M = 0.15, 95% CI = 0.04, 0.25) than in the pre-training run ( M = 0.23, 95% CI = 0.12, 0.33), p < .05. However, there was no between-run difference in the activation of the right inferi or parietal lobe for non-evaluative tasks, (M = 0.14, 95% CI = 0.04, 0.25, vs. M = 0.16, 95% CI = 0.06, 0.27; pre-tr aining vs. post-training run, ns ). No other main effects or interactions were found, F s < 1. We also found a marginally significant interac tion between task type and run for the left inferior parietal lobe (centered on 34, 46, 56; Mallol et al., 2007), F (1, 1654) = 3.35, p = .07. However, the pattern of signal change was different from that of the right inferior parietal lobe. Specifically, the activation at th e left inferior parietal region for evaluative tasks increased significantly from the pre( M = 0.97, 95% CI = 1.757, 0.185) to the post( M = 0.62, 95% CI = 0.20, 1.44) training run, p < .01. In contrast, there was no si gnificant regional activity change for non-evaluative tasks ( M = 0.33, 95% CI = 0.53, 1.20, vs. M = 0.39, 95% CI = 0.43, 1.22; prevs. posttraining run, ns ). BA 46. To test effects of training on evaluation in the inferior frontal area, we centered the BA 46 on 47, 17, 24 (Maccotta & Buckner, 2004). Results s howed a significant main effect of run (pre-training vs. post-training), F (1, 1185) = 26.28, p < .001, indicating that the BA 46 region was more active in the post-training run ( M = 0.56, 95% CI = 0.46, 0.66) than in the pretraining run ( M = 0.40, 95% CI = 0.30, 0.50). Moreover, ther e was an interaction between run and task, F (1, 1484) = 4.47, p < .05. Specifically, post training in creases in the activation in the left BA 46 were greater for evaluative (M = 0.36, 95% CI = 0.25, 0.47, vs. M = 0.58, 95% CI = 0.47, 0.69; pre-training vs. post-training run, p < .001) than non-evaluative tasks ( M = 0.45, 95% 63

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CI = 0.34, 0.56, vs. M = 0.54, 95% CI = 0.43, 0.64; pretraining vs. post-training run, p = .05). Further analysis revealed that, in the pre-training run, non-evaluative tasks ( M = 0.45) triggered higher left BA 46 activation than evaluative tasks ( M = 0.36), p < .05, a pattern not present after the training run for either eval uative or non-evaluative tasks ( M s= 0.58 and 0.54), ns In addition, we found a similar BOLD change pattern in the right BA 46 area (centered on 47, 17, 24). After evaluative tr aining, activity in the ri ght BA 46 area increased ( M = 0.30, 95% CI = 0.21, 0.40, vs. M = 0.41, 95% CI = 0.31, 0.50; prevs. posttraining run), F (1230) = 13.80, p < .001. Moreover, there was a marginally sign ificant interaction be tween task and run, F (1, 1498) = 3.59, p = .06. The activity in the right BA 46 increased for evaluative tasks ( M = 0.26, 95% CI = 0.16, 0.36 vs. M = 0.42, 95% CI = 0.32, 0.52; pre-tr aining vs. post-training run, p < .001), but did not change for non-evaluative tasks ( M = 0.34, 95% CI = 0.24, 0.44 vs. M = 0.39, 95% CI = 0.29, 0.49; pre-trai ning vs. post-training run, ns ). Similar to the left BA 46 region, in the pre-training run, the right BA 46 region was more activ e for non-evaluative tasks ( M = 0.34) than for evaluative tasks ( M = 0.26), p = .06. However, the post-train ing run showed no effect of type of task ( M = 0.39 vs. 0.42, non-evaluative vs. evaluative tasks), ns No effects of valence (pleasant vs. unpleasant images) were found on either the left or right BA 46 areas. BOLD signals in the left and right BA 46 areas were combined to represent the activation change in bilateral BA 46 areas. Results of th e mixed model revealed a main effect of run, F (1, 1201) = 26.23, p < .001. That is, the bilateral BA 46 was more active after ( M = 0.47, 95% CI = 0.39, 0.55) than before ( M = 0.35, 95% CI = 0.27, 0.42) the evaluative training, p < .001. As in the left and right BA 46, there was an interaction betw een task and run in the bilateral BA 46, F (1, 1483) = 5.40, p < .05. Specifically, the post-training activation of the bilateral BA 46 increased for evaluative tasks ( M = 0.30, 95% CI = 0.22, 0.39, vs. M = 0.49, 95% CI = 0.41, 64

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0.58, prevs. posttraining run, p < .001), but not for non-evaluative tasks ( M = 0.39, 95% CI = 0.30, 0.48, vs. M = 0.45, 95% CI = 0.37, 0.54, prevs. posttraining run, p > .05). BA 9. Similarly, we found an interaction of run a nd task in another in ferior frontal area around 43, 3, 32 (BA 9; located in Maccotta & Buckner, 2004), F (1, 1475) = 4.13, p < .05. After training, activation in the right BA 9 increased more for evaluative tasks ( M = 0.34 vs. 0.58, prevs. posttraining run, p < .001), than non-evaluative tasks ( M = 0.44 vs. 0.56, prevs. posttraining run, p < .01). Also, activation in the left BA 9 (centered on 43, 3, 32) increased after the training run for both evaluative ( M = 0.38 vs. 0.62; pre-traini ng vs. post-training run, p < .001) and non-evaluative tasks ( M = 0.43 vs. 0.60; pre-traini ng vs. post-training run, p < .001). Similarly to the BA 46, the left and right BA 9 areas were combined to represent the activation change in bilateral BA 9 areas. Analyses revealed a main effect of run, F (1, 1199) = 56.72, p < .001, indicating more activity after ( M = 0.59, 95% CI = 0.50, 0.68) than before ( M = 0.39, 95% CI = 0.31, 0.48) th e evaluative training, p < .001. Unlike in the left and right BA 9, however, we found a marginal interaction between task and run in the bilateral BA 9, F (1, 1472) = 3.16, p = .08. Specifically, the post-training activati on of the bilateral BA 9 increased for evaluative tasks ( M = 0.36, 95% CI = 0.26, 0.45, vs. M = 0.60, 95% CI = 0.50, 0.69, prevs. posttraining run, p < .001) to a greater extent than for non-evaluative tasks ( M = 0.43, 95% CI = 0.34, 0.53, vs. M = 0.58, 95% CI = 0.48, 0.67, prevs. posttraining run, p < .001). Summary. Results in above brain regi ons previously associated with procedural learning showed that, when evaluations are proceduralized, activations in the striatal system (i.e., the caudate nucleus, the caudovent ral putamen, and the nucleus accumbens), the cerebellum, the precuneus, and the inferior fr ontal cortices, increase. More over, as predic ted, unlike the caudoventral putamen, the rostrodorsal putamen activation decrease d with evaluation 65

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proceduralization. Also, different ar eas of the inferior parietal l obe showed different patterns of signal change with evaluation pr oceduralization. A summ ary of findings in regions associated with procedural learning app ears in Table 3-7. Maps for brain activity changes for evaluative tasks in regions linked to procedural learning are include d in Figure 3-3. Regions associated with declarative learning. To test our hypothe ses that evaluation proceduralization is associated w ith decreased activity in brain re gions implicated in declarative learning, we centered the medial temporal lobe on coordinates 32, 15, 24 (Rose et al., 2004), the dorsolateral prefrontal cortex on 30, 55, 24 (Klingberg et al., 1997) and 48, 41, 9 (Taylor et al., 2003), and the ventromedi al prefrontal lobe on 22, 30, 16 (Lieberman et al., 2004) (see Table 3-6 for a list of information for each ROI). These regions were included in the cluster of declarative learning in the anal ysis at the cluster level. Medial temporal lobe (MTL). Analyses revealed a marginally significant main effect of run on the activation of the left medial temporal lobe area (located around 39, 15, 24; Rose et al., 2004), F (1, 1458) = 3.08, p = .08. That is, the MTL area wa s less active during the posttraining run ( M = 0.07, 95% CI = 0.17, 0.02) than the pre-training run ( M = 0.05, 95% CI = 0.05, 0.14). More interestingly, the decreased ac tivation was present fo r evaluative tasks ( M = 0.09, 95% CI = 0, 0.18, vs. M = 0.12, 95% CI = 0.21, 0.03; prevs. posttraining run, p = .002), but not for non-evaluative tasks ( M = 0.07, 95% CI = 0.07, 0.21, vs. M = 0.01, 95% CI = 0.12, 0.15; prevs. pos ttraining run, ns ), p > .10. Thus, in the pre-training run, the MTL was equally active for evaluative ( M = 0.09) and non-evaluative tasks ( M = 0.07), ns In contrast, in the post-training run, evaluative tasks ( M = 0.12) triggered lower activation in the MTL than non-evaluative tasks ( M = 0.01), p < .05. No effects of valence were found. 66

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Dorsolateral prefrontal cortex (dlPFC). We found a main effect of task (evaluative vs. non-evaluative tasks) on the dlPFC (located around 30, 55, 24; Klingberg et al., 1997), F (1, 1676) = 6.59, p = .01. The activation of dlPFC wa s higher for evaluative tasks ( M = 0.37, 95% CI = 0.01, 0.75) than for non-evaluative tasks ( M = 0.12, 95% CI = 0.27, 0.50). In addition, the interaction between task and run reached significance, F (1, 1673) = 3.77, p = .05. Specifically, the dlPFC activation for evaluative tasks decreased significantly from the pre( M = 0.55, 95% CI = 0.15, 0.95) to the posttraining run ( M = 0.20, 95% CI = 0.21, 0.60), p = .01. However, the activation at the dlPFC for non-evaluative tasks did not change ( M = 0.09, 95% CI = 0.32, 0.50, vs. M = 0.14, 95% CI = 0.27, 0.54, ns prevs. posttraining run). Furthermore, we found a main effect of r un on the right dlPFC area (centered on 48, 41, 9; Taylor et al., 2003), F (1, 1312) = 8.80, p < .01, indicating lower activ ation in the post-training ( M = 0.01, 95% CI = 0.06, 0.07) than in the pre-training ( M = 0.15, 95% CI = 0.08, 0.21) run. Moreover, this decreased activation was present for evaluative tasks ( M = 0.17, 95% CI = 0.08, 0.26, vs. M = 0.01, 95% CI = 0.10, 0.08; prevs. posttraining run, p < .01), but not for nonevaluative tasks ( M = 0.13, 95% CI = 0.03, 0.23, vs. M = 0.02, 95% CI = 0.07, 0.12; prevs. posttraining run, ns ), p > .10. No other main effects or interactions were found, F s < 1. Because the findings in the left and right dl PFC areas are similar, the signal changes in these two areas were combined to represent the activation change in the dlPFC region. Results revealed a main effect of run, F (1, 1581) = 6.43, p = .01. That is, the dlPFC activation was lower in the post-training run ( M = 0.10, 95% CI = 0.15, 0.34) than the pre-training run ( M = 0.24, 95% CI = 0.01, 0.48), p = .01. There was also a main effect of task on this region, F (1, 1635) = 5.51, p < .05. That is, across the runs, evaluative tasks (M = 0.23, 95% CI = 0.01, 0.47) triggered higher activation in the dl PFC than did non-evaluative tasks ( M = 0.10, 95% CI = 67

