BEHAVIORAL AND NEURAL INVESTIGATIONS OF PERCEPTUAL AFFECT by Edward Allen ...

225 Pages·2004·5.25 MB·English

BEHAVIORAL AND NEURAL INVESTIGATIONS
OF PERCEPTUAL AFFECT

by

Edward Allen Vessel

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(NEUROSCIENCE)

May 2004

Copyright © 2004 Edward Allen Vessel
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ACKNOWLEDGMENTS

First and foremost, I would like to thank my advisor, Irving Biederman, for his
professional, financial, and personal support. He gave me this wonderful
opportunity to work on this incredibly interesting idea, and the confidence to
believe that I could do great things with it. He provided extensive guidance
during the design, execution, and analysis of the experiments, and very
efficiently gave me feedback on drafts of this document. Most important of
all, he put up with my many idiosyncrasies and enthusiastically encouraged
my successes.

Special thanks are also due to Mark Cohen of the UCLA Brain Mapping
Center for his extensive guidance and friendship through all the phases of
the brain imaging study, including design, running subjects, data analysis,
and interpretation.

I would also like to thank the members of my dissertation committee.
Zhonglin Lu gave me assistance with brain imaging analysis and group
statistics in addition to general guidance & support. My discussions with
Richard Thompson about learning and habituation proved very helpful, as
was his general guidance and support. Bartlett Mel had many helpful
thoughts on the modeling of preference. Mitchell Earleywine provided helpful
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comments on the factor analysis of preference, as well as personal support
in becoming a better writer and researcher.

In addition, I am indebted to a number of other USC faculty have helped me
with aspects of this work. Bosco Tjan provided an algorithm for creating the
spectrally equivalent images, assisted with the computation of RMS contrast,
provided scripts for the LO localizer scan, and gave extensive assistance in
brain imaging analysis and Linux system administration. Alan Watts and
Larry Swanson provided valuable insights into peptide systems, cortical
output, anatomy, and emotion. The simulated annealing algorithm used to
design imaging sequences was designed with the assistance of Laurent Itti,
and Laura Baker provided assistance with the multiple regression analyses
used in both the behavioral and imaging experiments.

This work would also have been quite difficult without the extensive input and
support from my fellow graduate students, and I would like to thank them. In
particular, Michael Mangini engaged me in many helpful discussions on this
work, assisted with the multiple regression analysis, and provided some
much needed distractions. My officemate Marissa Nederhouser provided
assistance with statistical analyses, good music, and interesting conversation
over the years. Several undergraduates also assisted in running subjects
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and creating stimuli, including Michelle Greene, Henry Nguyen, Tiger
Nguyen, Viet Nguyen and Ali Narayan.

This work would also not have been possible without extensive help from a
number of researchers around the world, and they deserve my heartfelt
thanks. Moshe Bar (currently at Massachusetts General Hospital) provided
many helpful discussions on preference, priming, and scene perception, and
assisted in my selection of imaging parameters and fMRI sequence design.
David Glahn (currently at UTHSCSA) assisted with the imaging study design
and analysis, particularly in the use of the AFNI software. At UCLA, graduate
student Richard Albistegui-Dubois provided extensive instruction on and
assistance with operating the magnet and data analysis, and both Zrinka
Bilusic and Anne Firestine provided analysis help and assistance with the
magnet. Craig Stark (JHU) provided assistance with the AFNI software and
the region of interest (ROI) analysis. Geoff Boynton (UCSD) aided
extensively in the design and analysis of the imaging experiment. Russ
Poldrack (UCLA) provided assistance with impulse response functions and
general analysis. Bob Cox (NIMH) provided help with the AFNI software,
especially as it pertains to the general linear model, deconvolutions, and
motion correction. Douglas Ward (Medical College of Wisconsin) assisted
with the deconvolution of my imaging data using the program 3dDeconvolve.
Stephen Smith and the rest of FMRIB group (Oxford University Center for
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Functional MRI of the Brain) were a tremendous help with the analysis of my
imaging data using the FSL software. Randy Buckner (WUSTL), also
provided helpful analysis information. I would also like to thank Jean-Marc
Fellous (UCSD Salk Institute) for bringing the opioid gradient to the attention
of Dr. Biederman.

I would like to my heartfelt gratitude to my many friends who put up with me
while working on these experiments, and for keeping me sane. In particular,
my housemates Jason, Trystan, Ian, and Adam deserve thanks for allowing
me to bring my work home with me, as do Roxanne, Ann, and Walid for
interesting discussions and being great friends. Lastly, Shari Cha deserves
special thanks for providing a peaceful place to write, helping me relax,
keeping me motivated, and cheering my spirits!

The Dartmouth Summer Institute deserves acknowledgment for giving me a
first chance to get some practical experience with imaging and analysis and
for introducing me to a number of people that have been very helpful.