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0.14, 0.34). Moreover, we found an inte raction between task and run, F (1, 1630) = 4.68, p < .05. Specifically, the activation in the dlPFC for evaluative tasks decr eased significantly ( M = 0.37, 95% CI = 0.11, 0.62, vs. M = 0.10, 95% CI = 0.15, 0.35, pre-training vs. post-training run), p < .001. In contrast, the dlPFC activation for non-evaluative tasks did not change ( M = 0.11, 95% CI = 0.15, 0.36, vs. M = 0.09, 95% CI = 0.16, 0.34, pre-training vs. post-training run), ns Ventromedial prefrontal cortex (vmPFC). There was a main effect of task on the left vmPFC area (located around 22, 30, 16; Lieberman et al., 2004), F (1, 1656) = 7.32, p < .01. That is, the left vmPFC was more active for evaluative ( M = 0.09, 95% CI = 0.09, 0.27) than for non-evaluative ( M = 0.27, 95% CI = 0.45, 0.08) tasks. In addition, there was an interaction between run and task, F (1, 1660) = 4.88, p < .05. Specifically, the activation in the vmPFC for evaluative tasks d ecreased from the pre( M = 0.32, 95% CI = 0.08, 0.57) to the posttraining run ( M = 0.14, 95% CI = 0.40, 0.12), p = .01, whereas the activation for nonevaluative tasks did not change ( M = 0.33, 95% CI = 0.60, 0.06, vs. M = 0.21, 95% CI = 0.46, 0.05, pre-training vs. post-training, ns ). In addition, when looking at the activation pattern within each run, we found that before the ev aluation training run, the left vmPFC was more active for evaluative ( M = 0.32) than non-evaluative tasks ( M = 0.33) tasks, p < .001. However, there was no between-task diffe rence after the training run ( M = 0.14 vs. 0.21, evaluative vs. non-evaluative tasks), ns No other main effects or interactions were found, F s < 1. Summary. The above results in regions previously associated with declarative learning indicate that evaluation proceduralization reduces brain activities in th ese regions. Specifically, when evaluations were proceduralized, brain ac tivities in the medial temporal lobe, the dorsolateral prefrontal cortex, and the ventromedial prefront al cortex decreased to or 68

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significantly below the baseline activation. A summary of findings in regions associated with declarative learning appears in Table 3-8. Maps for brain activity changes fo r evaluative tasks in regions linked to declarative lear ning are included in Figure 3-3. Findings in Regions Associated with Evaluation To test the effects of practic e with evaluations in regions associated with evaluative processing, we obtained coordinates of ROIs from different sources. First, the bilateral amygdala was located using a computer-generated image based on the Talarirach-defined coordinates (Norris et al., 2004). Specificall y, regions capturing the right and left amygdala ROIs extended from (left) to (right) in the x -plane; from 2 (posterior) to 12 (anterior) in the y -plane; and from 37 (inferior) to 7 (superior) in the z -plane. Moreover, several regions associated with evaluative processing of images were taken direct ly from previous research. That is, voxels within 5mm of the temp oral pole (BA 38; 42, 8, 30) and the frontal operculum (-46, 14, 6) were used to define the regions of interest (Lane et al., 1997). In addition, we located the anterior cingulate cortex around 4, 24, 32 (Cunningham et al., 2004), the insula around 30, 23, 7 (located in an independent study, N = 8), and the orbito-fr ontal cortex around 45, 35, 4 (BA47; Maccotta & Buckner, 2004) (see Table 3-6 for a list of coordinates of ROIs related to evaluation). We expected that ev aluation proceduralization would either increase or decrease activities in these evaluation-related brain re gions. Moreover, evalua tion proceduralization should produce comparable changes in regions as sociated with both automatic and controlled evaluation. Regions previously linked to automa tic (controlled) evaluati on were included in the cluster of automatic (controlled) evaluation in the cluster analys is of regional change with evaluation proceduralization. Amygdala. We found a significant between-run differe nce in the activation of the left amygdala area, F (1, 1330) = 9.82, p = .002. Specifically, the left amygdala was more active 69

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during the post-training run (M = 0.32, 95% CI = 0.21, 0.43) than the pre-training run ( M = 0.22, 95% CI = 0.11, 0.34). This increased activation of the left amygdala was present for both evaluative ( M = 0.22, 95% CI = 0.10, 0.34, vs. M = 0.30, 95% CI = 0.18, 0.42; prevs. posttraining run, p = .09) and non-evaluative ( M = 0.23, 95% CI = 0.11, 0.35, vs. M = 0.35, 95% CI = 0.23, 0.47; prevs. posttraining run, p < .01) tasks. Moreover, we found a significant main effect of valence in the act ivation of the left amygdala, F (1, 1577) = 4.27, p < .05. That is, the left amygdala had the tendency to be more active for pleasant images ( M = 0.31, 95% CI = 0.19, 0.42) than unpleasant images ( M = 0.25, 95% CI = 0.13, 0.36), F (1, 1579) = 3.29, p = .07. Interestingly, there was a marginal interaction between run and valence, F (1, 1553) = 3.01, p = .08. After the training ru n, the activation of the left amygdala for pleasant images increased ( M = 0.23, 95% CI = 0.11, 0.35, vs. M = 0.38, 95% CI = 0.26, 0.51, p < .001), whereas the activation for unpl easant images did not change (M = 0.22, 95% CI = 0.11, 0.34, vs. M = 0.27, 95% CI = 0.15, 0.39, ns ). In addition, in the pre-training run, there was no difference in the activit y of the left amygdala for pleasant ( M = 0.23) and unpleasant images ( M = 0.22), ns However, in the post-training run, the amygdala was significantly more active for pleasant ( M = 0.38) than for unpleasant images ( M = 0.27), p < .05. Thus, we can conclude that, over time, the more emotional images participants viewed, the more the left amygdala was active, regardless of the explicit task part icipants performed. In addition, consistent with previous fi ndings (e.g., Cunningham et al., 2003), the left amygdala was equally significantly active (vs. null trials) for both types of task in both the pretraining run ( M = 0.22 vs. 0.23, evaluative vs. non-evaluative tasks, ns ) and the post-training run ( M = 0.30 vs. 0.35; evaluative vs. non-evaluative tasks, ns ). Active amygdala function in 70

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response to emotional images regardless of explic it task instructions and run supports the idea that the amygdala is involved in the automatic aspect of evaluation. Interestingly, the pattern of activity change in the left amygdala was not present in the right amygdala. Specifically, although th ere was a significant main effect of run on the right amygdala, F (1, 1386) = 8.15, p < .01, this effect was present only for non-evaluative tasks and comprised an increase in activity ( M = 0.24, 95% CI = 0.11, 0.38, vs. M = 0.39, 95% CI = 0.25, 0.52, prevs. posttraining run, p < .01). In contrast, for evalua tive tasks, run did not influence activity of the right amygdala ( M = 0.27, 95% CI = 0.13, 0.40, vs. M = 0.32, 95% CI = 0.18, 0.45, prevs. posttraining run, ns ). No other main effects or interactions were found, F s < 1. Insula. Like the left amygdala area, the left insula (BA 13; 30, 23, 7) was more active during the post-training th an the pre-training run, Mdiff = 1.08, F (1, 1655) = 109.55, p < .001. This pattern was found for both evaluative ( M = 0.34, 95% CI = 0.52, 1.19, vs. M = 1.38, 95% CI = 0.53, 2.24; pre-training vs. post-training, p < .001) and non-evaluative tasks ( M = 0.30, 95% CI = 0.55, 1.16, vs. M = 1.42, 95% CI = 0.57, 2.28; pretraining vs. post-training, p < .001). Moreover, in the pre-training run, the insula was not active (vs. nu ll trials) for either evaluative or non-evaluative tasks. Furthermore, the insu la was equally active for evaluative and nonevaluative tasks in the pr e-training as well as the post-training run, both t s < 1. Therefore, in the current study, the insula was appa rently involved in automatic evaluation, as its activation was not influenced by task instruc tions in either the pre-trai ning or the post-training run. Orbito-frontal cortex (OFC). After the evaluative training, the activation in the right lateral orbito-frontal regi on (BA 47; located around 45, 35, 4; Maccotta & Buckner, 2004) increased significantly, Mdiff = 0.53, F (1, 1655) = 40.95, p < .001. The increased activation was present for both evaluative tasks ( M = 0.06, 95% CI = 0.45, 0.33, vs. M = 0.43, 95% CI = 71

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0.04, 0.82, prevs. posttraining, p < .001) and non-evaluative tasks ( M = 0.07, 95% CI = 0.46, 0.33, vs. M = 0.49, 95% CI = 0.10, 0.89, pr evs. posttraining, p < .001). Moreover, the right OFC region was equally activ e for evaluative and non-evaluati ve tasks in the pre-training (pairwise comparison, ns ) and the post-training run (pairwise comparison, ns ). No other main effects or interactions were found, F s < 1. As the pattern of activ ation change in the OFC was identical to the ones in the amygda la and the insula, we can concl ude that, as expected, the OFC was involved in automatic evaluation. Anterior cingulate cortex. There was a marginal interacti on between run and task in the left dorsal ACC area (BA 32; centered on 4, 24, 32; Cunningham et al., 2004), F (1, 1491) = 3.53, p = .06. Specifically, for evaluativ e tasks, the activity of th e ACC was higher in the posttraining run ( M = 0.09, 95% CI = 0.02, 0.16) than in the pre-training run ( M = 0.02, 95% CI = 0.05, 0.09), p = .01. However, for non-evaluative tasks, the ACC activation did not change across runs ( M = 0.06, 95% CI = 0.01, 0.13, vs. M = 0.07, 95% CI = 0, 0.14; prevs. posttraining run respectively, ns ). In addition, there were no betw een-task differences in the ACC activation in either the pre-training run (M = 0.02 vs. 0.06, for evaluative and non-evaluative tasks, ns ) or the posttraining run ( M = 0.09 vs. 0.07, for evaluative and non-evaluative tasks, ns ). No other effects were found in this region, F s < 1. In sum, as predicted, the activation of the ACC only increased for the trained evaluative task s, not for the untrained non-evaluative tasks. Frontal operculum. We centered the frontal operculum area on 46, 14, 6 (Lane et al., 1997), and found an increased activation in the BOLD signal across runs. That is, this region was more active in the post-training run ( M = 0.43, 95% CI = 0.31, 0.54) than in the pre-training run ( M = 0.34, 95% CI = 0.23, 0.45), F (1, 1271) = 4.33, p < .05. The increased activation in the post-training (vs. pre-training) r un was present for evaluative tasks ( M = 0.42, 95% CI = 0.30, 72

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0.55, vs. M = 0.28, 95% CI = 0.16, 0.41; p < .05), but not for non-evaluative tasks ( M = 0.43, 95% CI = 0.30, 0.56, vs. M = 0.39, 95% CI = 0.26, 0.52; ns ), p > .15. Further analysis indicated that, the left frontal opercul um was marginally more ac tive for non-evaluative tasks ( M = 0.39, 95% CI = 0.26, 0.52) than for evaluative tasks ( M = 0.28, 95% CI = 0.16, 0.41) in the pretraining run, p = .08, with no task differences ( M = 0.43 vs. 0.43, evaluative vs. non-evaluative tasks) in the post-training run, ns In addition, there was a significan t interaction between task and valence in the left frontal operculum area, F (1, 1554) = 5.94, p < .05. Specifically, during evaluative tasks, the average BOLD signal of the left frontal operculum area was 0.29 (95% CI = 0.16, 0.41) for unpleasant images and 0.41 (95% CI = 0.28, 0.53) for pleasant images, p = .05. However, when tasks were non-evaluative, there was no difference between unpleasant ( M = 0.46, 95% CI = 0.33, 0.59) and pleasant ( M = 0.36, 95% CI = 0.23, 0.49) images, ns Temporal pole. The right temporal pole (centered on 42, 8, 30; Lane et al., 1997) was more active during the post-training run ( M = 0.28, 95% CI = 0.17, 0.39) than during the pretraining run ( M = 0.16, 95% CI = 0.05, 0.27), F (1, 1296) = 13.47, p < .001. Moreover, there was an interaction between run and task in this area, F (1, 1523) = 4.28, p < .05. Specifically, the activation in the temporal pole during evaluative tasks was high er in the post-training run ( M = 0.31, 95% CI = 0.19, 0.43) than in the pre-training run (M = 0.12, 95% CI = 0.01, 0.24), p < .001. However, there was no between-run differen ce in the temporal pole activation during nonevaluative tasks ( M = 0.20, 95% CI = 0.08, 0.32, vs. M = 0.25, 95% CI = 0.13, 0.37; pre-training and post-training run respectively, ns ) This difference in temporal pole activation change for evaluative and non-evaluative tasks confirmed that the temporal po le is involved in controlled 73

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evaluation. Again, there was no between-task difference in the right temporal pole activation in either the preor the post-training runs, ns Summary. Although activities in the evaluative processing ROIs all increased after evaluation training, there are seve ral differences in the change patterns. For example, regions previously associated with automatic evaluati on (i.e., the amygdala, the OFC, and the insula) were more active in the post-training (vs. th e pre-training) run for both evaluative and nonevaluative tasks. In contrast, af ter the evaluation training, regions previously associated with controlled evaluation (i.e., the anterior cingulate cortex, the temporal pole, and the frontal operculum) were more active for evaluative tasks, but not for non-evaluative tasks. Nonetheless, evaluation training enhanced the activation duri ng evaluative tasks in various evaluative processing regions. A summary of findings in regions associated with evaluation appears in Table 3-9. Maps for brain activity changes for eval uative tasks in regions linked to evaluation are included in Figure 3-4. Findings in Visual Areas For visual processing regions, we focused on bot h early and late visual regions (see Table 3-6 for a summary of ROI coordinates). Specifica lly, the early visual region was selected around 17, 73, 17 (calcarine, Maccotta & Buckner, 2004). Mo reover, in current study, late visual regions included the posterior fusiform around 36, 73, 13 (Maccotta & Buckner, 2004), the superior occipital lobe (32, 74, 29; Garavan et al., 1999), and th e parietal occipital lobe (44, 70, 20; Lane et al., 1997). Proceduralization was e xpected to produce no change in activation in the early visual region but increases in the late visual regions. These regions were included in the cluster of visual processing in the cluster analysis of brain activity change. 74

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Calcarine. As predicted, we did not find any effects of eval uative skill training on the calcarine area (centered on 17, 93, 17; Maccotta & Buckner, 2004), F < 1. In addition, this region showed no effects of type of tasks, or image valence, F s < 1. Posterior fusiform. There was a main effect of run in the late visual region near the left posterior fusiform gyrus (BA 18/19; 36, 73, 13; Maccotta & Buckner, 2004), F (1, 1227) = 13.70, p < .001. The means corresponding to this main effect indicated greater activation in the left posterior fusiform gyru s in the post-training run ( M = 1.15, 95% CI = 1.01, 1.30) than in the pre-training run ( M = 0.98, 95% CI = 0.84, 1.12), p < .001. There was also a marginal interaction between run and task in this area, F (1, 1486) = 3.05, p = .08. For evaluative tasks, the left posterior fusiform was more activ e after than before training ( M = 0.90, 95% CI = 0.74, 1.05, vs. M = 1.16, 95% CI = 1.00, 1.32; pre-tr aining vs. post-training run), p < .01. However, there was no between-run difference in activation for non-evaluative tasks ( M = 1.06, 95% CI = 0.90, 1.22, vs. M = 1.15, 95% CI = 0.99, 1.30; pretraining vs. post-training run, ns ). No effects of image valence were found, F < 1. Parietal-occipital cortex. We also tested whether traini ng in evaluation changed the brain activity at the parietal-occ ipital cortex (BA 39), BOLD signals within 5mm around 44, 70, 20 (Lane et al., 1997). Results showed a significant main effect of run on this late visual area, F (1, 1268) = 64.31, p < .001. That is, the parietal-occipital cortex was more active in the post-training run ( M = 0.46, 95% CI = 0.40, 0.57) than in the pre-training run ( M = 0.18, 95% CI = 0.07, 0.28). After training in evaluation, the activation in the right pari etal-occipital region increased for evaluative tasks ( M = 0.15, 95% CI = 0.04, 0.27, vs. M = 0.47, 95% CI = 0.35, 0.58, pretraining vs. post-training run, pairwise comparison, p < .001), as well as for non-evaluative tasks 75

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( M = 0.20, 95% CI = 0.09, 0.32, vs. M = 0.46, 95% CI = 0.34, 0.57, pretraining vs. post-training run, pairwise comparison, p < .001). No other effects were found, F s < 1. Superior occipital gyrus. For the brain activity in the s uperior occipital gyrus (located around 32, 74, 29; Garavan et al., 1999), analyses s howed a significant ma in effect of run, F (1, 1142) = 25.44, p < .001, and a significant intera ction between task and run, F (1, 1479) = 5.41, p = .02. Generally, the right superior occipital gy rus was more active during the post-training run ( M = 0.54, 95% CI = 0.43, 0.65) than the pre-training run ( M = 0.37, 95% CI = 0.26, 0.48), p < .001. Moreover, post training activation in th is area increased for evaluative tasks ( M = 0.31, 95% CI = 0.19, 0.43, vs. M = 0.57, 95% CI = 0.45, 0.69; pretraining vs. post-training, p < .001), but not for non-evaluative tasks ( M = 0.43, 95% CI = 0.30, 0.55 vs. M = 0.52, 95% CI = 0.39, 0.64, pre-training vs. post-training run, p > .05). Summary. In sum, training in evaluative tasks di d not have an effect on early visual regions such as the calcarine. However, late visual regions, such as the posterior fusiform and the superior occipital lobes showed greater activ ity increases for evaluative tasks than nonevaluative tasks. In addition, there was increased activation in the pariet al occipital gyrus for both types of tasks. A summary of findings in th e visual areas appears in Table 3-10. Maps for brain activity changes in visual processing region s for evaluative tasks are included in Figure 35. Findings in Motor Areas Motor cortex. To examine whether training in evaluati ve skills influenced the activity in motor areas, the hand response execution region (BA 4) in the left motor cortex was selected around 37, 25, 50 (Maccotta & Buckner, 2004). Results revealed a significant main effect of training on the left motor cortex. That is, overall, activity in the left motor cortex decreased in the post-training run ( M = 0.50, 95% CI = 0.41, 0.60) relati ve to the pre-training run ( M = 0.61, 95% 76

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CI = 0.51, 0.70), F (1, 1156) = 8.15, p < .01. Specifically, this decrease was found in nonevaluative tasks ( M = 0.65, 95% CI = 0.54, 0.77, vs. M = 0.50, 95% CI = 0.39, 0.60, pre-training vs. post-training, pairwise comparison, p < .01), but not in evaluative tasks (M = 0.56, 95% CI = 0.46, 0.67, vs. M = 0.51, 95% CI = 0.40, 0.62; pre-trai ning vs. post-training, pairwise comparison, ns ), F (1, 1503) = 1.94, p > .15. Moreover, before the training run, non-evaluative tasks ( M = 0.65) tended to trigger higher activation in the motor cort ex than did evaluative tasks ( M = 0.56), p < .10. In contrast, there was no between-task difference in the motor cortex activation after the training run ( M = 0.51 vs. 0.50, evaluative vs. non-evaluative tasks, ns ). More interestingly, there was a 3-way interacti on between run, task, and valence in the left motor cortex, F (1, 1543) = 5.07, p < .05. Specifically, after training, activity in the left motor cortex decreased significan tly for pleasant images ( M = 0.61 vs. 0.44; prevs posttraining run, p < .05), but did not change for unpleasant images ( M = 0.51 vs. 0.57; prevs. posttraining run, ns ), F (1, 730) = 3.95, p < .05. However, the pattern of brain ac tivity in the left motor cortex was different when the tasks were non-evaluative. Compared to the activation during the pre-training run, the left motor cortex was less active in the post-training run for both pleasant ( M = 0.65 vs. 0.49; prevs. posttraining run, p = .05) and unpleasant images (M = 0.66 vs. 0.50; prevs. posttraining run, p < .05). Therefore, as predicted, overall, evaluati on proceduralization had no effects on the activation in the motor cortex (BA 4, the hand region) for evaluative tasks. However, we observed a decreased activation in the BA 4 region for evaluative ta sks of pleasant images (vs. unpleasant images). A summary of these findings appears in Table 3-10. Maps for brain activity changes in the motor cortex for evalua tive tasks are included in Figure 3-5. 77

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Correlations between Behavioral Performance a nd Brain Activities in Regions Associated with Learning and Evaluation ROIs activation change. It was also important to s how that BOLD signal changes correlated with our behavioral measures. For each ROI, we computed a regional-activity-change score by taking the difference between BOLD sign als for evaluative tasks in the post-training and the pre-training run. Thus, a positive regiona l activation change indi cates higher regional activity in the post-training (vs. pre-training) run, whereas a ne gative regional change indicates lower regional activity after training in eval uative skills. Then, we conducted correlational analyses of activity change s in each ROI and task response time (see Appendix B). Response time and ROIs. To examine association be tween brain activity and our behavioral data, we grouped regi ons with significant training effects for evaluative tasks. The cluster of regions previously lin ked to procedural learning include d the striatal regions (i.e., the nucleus accumbens, the bilate ral caudate nucleus, and the put amen areas), bilateral BA 46, bilateral BA 9, the cerebellum, and the precuneus ( = 0.72), whereas the cluster of regions previously linked to declarative learning include d the medial temporal lobe, the dorsolateral prefrontal cortices, and the vent romedial prefrontal cortex ( = 0.69). Furthermore, the cluster of regions involved in automatic evaluation included the amygdala, the orbito-frontal cortex, and the insula ( = 0.70), whereas the cluster of regions involved in controlled evaluation consisted of the temporal pole, the anterior cing ulate cortex, and the frontal operculum ( = 0.86). Last, a cluster of regions previously a ssociated with late visual processing included the posterior fusiform, the superior occipital gyrus, and the parietal-occipital cortex ( = 0.82). Using these clusters, we next examined the e ffect of evaluation trai ning on brain activation changes for evaluative tasks by using the earlier mixed model of run (pre-training vs. posttraining) with time linear trend and quadratic trend as covariates. Resu lts showed significant 78