While this work was being completed, I was supported by a variety of
sources, including an NIH Predoctoral Cognitive and Computational Training
Grant to USC (T32 MH20003-05), and grants awarded to Irving Biederman
from the Human Frontiers Science Program Organization (RG0035/2000B),
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The Army Research Office (MURI ARO DAAG55-98-1-0293), the National
Science Foundation (IMSC NSF EEC-9529152), and the James S.
McDonnell Foundation (99-53).
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TABLE OF CONTENTS

ACKNOWLEDGMENTS……………………………………………………………ii

LIST OF TABLES………………………………………………...………………... x

LIST OF FIGURES………………………………………………………………... xi

ABBREVIATIONS……………………………………………………..…………. xiv

ABSTRACT……………………………………………..………………………. xvi

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

II. A THEORY OF COGNITIVE AND PERCEPTUAL AFFECT………………. 9

Positive Affect in the Ventral Visual Pathway……………………………9
A Neurochemical Index of Interpretability…..…………………. 12
Behavioral Effects of Opioid Antagonists……………… 16
Opioid Systems in the Brain…………………………..… 17
Novelty, Priming, and Competitive Learning………………...…26
Perceptual Affect in a Systems Perspective…………………... 32
Predictions and Extensions………………………………………………37

III. VISUAL PREFERENCE………………………………………………………43

Background……………………………………………………………..… 43
Complexity vs. Interpretability……………………………………43
Effects of Previous Exposure…………………………………… 46
Arousal and Emotional Valence……………………………...….54
A Stimulus Set for Testing Positive Perceptual Affect………………...57
Calibration Experiment……………………………………...…… 57
Stimuli……………………………………......................... 58
Subjects…………………………………………............... 58
Method………………………………………………..…… 59
Results………………………………………………….…. 61

IV. REPETITION……………………………………………………………….….65

Experiment 1: Effects of Repetition, Between-Subjects Design…….. 66
Methods………………………………………………………….... 66
Subjects………………………………………………….... 66
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Stimuli……………………………………………………... 67
Procedure…………………………………………….…… 67
Results…………………………………………………………..… 68
Conclusions from Experiment 1……………………………....... 73
Experiment 2: Selection of Image Sets for FMRI…………………...…7 5
Methods………………………………………………………….... 76
Subjects………………………………………………….... 76
Stimuli……………………………………………………... 76
Procedure…………………………………………………. 79
Results………………………………………………………….…. 80
Conclusions from Experiment 2……………………………….... 88

V. ENVIRONMENTAL UTILITY……………………………………………….... 90

An Evolutionary Account of Scene Preference……………………….. 90
Experiment 3: Predicting Scene Preference from
Environmental Utility……………………………………………………. 99
Methods………………………………………………………….... 99
Stimuli……………………………………………………… 99
Subjects………………………………………………..… 100
Procedure……………………………………………...… 101
Results………………………………………………………...…. 106
Individual Factor Results……………………………..…106
Consistency Results………………………………….… 113
Multiple Regression Results…………………………....114
Conclusions & Discussion………………………………………117
Biophilia and the Preference for Natural
Environments…………………………………………...120

VI. FMRI INVESTIGATION OF SCENE PREFERENCE……….………….. 125

Background……………………………………………………………… 125
Experiment 4: Neural Correlates of Perceptual Affect……………… 131
Methods………………………………………………………….. 132
Subjects………………………………………………..… 132
Apparatus………………………………………………... 132
Imaging Protocol………………………………………... 133
Preference Experiment………………...………………. 135
Procedures…………………………………….… 135
Trial Sequence………………………………….. 137
LOC Localizer…………………………………………… 139
Procedures………………………………….…… 139
Analysis and Results……………………………………...……. 140
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Single Subject Analysis Procedure………………...…. 140
Group Analysis: Whole Brain Voxelwise ANOVA…… 143
Group Analysis: Cluster-Level Significance……..…… 150
Selection of a Subset of Subjects……………...158
Group Analysis: Regions of Interest ANOVA…………163
Individual Differences in Activation Patterns………….169
Repetition “Rebound”……………………………….... 172
Image-Based Correlation Analysis……………………. 173
Conclusions ……………………………………………………. 181

VII. CONTRIBUTIONS AND FUTURE DIRECTIONS…………………..….. 184

Contributions…………………………………………………………….. 184
Future Directions………………………………………………………... 186
Behavioral Studies…………………………………………….... 187
Eyetracking……………………………………………………….188
Individual Differences in the Neural Correlates of
Preference…..………………………...……………………….. 189
Pharmacology…………………………………………...……….190
Natural vs. Urban: Distribution of Image Factors in the
Ventral Visual Pathway……...…………………………..…….190
Learning Effects…………………………………………….……192
Cross-Modal and Conceptual Priming………………………... 193
Direct Measures of Preference………………………………... 194

BIBLIOGRAPHY………………………………………………………………... 195
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LIST OF TABLES

Table 4.1. Correlation of Average Preference Ratings for Experiments
2a and 2b………………………………………………………………………..… 86

Table 5.1. Factor Summary Statistics……………………………………...... 107

Table 5.2. Consistency of Factor Ratings…………………………………… 113

Table 5.3. Correlates of Preference: Multiple Regression Results……….. 114

Table 5.4. Correlates of Preference: Part Correlations for Each Model…..116

Table 6.1. Summary of BOLD Effects: Preference and Repetition……..…162

Table 6.2. Correlation with Preference by ROI……………………………... 179