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main effects of run in each cluster, p s < .004 (Bonferroni corr ection was applied, the level was set at .01 as there are five clusters in this analysis). Specifically, activations for evaluative tasks increased in the procedural-learning cluster ( M = 0.32, 95% CI = 0.22, 0.41, vs. M = 0.56, 95% CI = 0.46, 0.66; pre-training vs. post-training run), F (1, 582) = 54.02, p < .001, the automaticevaluation cluster ( M = 0.14, 95% CI = 0.25, 0.54, vs. M = 0.68, 95% CI = 0.28, 1.08; pretraining vs. post-training run), F (1, 821) = 47.02, p < .001, the controlledevaluation cluster ( M = 0.20, 95% CI = 0.10, 0.29, vs. M = 0.31, 95% CI = 0.21, 0.41; pre-training vs. post-training run), F (1, 605) = 8.44, p < .004, and the late-visual cluster ( M = 0.44, 95% CI = 0.35, 0.54, vs. M = 0.73, 95% CI = 0.63, 0.82; pre-tr aining vs. post-training run, p < .001). At the same time, the activation in the declarativ e learning cluster decreased to baseline following training (M = 0.24, 95% CI = 0.07, 0.40, vs. M = 0.07, 95% CI = 0.23, 0.10; pre-training vs. post-training run), F (1, 791) = 14.02, p < .001. Practice-related ac tivation changes for eval uative tasks of each cluster are shown in Figure 3-2. Analysis of these regional clusters indicated that the change in activation of regions of controlled evaluation from the preto the posttraining run positively correlated with the change in the procedural learning regions ( r = 0.78, p < .001), the late visual regions (r = 0.57, p < .05), and the motor cortex (r = 0.60, p < .05). In addition to the activ ation changes in controlled evaluation regions, activation change in the motor cortex also corre lated with the changes in late visual regions ( r = 0.64, p < .05) and procedural learning regions ( r = 0.58, p < .05). The activation change in procedural learning regions was also margina lly correlated with the change in late visual regions, r = 0.48, p < .10. No other significant co rrelations between cluster activation changes (prevs. posttraining run) were found, ns Correlation coefficients between activation changes in each clus ter appear in Table 3-11. 79

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We next examined the relation between effects of training on re gional activity and behavioral response time by regressing behavior al practice effects for evaluative tasks on activation changes for evaluative tasks. Behavioral practice effect s were computed by subtracting response time for evaluative tasks in the post-training run from th e response time for evaluative tasks in the pre-training run. Ther efore, larger differences in response time indicate faster response time in the post-tra ining (vs. pre-training) r un and are an indication of proceduralization. Results of the regression analysis revealed that this score correlated positively with activation change in the automatic evaluation regions (Beta = 0.43, t = 4.83, p < .01), the controlled evaluation regions ( Beta = 0.52, t = 3.87, p < .01), and the declarative learning regions ( Beta = 0.61, t = 6.66, p < .001). Moreover, this score corre lated negatively with activation change in the procedural learning regions ( Beta = 0.49, t = 3.58, p < .05), the motor cortex (BA 4, Beta = 0.32, t = 2.81, p < .05), and the early visual region (the calcarine, Beta = 0.35, t = 3.03, p < .05). No significant relati on between the change in re sponse time and the change in brain activation at the la te visual regions was found, Beta = 0.21, t = 1.76, p > .10. The regression model was significant, R square = 0.96, F (7, 13) = 22.22, p < .001. Omit rate and ROIs. Similar procedures were applie d to examine the relation between changes in omit rate across runs and changes in brain activity. Specifically, the change in omit rate was computed by subtracting the omit rate in the post-training run from the one in the pretraining run. Therefore, larger differences in omit rate indicate improved behavioral performance. Then, we correlated changes in brain activity and cha nges in omit rate (see Appendix B). The change in omit rate from the preto the posttraining run was positively correlated with the change in BA 4 activation, r = 0.51, p = .07. That is, increased activation in 80

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BA 4 across runs contributed to greater omit rate reduction for evaluative tasks. No other significant relations were found. Discussion Experiment 2 was conducted to explore the underlying brain func tional processes of evaluation proceduralization observed in Experime nt 1. As in Experiment 1, at the behavioral level, participants responded to evaluative ta sks more quickly after than before receiving training. In contrast, with no training in non-evaluative task s, the response times to nonevaluative tasks did not change over runs. Moreover, the omit rate decreased for both evaluative and non-evaluative tasks after the training in evaluative skills. In addition to the response times to tasks, we observed changes in the pattern of how participants responded to different types of ta sk (evaluative vs. frequency-related tasks). Specifically, unpleasant images presented in the post -training run were rated as less frequently seen on TV than the unpleasant images pres ented in the pre-training run. Moreover, the pleasantness ratings were more polarized after than before the evaluation tr aining run. That is, in the post-training run, pleasant images were rated as more pleasant and unpleasant images as more unpleasant than comparable images in the pre-training run. Th is finding is consistent with previous research on attitude polarization in which repeated attitude expressions lead to attitude extremity (Brauer, Judd, & Gliner, 1995). Importan tly, the current research indicated that the increased attitude extremity can also occur with practiced expressions of evaluations of different targets. Furthermore, a distribution test indi cated that practice with evaluative judgments polarized pleasantness ratings to images but no t the pattern of selection of response buttons during evaluative tasks. In contra st, the pattern of response sel ection for untrained non-evaluative tasks did change. 81

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At the brain level, evaluation proceduralization was reflected by increased activation in the striatal system and other pro cedural learning regions (i.e., th e cerebellum, the precuneus, and inferior frontal corti ces) during evaluative tasks after eval uation training. The increased regional activity in striatal areas (e .g., the bilateral caudate nucleus the putamen, and the nucleus accumbens) suggests that the striatal system is involved in proceduralization not only of cognitive tasks, but also of evaluative tasks. With in the striatal system, we also detected specific functions of sub-putamen areas in evaluation proceduralization. That is, consistent with previous findings (Lehericy et al., 2005), post training activation increa sed in the caudoventral putamen but decreased in the rostrodorsal putamen. Also, there were signifi cant decreases in activation in several regions previously linked to declarative learning, such as the medial temporal pole and the prefrontal cortices (e.g., the dorsolateral prefrontal cortex, a nd the ventromedial prefrontal cortex). The finding that activity in the medial temporal lobe declined as activ ity in fronto-striatal regions increased is consistent with Seger a nd Cincottas (2006) re search on explicit rule learning. In addition, the increased activation in the inferior front al regions along with decreased activation in the prefrontal regi ons suggests the facil itation of frontally mediated processes in evaluation proceduralization. Moreov er, the findings in the posterio r parietal lobe offer further evidence indicating that the superi or parietal lobe is involved in procedur al learning (see also Nadel et al., 2007). However, the fi ndings in the inferior parietal lobe were not congruent. The right inferior parietal became less active with practice (see also Klingberg et al., 1997), whereas the left inferior parietal became more active with practice. Moreover, changes in these two inferior parietal areas did not co rrelate with any other regional activation change. More research 82

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needs to be conducted to address directly the ro le of inferior pariet al lobe in evaluative procedural learning. In conclusion, the current research demonstrat ed that proceduraliza tion of evaluation was accompanied by decreases in the involvement of the declarative learning regions, along with increases in the involvement of the procedural learning regions. These changes in turn suggest that after being proceduralized in the practice run, the evalua tion processes were transformed from declarative memory to procedural memo ry. Specifically, converging evidence supports the notion that the functional brain network for im plicit learning includes the basal ganglia, the cerebellum, the superior pariet al lobe, and the prefrontal co rtices, whereas the network for explicit learning includes the medial temporal pole and the inferior frontal cortices. Therefore, our study supports the well-know n independence of brain correlates of declarative and procedural learning (see also Willingham et al., 2002). Another important finding was that procedur alization affected brain areas related to evaluation. Specifically, we found si gnificant increases for evaluative tasks in the activation in evaluative processing regions (i.e., the amygdala, the insula, the OFC, the ACC, the temporal pole, and the frontal operculum). This finding su ggests that practice with evaluative judgments increases instead of decreases brain activation of task-specific regions (Grossman, Blake, & Kim, 2004). Among these evaluative processing regi ons, there are several unique patterns in each region. First, while evaluative judgments are proceduralized, the activity in the left amygdala increased whereas in the activity in the right amygdala re mained unaltered. This finding is consistent with previous findings of dissociable function of bilateral amygdala in emotional memory and the involvement of the left amygdala in emotional memory retrieval (Sergerire, Lepage, & Armony, 2006). Interestingly, the increased activation of the left amygdala 83

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was found for positive but not for negative targ ets. One might relate this finding to a developmental view of evaluative-skill learni ng. For example, Mather and colleagues (2004) found that viewing positive pictures led to gr eater amygdala activation than viewing negative pictures for adults over 70 year s but not for adults under 30 years. Therefore, with age/practice, the amygdala may show decrease d reactivity to negative info rmation along with increased reactivity to positive information. Second, although all the evaluative processing regions were more active in the post-tr aining than in the pre-training run, their post-training activation levels differed as a function of task instructions. That is, activations in regions previously associated with automatic evaluation (i.e ., the amygdala, the insula, and the OFC) increased for both evaluative and non-evaluative tasks. In contrast, activation in regions asso ciated with controlled evaluation (i.e., the ACC, the temporal pole, and the frontal operculum) only increased for trained evaluative tasks. Furthermore, the findi ng of increased amygdala activity and decreased vmPFC activity when evaluations are proceduralized is consistent with reports of the top-down inhibitory effect of the vmPFC on the amygdala (Urry et al., 2006). Moreover, similar to findings from previous research (e.g., Maccotta & Buckner, 2004), we found no significant training effects of evaluative judgments on the motor cortex (e.g., the hand response execution region) or the early visual cortex (e.g., the calcarine). Therefore, practice with evaluative judgments did not alter brain activity in regions associated with basic motor and visual functions. In contrast, we obser ved increased activation of late visual regions (e.g., the superior occipital corte x, the posterior fusiform, and the parietal occipital cortex) for previously trained evaluative tasks. This finding also supports the hypothesis that practice increases activation in task-related regions, incl uding late visual areas when the stimuli are visual. 84

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In sum, practice with evaluation produced bot h increases and decreases in brain activity. As summarized by Kelly and Garavan (2005), de creased activation during task practice is usually a result of a reduced neural representation of the stimulus or a more efficient firing of a more precise functional circuit. In contrast, increased activat ion during task practice often indicates recruitment of additional cortical units resulting in increas ed spatial extent of activation or a strengthened response within a region. As different regions engaged in evaluative task performance before and after practice, we can co nclude that practice in evaluative judgments reorganizes functional activations in several brain regions. Specifi cally, before practice in evaluation, active regions relate d to procedural learning, decl arative learning, controlled evaluation, and late visual pro cessing. After practice in evaluation, however, regions associated with declarative learning were no longer active, whereas regions related to automatic evaluation became involved. 85

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Table 3-1. IAPS picture codes, va lence and arousal ratings for images presented in the pretraining run (Experiment 2) Task Valence set Code Description Valence rating Arousal rating Evaluative Negative 5731 Flowers 5.39 (1.58) 2.74 (1.95) 9471 Burnt bull dog 3.16 (1.35) 4.48 (2.02) 9040 Starving child 1.67 (1.07) 5.82 (2.15) 7036 Shipyard 4.88 (1.08) 3.32 (2.04) 1113 Snake 3.81 (1.75) 6.06 (2.12) 9472 Bridge 4.07 (1.34) 4.16 (2.00) 7035 Mug 4.98 (0.96) 2.66 (1.82) 9700 Trash 4.77 (1.24) 3.21 (1.92) 9140 Cow 2.19 (1.37) 5.38 (2.19) 2810 Boy 4.31 (1.65) 4.47 (1.92) 3170 Baby tumor 1.46 (1.01) 7.21 (1.99) 6560 Attack 2.16 (1.41) 6.53 (2.42) 5535 Still life 4.81 (1.52) 4.11 (2.31) 1303 Dog 4.68 (2.11) 5.70 (2.04) 2393 Factory worker 4.87 (1.06) 2.93 (1.88) Positive 4220 Erotic female 6.60 (1.72) 5.18 (2.33) 7430 Candy 7.11 (1.78) 4.72 (2.29) 5811 Flowers 7.23 (1.60) 3.30 (2.33) 8130 Pole vaulter 6.58 (1.34) 5.49 (2.07) 1620 Sprg bok 7.37 (1.56) 3.54 (2.34) 5849 Flowers 6.65 (1.93) 4.89 (2.43) 4000 Artist 4.82 (1.66) 3.97 (2.15) 4003 Erotic female 5.48 (2.05) 5.09 (2.07) 8040 Diver 6.64 (1.56) 5.61 (2.01) 1601 Giraffes 6.86 (1.51) 3.92 (2.07) 7500 Building 5.33 (1.44) 3.26 (2.18) 1670 Cow 5.82 (1.63) 3.33 (1.98) 8330 Winner 6.65 (1.39) 4.06 (2.28) 7620 Jet 5.78 (1.72) 4.92 (2.11) 5220 Nature 7.01 (1.50) 3.91 (2.27) Nonevaluative Negative 4536 Attractive man 6.01 (1.49) 3.95 (2.30) 9594 Injection 3.76 (1.70) 5.17 (2.17) 9342 Pollution 2.85 (1.41) 4.49 (1.88) 6230 Aimed gun 2.37 (1.57) 7.35 (2.01) 2795 Boy 3.92 (1.77) 4.70 (2.00) 1321 Bear 4.32 (1.87) 6.64 (1.89) 6550 Attack 2.73 (2.38) 7.09 (1.98) 2200 Neutral face 4.79 (1.38) 3.18 (2.17) 6242 Gang 2.69 (1.59) 5.43 (2.36) 9913 Truck 4.38 (1.89) 4.42 (2.14) 7161 Pole 4.98 (1.02) 2.98 (1.99) 86

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87 Table 3-1. Continued Task Valence set Code Description Valence rating Arousal rating Nonevaluative Negative 9101 Cocaine 3.62 (1.96) 4.02 (2.33) 8010 Runner 4.38 (1.86) 4.12 (2.08) 9390 Dishes 3.67 (1.58) 4.14 (2.52) 2745.2 Shoplifter 3.91 (2.00) 5.17 (2.14) Positive 8280 Diver 6.38 (1.46) 5.05 (2.18) 2331 Chef 7.24 (1.72) 4.30 (2.38) 4640 Romance 7.18 (1.97) 5.52 (2.28) 8041 Diver 6.65 (1.67) 5.49 (2.29) 7350 Pizza 7.10 (1.98) 4.97 (2.44) 2320 Girl 6.17 (1.51) 2.90 (1.89) 8031 Skier 6.76 (1.39) 5.58 (2.24) 4279 Erotic female 5.47 (2.04) 4.38 (2.61) 2600 Beer 5.84 (1.85) 4.16 (1.74) 2352 Kiss 6.94 (1.87) 4.99 (1.98) 7289 Food 6.32 (2.00) 5.14 (2.51) 7481 Food 6.53 (1.78) 4.92 (2.13) 1947 Octopus 5.85 (1.77) 4.35 (2.37) 2375.2 Attractive female 6.34 (1.54) 4.30 (2.29) 2394 Medical worker 5.76 (1.47) 3.89 (2.26)

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Table 3-2. IAPS picture codes, desc ription, valence, and arousal ra tings of images presented in the training run (Experiment 2) Block Valence set Code Description Valence rating Arousal rating One Negative 2205 Hospital 1.95 (1.58) 4.53 (2.23) 2221 Judge 4.39 (1.21) 3.07 (2.08) 9230 Oil fire 3.89 (1.58) 5.77 (2.36) 9050 Plane crash 2.43 (1.61) 6.36 (1.97) 9800 Skin head 2.04 (1.57) 6.05 (2.71) 2215 Neutral man 4.63 (1.24) 3.38 (2.00) 1051 Snake 3.80 (1.75) 5.95 (1.98) 7595 Traffic 4.55 (1.46) 3.77 (2.22) Positive 8116 Football 6.82 (1.77) 5.97 (2.29) 2080 Babies 8.09 (1.47) 4.70 (2.59) 2515 Harvest 6.09 (1.54) 3.80 (2.12) 2030 Woman 6.71 (1.73) 4.54 (2.37) 8060 Boxer 5.36 (2.23) 5.31 (1.99) 8540 Athletes 7.48 (1.51) 5.16 (2.37) 5201 Nature 7.06 (1.71) 3.83 (2.49) Two Negative 2053 Baby 2.47 (1.87) 5.25 (2.46) 1390 Bees 4.50 (1.56) 5.29 (1.97) 3022 Scream 3.70 (1.91) 5.88 (2.08) 7950 Tissue 4.94 (1.21) 2.28 (1.81) 7493 Man 5.35 (1.34) 3.39 (2.08) 2681 Police 4.04 (1.60) 4.97 (2.26) 7002 Towel 4.97 (0.97) 3.16 (2.00) Positive 5900 Desert 5.93 (1.64) 4.38 (2.10) 8620 Woman 6.04 (1.43) 4.60 (2.08) 5600 Mountains 7.57 (1.48) 5.19 (2.70) 4610 Romance 7.29 (1.74) 5.10 (2.29) 1600 Horse 7.37 (1.56) 4.05 (2.37) 5700 Mountains 7.61 (1.46) 5.68 (2.33) 7281 Food 6.40 (1.52) 4.41 (2.26) 4631 Biker couple 5.36 (1.86) 5.19 (2.04) Three Negative 2749 Smoking 5.04 (1.39) 3.76 (2.03) 3220 Hospital 2.49 (1.29) 5.52 (1.86) 6830 Guns 2.82 (1.81) 6.21 (2.23) 9910 Car accident 2.06 (1.26) 6.20 (2.16) 6250 Aimed gun 2.83 (1.79) 6.54 (2.61) 7360 Flies on pie 3.59 (1.95) 5.11 (2.25) 9220 Cemetery 2.06 (1.54) 4.00 (2.09) 9440 Skulls 3.67 (1.86) 4.55 (2.02) Positive 1590 Horse 7.24 (1.45) 4.80 (2.10) 2510 Elderly woman 6.91 (1.91) 4.00 (2.10) 8461 Happy teens 7.22 (1.53) 4.69 (2.20) 8033 Ice skater 6.66 (1.52) 5.01 (2.15) 88

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Table 3-2. Continued Block Valence set Code Description Valence rating Arousal rating Three Positive 5982 Sky 7.61 (1.48) 4.51 (2.85) 4004 Erotic female 5.14 (1.85) 4.44 (2.14) 7480 Pasta 7.08 (1.62) 4.55 (2.42) Four Negative 2830 Woman 4.73 (1.60) 3.64 (2.23) 2190 Man 4.83 (1.28) 2.41 (1.80) 5130 Rocks 4.45 (1.13) 2.51 (1.72) 7034 Hammer 4.95 (0.87) 3.06 (1.95) 2840 Chess 4.91 (1.52) 2.43 (1.82) 6350 Attack 1.90 (1.29) 7.29 (1.87) 9470 Ruins 3.05 (1.51) 5.05 (1.98) Positive 7200 Brownie 7.63 (1.74) 4.87 (2.59) 2499 Neutral male 5.34 (1.43) 3.08 (1.73) 2340 Family 8.03 (1.26) 4.90 (2.20) 4535 Weight lifter 6.27 (1.70) 4.95 (2.32) 5830 Sunset 8.00 (1.48) 4.92 (2.65) 9156 Plane 6.43 (1.59) 5.79 (2.30) 2191 Farmer 5.30 (1.62) 3.61 (2.14) 4150 Attractive female 6.53 (1.86) 4.86 (2.55) Five Negative 2700 Woman 3.19 (1.56) 4.77 (1.97) 9181 Dead cows 2.26 (1.85) 5.39 (2.41) 7700 Office 4.25 (1.45) 2.95 (2.17) 9530 Boys 2.93 (1.84) 5.20 (2.26) 1930 Shark 3.79 (1.92) 6.42 (2.07) 2493 Neutral male 4.82 (1.27) 3.34 (2.10) 6571 Car theft 2.85 (5.59) 5.59 (2.50) Positive 8497 Carnival ride 7.26 (1.44) 4.19 (2.18) 2435 Mom/Son 5.84 (1.27) 3.94 (1.93) 2395 Family 7.49 (1.69) 4.19 (2.40) 2530 Couple 7.80 (1.55) 3.99 (2.11) 7286 Pancakes 6.36 (1.72) 4.44 (2.44) 2580 Chess 5.71 (1.41) 2.79 (1.78) 2389 Teens 6.61 (1.69) 5.63 (2.00) 7320 Desserts 6.54 (1.63) 4.44 (2.12) Six Negative 7590 Traffic 4.75 (1.55) 3.80 (2.13) 2710 Drug addict 2.52 (1.69) 5.46 (2.29) 9330 Garbage 2.89 (1.74) 4.35 (2.07) 1220 Spider 3.47 (1.82) 5.57 (2.34) 2455 Sad girls 2.96 (1.79) 4.46 (2.12) 9010 Barbed wire 3.94 (1.70) 4.14 (2.05) 6313 Attack 1.98 (1.38) 6.94 (2.23) Positive 2501 Couple 6.89 (1.78) 3.09 (2.21) 2311 Mother 7.54 (1.37) 4.42 (2.28) 8490 Roller coaster 7.20 (2.35) 6.68 (1.97) 89

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Table 3-2. Continued Block Valence set Code Description Valence rating Arousal rating Six Positive 2240 Neutral child 6.53 (1.48) 3.75 (2.14) 8300 Pilot 7.02 (1.60) 6.14 (2.21) 4623 Romance 7.13 (1.80) 5.44 (2.23) 1900 Fish 6.65 (1.80) 3.46 (2.32) 1460 Kitten 8.21 (1.21) 4.31 (2.63) Seven Negative 9046 Family 3.32 (1.49) 4.31 (1.99) 2272 Lonely boy 4.50 (1.78) 3.74 (1.94) 9320 Vomit 2.65 (1.92) 4.93 (2.70) 8465 Runner 5.96 (1.49) 3.93 (2.34) 9920 Car accident 2.50 (1.52) 5.76 (1.96) 3550 Injury 2.54 (1.60) 5.92 (2.13) 3300 Disabled child 2.74 (1.56) 4.55 (2.06) 9911 Car accident 2.30 (1.37) 5.76 (2.10) Positive 8200 Water skier 7.54 (1.37) 6.35 (1.98) 7340 Ice cream 6.68 (1.63) 3.69 (2.58) 2381 Girl 5.25 (1.22) 3.04 (1.97) 5870 Clouds 6.78 (1.76) 3.10 (2.22) 1603 Butterfly 6.90 (1.48) 3.37 (2.20) 4605 Couple 5.59 (1.52) 3.84 (2.12) 5460 Astronaut 7.33 (1.51) 5.87 (2.50) Eight Negative 2271 Woman 4.20 (1.26) 3.74 (1.69) 2750 Bum 2.56 (1.32) 4.31 (1.81) 9400 Soldier 2.50 (1.61) 5.99 (2.15) 7224 File cabinets 4.45 (1.36) 2.81 (1.94) 9280 Smoke 2.80 (1.54) 4.26 (2.44) 9041 Scared child 2.98 (1.58) 4.64 (2.26) 2745.1 Shopping 5.31 (1.08) 3.26 (1.96) 2005 Attractive man 6.00 (1.82) 4.07 (2.44) Positive 7352 Pizza 6.20 (2.20) 4.58 (2.45) 2224 Boys 7.24 (1.58) 4.85 (2.11) 2791 Balloons 6.64 (1.70) 3.83 (2.09) 8034 Skier 7.06 (1.53) 6.30 (2.16) 1450 Gannet 6.37 (1.62) 2.83 (1.87) 8192 Volcano skier 5.52 (1.53) 6.03 (1.97) 7250 Cake 6.62 (1.56) 4.67 (2.15) Nine Negative 4534 Male dancer 5.70 (1.68) 4.16 (2.37) 2900 Crying boy 2.45 (1.42) 5.09 (2.15) 6311 Distressed female 2.58 (1.56) 4.95 (2.27) 1200 Spider 3.95 (2.22) 6.03 (2.38) 9080 Wires 4.07 (1.45) 4.36 (2.17) 9830 Cigarettes 2.54 (1.75) 4.86 (2.63) 1932 Shark 4.00 (2.28) 6.80 (2.02) Positive 1740 Owl 6.91 (1.38) 4.27 (2.03) 90

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Table 3-2. Continued Block Valence set Code Description Valence rating Arousal rating Nine Positive 1710 Puppies 8.34 (1.12) 5.41 (2.34) 1942 Turtles 6.26 (1.76) 4.01 (2.05) 5532 Mushrooms 5.19 (1.69) 3.79 (2.20) 2341 Children 7.38 (1.59) 4.11 (2.31) 1616 Bird 5.21 (1.12) 3.95 (1.95) 8232 Boxer 5.07 (1.80) 5.10 (2.21) 8160 Rock climber 5.07(1.97) 6.97 (1.62) Ten Negative 3530 Attack 1.80 (1.32) 6.82 (2.09) 1201 Spider 3.55 (1.88) 6.36 (2.11) 9210 Rain 4.53 (1.82) 3.08 (2.13) 3301 Injured child 1.80 (1.28) 5.21 (2.26) 8230 Boxer 2.95 (1.88) 5.91 (2.15) 2751 Drunk driving 2.67 (1.87) 5.18 (2.39) 6900 Aircraft 4.76 (2.06) 5.64 (2.22) 9120 OilFires 3.20 (1.75) 5.77 (1.94) Positive 2150 Baby 7.92 (1.59) 5.00 (2.63) 7475 Shrimp 6.33 (1.66) 4.17 (2.49) 4614 Romance 7.15 (1.44) 4.67 (2.47) 2222 Boys reading 7.11 (1.54) 4.08 (2.15) 7402 Pastry 5.98 (2.04) 5.05 (2.12) 5621 Sky divers 7.57 (1.42) 6.99 (1.95) 2235 Butcher 5.64 (1.27) 3.36 (1.92) 91

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Table 3-3. IAPS picture codes, desc ription, valence, and arousal ra tings of images presented in the post-training run (Experiment 2) Task Valence set Code Description Valence Arousal Evaluative Negative 5531 Mushroom 5.15 (1.45) 3.69 (2.11) 9600 Ship 2.48 (1.62) 6.46 (2.31) 2870 Teenager 5.31 (1.41) 3.01 (1.72) 9102 Heroin 3.34 (1.76) 4.84 (2.50) 6000 Prison 4.04 (1.74) 4.91 (2.17) 1280 Rat 3.66 (1.75) 4.93 (2.01) 1050 Snake 3.46 (2.15) 6.87 (1.68) 2440 Neutral girl 4.49 (1.03) 2.63 (1.70) 7006 Bowl 4.88 (0.99) 2.33 (1.67) 1052 Snake 3.50 (1.87) 6.52 (2.23) 2691 Riot 3.04 (1.73) 5.85 (2.03) 4621 Harassment 3.19 (1.59) 4.92 (2.24) 1101 Snake 4.10 (1.85) 5.83 (2.25) 1300 Pit bull 3.55 (1.78) 6.79 (1.84) 1120 Snake 3.79 (1.93) 6.93 (1.68) Positive 8340 Plane 6.85 (1.69) 5.80 (2.36) 8186 Sky surfer 7.01 (1.57) 6.84 (2.01) 2391 Boy 7.11 (1.77) 4.63 (2.43) 5910 Fireworks 7.80 (1.23) 5.59 (2.55) 5480 Fireworks 7.53 (1.63) 5.48 (2.35) 5750 Nature 6.60 (1.84) 3.14 (2.25) 1726 Tiger 4.79 (2.10) 6.23 (2.19) 1670 Cow 5.82 (1.63) 3.33 (1.98) 5780 Nature 7.52 (1.45) 3.75 (2.54) 5260 Waterfall 7.34 (1.74) 5.71 (2.53) 7080 Fork 5.27 (1.09) 2.32 (1.84) 1812 Elephants 6.83 (1.33) 3.60 (2.11) 4533 Attractive man 6.22 (2.24) 5.01 (2.47) 8021 Skier 6.79 (1.44) 5.67 (2.37) 7270 Ice cream 7.53 (1.73) 5.76 (2.21) Nonevaluative Negative 6315 Beaten female 2.31 (1.69) 6.38 (2.39) 9190 Woman 3.90 (1.44) 3.91 (1.73) 9415 Handicapped 2.82 (2.00) 4.91 (2.35) 2880 Shadow 5.18 (1.44) 2.96 (1.94) 7090 Book 5.19 (1.46) 2.61 (2.03) 2752 Alcoholic 4.07 (1.84) 4.30 (1.94) 9182 Horses 3.52 (2.04) 4.98 (2.07) 2312 Mother 3.71 (1.64) 4.02 (1.66) 2690 Terrorist 4.78 (1.43) 4.02 (2.07) 7110 Hammer 4.55 (0.93) 2.27 (1.70) 2141 Grieving female 2.44 (1.64) 5.00 (2.03) 92

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93 Table 3-3. Continued Task Valence set Code Description Valence Arousal Nonevaluative Negative 7037 Trains 4.81 (1.12) 3.71 (2.08) 6243 Aimed gun 2.33 (1.49) 5.99 (2.23) 9110 Puddle 3.76 (1.41) 3.98 (2.23) 6200 Aimed gun 3.20 (1.62) 5.82 (1.99) Positive 5623 Wind surfers 7.19 (1.44) 5.67 (2.32) 8178 Cliff diver 6.50 (2.00) 6.82 (2.33) 1313 Frog 5.65 (1.47) 4.39 (2.03) 2170 Mother 7.55 (1.42) 4.08 (2.48) 5270 Nature 7.26 (1.57) 5.49 (2.54) 4274 Attractive female 5.42 (1.83) 4.18 (2.39) 4689 Erotic couple 6.90 (1.55) 6.21 (1.74) 2160 Father 7.58 (1.69) 5.16 (2.18) 5991 Sky 6.55 (2.09) 4.01 (2.44) 2579 Bakers 5.53 (1.35) 3.85 (2.00) 8370 Rafting 7.77 (1.29) 6.73 (2.24) 1920 Porpoise 7.90 (1.48) 4.27 (2.53) 1340 Women 7.13 (1.57) 4.75 (2.31) 1999 Mickey 7.43 (1.47) 4.77 (2.40) 6250.2 Ice cream 6.32 (1.70) 5.13 (2.06)

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Table 3-4. Ratings of pleasant and unpleasant images in the pre-training and the posttraining run (Experiment 2) Judgment type Valence Run Pre-training run Post-training run Evaluative Pleasant 3.69 (1.01) 3.99 (0.84) Unpleasant 3.08 (1.09) 2.64 (0.71) Non-evaluative Pleasant 3.36 (0.92) 3.31 (0.85) Unpleasant 3.37 (0.91) 3.09 (0.79) Data presented in the cells are ratings to images presented in each run. Data in the parentheses are standard deviations of corresponding cell means. 94

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95Table 3-5. Distribution of responses for ev aluative and non-evaluative tasks in pretraining and post-training runs (Experiment 2) Judgment type Run Responses (frequency and %) Button 1 Button 2 Button 3 Button 4 omitted Evaluative Pre-traini ng 93 (27.5) 86 (25.4) 92 (27.2) 67 (19.8) 82 (19.5) Post-training 105 (26.9) 116 ( 29.7) 111 (28.4) 59 (15.1) 29 (6.9) Non-evaluative Pre-training 58 (18.8) 112 (36.4) 105 (34.1) 33 (10.7) 112 (26.7) Post-training 72 (19.3) 182 ( 48.7) 94 (25.1) 26 (7.0) 46 (11.0) For evaluative tasks, button 1 represents extremely unpleasant button 2 represents unpleasant button 3 represents pleasant and button 4 represents extremely pleasant For non-evaluative tasks, button 1 represents rarely/never button 2 represents occasionally button 3 represents often and button 4 represents always. For button 1, 2, 3 and 4, the data in the parentheses are percentage of frequency of button selection in all valid responses for co rresponding tasks in corresponding run. For missing responses, data in the parentheses are percentage of omitted trials in all 420 possible responses for corresponding tasks in corresponding run.

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Table 3-6. List of references of regions-of interest selected in the fMRI study of evaluation pro ceduralization (Experiment 2) Region BA Side Coordina tes Voxels Reference x y z Caudate nucleus L & R 15 4 485 Poldrack et al., 1999 Putamen L 27 8 4 485 An independent study R 18 1 15 485 Mallol et al., 2007 Nucleus accumbens L 4 6 2 485 Lieberman et al., 2004 Cerebellum R 31 65 18 485 Willingham et al., 2002 Superior parietal lobe 7 R 12 67 50 485 Willingham et al., 2002 Inferior parietal lobe L 34 46 56 485 Mallol et al., 2007 40 R 50 26 36 485 Poldrack et al., 1999 Medial temporal lobe L 39 15 24 485 Rose et al., 2004 Dorsolateral prefrontal cortex L 30 55 24 485 Klingberg et al., 1997 R 48 41 9 485 Taylor et al., 2003 Ventromedial prefrontal cortex L 22 30 16 485 Lieberman et al., 2004 Amygdala L 24 1 14 1764 Anatomically defined (Norris et al., 2004) Insula 13 L 30 23 7 111 An independent study Orbito-frontal cortex 47 R 45 35 96 4 485 Maccotta & Buckner, 2004 Anterior cingulate cortex 32 L 4 24 32 485 Cunningham et al., 2004 Frontal operculum L 46 14 6 485 Lane et al., 1997 Temporal pole 21/38 R 42 8 30 485 Lane et al., 1997 Calcarine L 17 93 17 485 Maccotta & Buckner, 2004 Posterior fusiform 18/19 L 36 73 13 485 Maccotta & Buckner, 2004 Superior occipital gyrus R 32 74 29 485 Garavan et al., 1999 Parieto-occipital cortex 39 R 44 70 20 485 Lane et al., 1997 Motor cortex 4 L 37 25 50 485 Maccotta & Buckner, 2004 Inferior frontal gyrus 46 L & R 17 24 485 Maccotta & Buckner, 2004 9 L & R 3 32 485 Maccotta & Buckner, 2004 BA = Brodmanns area; L, left; R, right; x y z : coordinates of the centroid of the region in Tailarach coordinates; Voxels: volume of the region in mm3. References indicate the articles from whic h the coordinates of ROIs were extracted.

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Table 3-7. Means of BOLD signals in ROIs previously linked to proced ural learning for evaluative and non-evaluative tasks in th e pre-training and post-traini ng runs (Experiment 2) Region BA Side Evaluative tasks Non-evaluative tasks Interaction Pre-training run Post-training run Pre-training run Post -training run df F Caudate nucleus L & R 0.14 0.28 0.22 0.26 1541 2.44 Putamen L 0.33 0.22 0.35 0.20 1558 < 1 R 0.21 0.29 0.14 0.25 1602 < 1 Nucleus accumbens L 0.10 0.30 0.34 0.01 1546 < 1 Cerebellum R 1.19 1.59 1.39 1.50 1499 4.52 Superior parietal lobe 7 R 0.22 0.44 0.34 0.39 1493 4.32 Inferior parietal lobe L 0.97 0.62 0.33 0.39 1654 3.35 40 R 0.23 0.15 0.14 0.16 1595 3.14 Inferior frontal gyrus 46 L & R 0.30 0.49 0.39 0.45 1483 5.40 9 L & R 0.36 0.60 0.43 0.58 1472 3.16 Table entries for evaluative and non-evalua tive tasks are means of BOLD signals of ROIs in each condition (vs. null trials). Significant BOLD signals indicate that the corresponding ROI was si gnificantly activated or deactiv ated comparing to the activa tion baseline of null trials. F = F score for interaction between task and run in each ROI. BA = Brodmanns area; L, left; R, right; df, degrees of freedom; Between-run comparisons in activations for evaluative tasks in a ll regions were significant at a .005 level except for the putamen areas ( p < .05) and the right infe rior parietal lobe ( p < .05) (Bonferroni correction was applied to reduced the Type I error in multiple comparisons, the level was set at .005). 97, p < .15 p < .10 *, p < .05

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Table 3-8. Means of BOLD signals in ROIs previously linked to declarative learning for evaluative and non-evaluative tasks in t he pre-training and post-traini ng runs (Experiment 2) Region BA Side Evaluative tasks N on-evaluative tasks Interaction Pre-training run Post-training run Pre-training run Post-training run df F Medial temporal lobe L 0.09 0.12 0.07 0.01 1656 < 1 Dorsolateral prefrontal cortex L & R 0.37 0.10 0.11 0.09 1630 4.68 Ventromedial prefrontal cortex L 0.32 0.14 0.33 4.88 0.21 1660 Table entries for evaluative and non-evalua tive tasks are means of BOLD signals of ROIs in each condition (vs. null trials). Significant BOLD signals indicate that the corresponding ROI was si gnificantly activated or deactiv ated comparing to the activa tion baseline of null trials. F = F score for interaction between task and run in each ROI. BA = Brodmanns area; L, left; R, right; df, degrees of freedom; Between-run comparisons in activations for evaluative tasks in a ll regions were significant at a .01 level (Bonferroni correction was applied to reduced the Type I error in multiple comparisons, the level was set at .017). *, p < .05 98

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Table 3-9. Means of BOLD signals in ROIs previously linked to evaluative processi ng for evaluative and non-evaluative tasks in the pre-training and post-traini ng runs (Experiment 2) Region BA Side Evaluative tasks Non-evaluative tasks Interaction Pre-training run Post-training run Pre-training run F Post -training run df Regions associated with automatic evaluation Amygdala L 0.22 0.30 0.23 0.35 1535 < 1 Insula 13 L 0.34 1.38 0.30 1.42 1655 < 1 Orbito-frontal cortex 47 R 0.06 0.43 0.07 0.49 1655 < 1 Regions associated with controlled evaluation Anterior cingulate cortex 32 L 0.02 0.09 0.06 0.07 1491 3.53 Frontal operculum L 0.28 0.42 0.39 0.43 1528 1.52 Temporal pole 21/38 R 0.12 0.31* 0.20* 0.25* 1523 4.28* Table entries for evaluative and non-evalua tive tasks are means of BOLD signals of ROIs in each condition (vs. null trials). Significant BOLD signals indicate that the corresponding ROI was si gnificantly activated or deactiv ated comparing to the activa tion baseline of null trials. F = F score for interaction between task and run in each ROI. BA = Brodmanns area; L, left; R, right; df, degrees of freedom; Between-run comparisons in activations for evaluative tasks in a ll regions were significant at a .001 level except for the amygdala (p < .10), the anterior cingulate cortex ( p = .01), and the frontal operculum ( p = .02) (Bonferroni correction was applied to reduced the Type I er ror in multiple comparisons, the level was set at .008). 99, p < .10 *, p < .05

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Table 3-10. Means of BOLD signals in ROIs previously linked to motor function an d visual processing fo r evaluative and nonevaluative tasks in the pr e-training and post-training runs (Experiment 2) Region BA Side Evaluative tasks Non-evaluative tasks Interaction Pre-training run Post-training run Pre-training run Post -training run df F Calcarine L 0.88 0.95 1.02 0.90 1545 < 1 Posterior fusiform 18/19 L 0.90 1.16 1.06 1.15 1486 3.05 Superior occipital gyrus R 0.31 0.57 0.43 0.52 1479 5.41 Parieto-occipital cortex 39 R 0.15 0.47 0.20 0.46 1532 < 1 Motor cortex 4 L 0.56 0.51 0.65 0.50 1503 1.94 Table entries for evaluative and non-evalua tive tasks are means of BOLD signals of ROIs in each condition (vs. null trials). Significant BOLD signals indicate that the corresponding ROI was si gnificantly activated or deactiv ated comparing to the activa tion baseline of null trials. F = F score for interaction between task and run in each ROI. BA = Brodmanns area; L, left; R, right; df, degrees of freedom; Between-run comparisons in activations for evaluative tasks in a ll regions were significant at a .001 level except for the calcarine ( ns ) and the motor cortex ( ns ) (Bonferroni correction was applied to reduced the Type I error in multiple comparisons, the level was set at .01). p < .10; *, p < .05 100

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101Table 3-11. Correlation coefficients of BOLD signal changes (prevs. posttraini ng run) during evaluative tasks in regional c lusters (Experiment 2) Declarative learning regions Procedural learning regions Automatic evaluation regions Controlled evaluation regions Early visual regions Late visual regions Motor regions Declarative learning regions 1 Procedural learning regions 0.15 1 Automatic evaluation regions 0.02 0.03 1 Controlled evaluation regions 0.04 0.78 *** 0.07 1 Early visual regions 0.35 0.35 0.16 0.11 1 Late visual regions 0.15 0.48 0.27 0.57 0.12 1 Motor regions 0.07 0.58 0 0.60 0.16 0.64 1 ***: p < .001, *: p < .05, : p < .10.

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A B C D Figure 3-1. Sample IAPS pictures used in Experi ment 2. A) Sample pleasant image presented for evaluative tasks. B) Sample unpleasant im age presented for evaluative tasks. C) Sample pleasant image presented for nonevaluative tasks. D) Sample unpleasant image presented for non-evaluative tasks. 102

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0.2 0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Pre trainingPost training Procedural learning Declarative learning Automatic evaluation Controlled evaluation Late visual processing Figure 3-2. Practice-related ac tivation changes for evalua tive tasks (Experiment 2). 103

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Figure 3-3. Maps for brain activity changes for evaluative tasks in le arning-related regions (Experiment 2). Areas previously linked to procedural learning include the putamen (A, the left putamen, t = 7.04; B, the right putamen, t = 5.03), the inferior parietal lobe (C, the right IPL, t = 4.41; D, the left IPL, t = 7.53), the caudate nucleus (E, the left caudate nucleus, t = 7.42; F, the right caudate nucleus, t = 8.79), the right superior parietal lobe (G, t = 16.04), the BA 46 (H, the right BA 46, t = 16.23; I, the left BA 46, t = 26.75), the cerebellum (J, t = 18.68), the BA 9 (K, the left BA 9, t = 30.95; L, the right BA 9, t = 37.31), and the left nucleus accumbens (M, t = 33.58). Areas previously linked to declarative learning include the medial temporal lobe (N, t = 10), the dorsolateral prefrontal cortex (O, the right dlPFC, t = 7.41; P, the left dlPFC, t = 6.51), and the left ventromedial prefrontal cortex (Q, t = 6.49). Color scale represents the order of t -values of prevs. posttr aining comparisons in BOLD responses for evaluative tasks. 104

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Figure 3-4. Maps for brain activity changes for evaluative tasks in evaluation-related regions (Experiment 2). Areas previously linked to automatic evaluation include the left amygdala (A, t = 2.96), the right orbitofrontal cortex (B, t = 18.78), and the left insula (C, t = 53.49). Areas previously linked to cont rolled evaluation incl ude the left frontal operculum (D, t = 5.60), the anterior cingulate cortex (E, t = 6.74), and the right temporal pole (F, t = 16.93). Color scale represents the order of t -values of prevs. posttraining comparisons in BOLD responses for evaluative tasks. 105

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Figure 3-5. Maps for brain activity changes for evaluative tasks in regions related to visual and motor processing (Experiment 2). Areas previ ously linked to primary motor skills is represented by the left BA 4 (hand region) (A, t = 1.11). Areas previously linked to primary visual processing is represented by the calcarine (B, t = 0.23). Areas previously linked to late visual proce ssing include the posterior fusiform (C, t = 15.21), the superior o ccipital gyrus (D, t = 27.84), and the parietal occipital cortex (E, t = 39.75). Color scale represents the order of t -values of prevs. posttraining comparisons in BOLD respons es for evaluative tasks. 106

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107 CHAPTER 4 GENERAL DISCUSSION We began with a question: As evaluation is of ten automatic, is there room for increases in the procedural efficiency of exp licit evaluative judgments? That is, can evaluative judgments be proceduralized through practice, and if so, what aspects of the process of making evaluative judgments are being proceduralized? To answer these questions, we firs t investigated whether there are decreases in the times required to ma ke evaluative judgments of emotional visual stimuli following practice, and subsequently ex plored the functional anatomic correlates of proceduralized evaluations of emotional stimuli. Together, our findings provide important evidence of the effects of proceduralization on behavioral and neural responses to emotional stimuli. Summary of Findings The results from Experiment 1 demonstrated th at, like various cogniti ve, social and motor skills, evaluative judgments can also be pr oceduralized or speeded up through practice. Moreover, consistent with Smith and his colleagues (1986, 1994) work on procedural efficiency, Experiment 1 showed that evaluativ e judgments quickly auto mate with repeated execution. More importantly, evalua tion proceduralization does not have to be content-specific, but instead generalizes or transf ers to novel stimuli. Specifically, participants who had practiced evaluations of pictures were more likely to make spontaneous evaluative responses of new pictures than participants who had practiced non-evaluative judgment s. Interestingly, nonevaluative (frequency-rating) judgm ents of the same set of imag es were also proceduralized through practice, but their proced ural efficiency did not facil itate corresponding responses to new stimuli.

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Experiment 2 further contributed to unde rstanding which aspects of evaluation proceduralize by providing evidence on brain co rrelates of evaluati on proceduralization. Generally, the results of Experiment 2 suggest that multiple brain regional systems are involved in evaluation proceduralization. Specifically, following practice, evaluative judgments evoked higher responses in task-related areas, such as re gions associated with automatic and controlled evaluation (i.e., the amygdala, the insula, the anterior cingulate cortex, the temporal pole, the frontal operculum and the orbitofr ontal cortex), as well as regions associated with late visual processing (i.e., the posterior fusiform, the pariet al occipital lobe, and the superior occipital lobe). Also, when evaluative task s proceduralized, regions related to procedural learning (i.e., the striatal system, the superior pari etal lobe, the inferior frontal cortex, and the cerebellum) were more responsive to evaluative tasks, whereas re gions related to declar ative learning (i.e., the medial temporal lobe, the dorsola teral prefrontal cortex, and the ventromedial pref rontal cortex) were correspondingly less responsiv e. These fMRI results provide insight into how practice proceduralizes evaluations at the brain level. Contributions The current study contributes to the area of social cognitive neuroscience of evaluation in several aspects. First, we found that the amygda la, as well as the frontal operculum, showed different activation patterns for pleasant and unpl easant images. These findings suggest that the amygdala and the frontal operculum are sensitive to valence. Second, the findings in the present research c onfirm that evaluation is not a single process but involves both automatic and controlled aspect s. Brain regions associated with automatic evaluation, as well as those associated with c ontrolled evaluation, were more active after than before the training in evaluative tasks. However, regions previously associated with automatic evaluation were more active rega rdless of explicit task instruc tion, whereas regions related to 108

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controlled evaluation were only more active for trained evaluative tasks but not untrained nonevaluative tasks. Therefore, eff ects of practice with evaluation on regions related to automatic evaluation generalized to other type of judgments, whereas th e effects on controlled evaluation regions were task-specific. Third, extensive research has distinguished f unctions of cerebral regions in implicit and explicit evaluation based on their differential activities during explicit and implicit evaluative tasks (e.g., Cunningham et al., 2003; Cunningham et al., 2004). Specifically, previous research suggest that the amygdala, the insula, and the OF C are involved in automatic evaluation as they are active with the presentation of emotional stimuli regardless of whether or not explicit evaluations are required. In c ontrast, the ACC, the temporal lobe, and the frontal operculum might be involved in controlled evaluation as their activation were higher when explicit evaluations of targets were required than when they were not (Lane et al., 1997). Consistently with the above previous findings in regions a ssociated with automatic evaluation (Cunningham, Raye, & Johnson, 2004; Cunningham et al., 2003; Lane et al., 1997; Wright & Liu, 2005; Wright et al., 2008), we did not detect task (evaluation vs. non-evaluation) differences in activations in the amygdala, the insula, or the OFC either before or after training in evaluation. However, the current study also did not detect any differences between activations in the controlled-evaluation regions for either evaluative or non-evaluative tasks during either the pre-training and the posttraining run. That is, the anteri or cingulate, as well as the temporal pole and the frontal operculum, were equally activated (vs. null trials ) for evaluative and non-e valuative tasks before and after training. One possible r eason for the lack of between-tas k difference in activations in controlled-evaluation regions might be the desi gn of the current study. That is, our rapid 109

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presentation of stimuli (less than 3000 ms) may have reduced the detectability of differential responses. Further research should be conducted to explain this discrepancy. Importantly, the present findings shed light on the processes that contribute to evaluation proceduralization. Although numerous studies have suggested a decreased regional activation due to practice, our findings suggest that ev aluation practice produces an increased regional activation in evaluative processing areas. One possible explanation is that experimental paradigms of repetition suppression (Buchel et al., 1999; Maccotta & Buckner, 2004) only entail repeated presentation of the same stimuli. Therefore, increased activations in regions linked to evaluative processing can also re flect a progressive optimization of neuronal responses elicited by evaluative tasks. In addition, th is progressive optimization may be facilitated by more focused activation voxels when evaluations proceduralized. In any case, the findings of increased brain activation in the evaluative processing areas are consistent with previ ous reports of practice induced increases in task-speci fic brain areas (Grossman et al ., 2004). Moreover, the increased activation in the left amygdala wh en evaluation proceduralizes can be explained by increased left amygdala demands for evaluative-memory retrie val (Sergerie, Lepage, & Armony, 2006). More direct comparisons of brain function during evaluations of iden tical stimuli and non-repeated stimuli should be done to understand how progr essive optimization o ccurs in each case. In closing, the current study supports a mode l in which brain activation changes as a function of experience. Practice with evaluative judgments reorga nizes regional activations, as brain regions activated early in the practice differ from the ones activated late in the practice. More specifically, evaluation pr oceduralization increases activity in brain regions associated with automatic evaluation but decreases activity in brain region s associated with declarative learning. Moreover, regression anal yses revealed that the effects of practice on response times 110

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111 for evaluative judgments can be predicted from activation changes in decl arative and procedural learning regions, automatic and controlled evaluati on regions, motor cortex, as well as the early visual regions. Taken together, our findings show the ways in which long-standing theories of attitude and evaluation can be tested using a social-cognit ive-neuroscience approach. Such analyses demonstrate the value of social psychological theories for unders tanding the work of the brain during practice with evaluation. By using neuroimaging to iden tify these brain correlates and drawing inferences from what is already known about the processing role s of these regions, we were able to generate new insi ghts regarding the behavioral and brain functional consequences of practice in evaluation. Such insights suggest that a social cognitive neuroscience approach can trigger promising advance for both social psychological theory and neuroscience.

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APPENDIX A INSTRUCTIONS AND CUES FOR EVAL UATIVE AND NON-EVALUATIVE TASKS (EXPERIMENT 2) Evaluative tasks instructions: This task invol ves evaluating the content of images. Please discriminate between images that contai n pleasant (i.e., positive, good, pleasing, etc.) content vs. unpleasant (i.e., nega tive, bad, displeasing, etc.) cont ent. We are interested in both your evaluation and the speed with which you make it. Therefore, try to respond as quickly and as accurately as you can. Click any button when you are ready to begin. Evaluative tasks questions: How pleasant do you find the content of this image?4-points scale: extremely unpleasant; unpleas ant; pleasant; extremely pleasant Non-evaluative tasks instructions: This task involves estimating the frequency with which images of similar content appear on televi sion. Please estimate the frequency using the scale provided.We are interested in both your estimate and the speed with which you make it. Therefore, try to respond as qui ckly and as accurately as you can. Non-evaluative tasks questions: How frequently do images with similar content appear on television? 4-points scale: rarely/never; occasiona lly; often; always 112

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APPENDIX B CORRELATION COEFFICIENTS OF ROI AC TIVITY CHANGES AND BEHAVIORAL PERFORMANCE CHANGE FROM THE PR ETO THE POSTTRAINING RUN (EXPERIMENT 2). 113

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Table B-1. Correlation coefficients of ROI ac tivity changes and behavioral performance ch ange from the preto the posttraini ng run (Experiment 2). RT Omit rate CN PT NA CB PC BA 46 BA 9 MTL dlPFC vmPFC omit rate 0.35 CN 0.41 0.09 PT 0.36 0.24 0.37 NA 0.10 0.29 0.50 0.50 CB 0.23 0.33 0.06 0.00 0.55 PC 0.04 0.08 0.47 0.16 0.18 0.33 BA 46 0.29 0.23 0.42 0.48 0.16 0.62 0.42 BA 9 0.03 0.03 0.16 0.22 0.03 0.53 0.47 0.38 MTL 0.19 0.18 0.27 0.09 0.24 0.29 0.03 0.31 0.55 dlPFC 0.39 0.32 0.06 0.13 0.01 0.03 0.11 0.01 0.49 0.47 vmPFC 0.31 0.21 0.07 0.09 0.22 0.11 0.05 0.14 0.35 0.39 0.41 IPL (L) 0.29 0.05 0.12 0.02 0.07 0.04 0.08 0.20 0.32 0.36 0.37 0.34 IPL (R) 0.33 0.28 0.30 0.35 0.17 0.22 0.06 0.20 0.37 0.01 0.04 0.45 AG 0.16 0.35 0.54 0.38 0.28 0.27 0.65 0.59 0.16 0.14 0.03 0.01 IS 0.70 ** 0.24 0.09 0.46 0.02 0.14 0.23 0.33 114 0.03 0.24 0.23 0.13 OFC 0.59 0.03 0.16 0.05 0.19 0.00 0.32 0.19 0.20 0.26 0.02 0.04 ACC 0.05 0.43 0.46 0.36 0.03 0.54 0.69 ** 0.76 ** 0.57 0.18 0.15 0.11 FO 0.13 0.01 0.20 0.24 0.25 0.62 0.65 0.73 ** 0.43 0.21 0.22 0.15 TP 0.12 0.32 0.53 0.27 0.26 0.15 0.69 ** 0.60 0.37 0.20 0.03 0.08 CA 0.08 0.25 0.22 0.22 0.01 0.24 0.11 0.27 0.39 0.18 0.36 0.43 PF 0.14 0.34 0.33 0.15 0.00 0.24 0.40 0.25 0.43 0.09 0.18 0.26 SOG 0.01 0.43 0.31 0.07 0.02 0.41 0.47 0.19 0.13 0.31 0.28 0.23 POC 0.31 0.27 0.33 0.08 0.35 0.06 0.60 0.06 0.12 0.20 0.00 0.05 MC 0.29 0.51 0.24 0.01 0.40 0.72 ** 0.40 0.54 0.49 0.12 0.09 0.33

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115Table B-1. Continued IPL (L) IPL (R) AG IS OFC ACC FO TP CA PF RSOG RPOC IPL (R) 0.12 AG 0.19 0.10 IS 0.18 0.15 0.25 OFC 0.17 0.01 0.30 0.75 ** ACC 0.20 0.37 0.72 ** 0.04 0.12 FO 0.00 0.03 0.54 0.19 0.05 0.64 TP 0.26 0.26 0.69 ** 0.07 0.05 0.84 *** 0.54 CA 0.43 0.08 0.07 0.11 0.25 0.19 0.20 0.06 PF 0.24 0.41 0.21 0.01 0.01 0.70 ** 0.21 0.56 0.23 SOG 0.03 0.31 0.38 0.24 0.25 0.52 0.21 0.43 0.00 0.61 POC 0.24 0.05 0.48 0.45 0.31 0.47 0.09 0.61 0.03 0.52 0.70 ** MC 0.03 0.51 0.34 0.01 0.07 0.75 ** 0.43 0.44 0.16 0.67 ** 0.59 0.36 RT: response time; CN: caudate nucleus; PT : putamen areas; NA: nucleus accumbens; CB: cerebellum; PC: precuneus; MTL: medial temporal lobe; dlPFC: dorsolateral prefrontal cortex areas; vmPFC: ventro medial prefrontal cortex; IP L: inferior parietal lobe (L: left side, R: right side); AG: amygdala; IS: insu la; OFC: orbitofrontal cortex; ACC: anteri or cingulate cortex; FO: frontal operculu m; TP: temporal pole; CA: calcarine; PF: posterior fusiform; SOG: superior occi pital gyrus; POC: parietal occipital cortex; MC: motor cortex. p < .10 *, p < .05 **, p < .01 ***, p < .001

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124 BIOGRAPHICAL SKETCH Hong Li has a Bachelor of Engineering degree from the Qingdao University, China, majoring in electrical en gineering, and a Master of Education degree from the Peking University, China, majoring in personality and social psyc hology. She joined the Ph.D. program of social psychology at the University of Florida in August 2003, and received her Master of Science degree, majoring in social psychology, in August 2005. Hong Li will r eceive her Doctor of Philosophy degree, majoring in soci al psychology, in December 2008.