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The emotional arousal effects on visual processing

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The emotional arousal effects on visual processing

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Content 1

THE EMOTIONAL AROUSAL EFFECTS ON VISUAL PROCESSING

By

Tae-Ho Lee

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

May 2015

Copyright 2015 Tae-Ho Lee
2

DEDICATION

This dissertaion is dedicated to my wife, Young-In Choi, and my son, Dong-
Ha Lee, for always being there for me.

3

ACKNOWLEDGEMENTS
I am very grateful to my committee members: Dr. Mara Mather, Dr.
Laurent Itti, Dr. John Monterosso and Dr. Darby Saxbe. Most of all, I must
thank my advisor Dr. Mara Mather, for her consistent support,
encouragement, and direction during my time at USC. She has influenced
me in a great number of ways. Without her, this disseration would not have
been possible.
I also want to express my deepest appeciation to Dr. Seung-Lark Lim
who always leads me along the right path and wraps me in his warm (but
cynical) embrace.
I would like to thank Dr. Steven G. Greening for his great patience
with my grumpiness! (as well as his support and advice on different aspects
of my projects). Also, I wish to express my thanks to Dr. Seon-Gyu Ko for
her encouragement during my times of trouble. I appreciate the support I
received from the members of the Mather Lab and my past advisors, Dr.
Changyil Ahn, Dr. Hyun-Taek Kim, Dr. June-Seek Choi, and Dr. Yang-
Seok Cho, in Korea University. Thanks to RAINZZOs as well; GoldBex
정우석
,
Astro
이장형
, Petalist
정효 민
and Xr302
원성혁
.
Finally, I thank my parents, Chin-Hong Lee and Myoung-Hee Lee, for
their love.

4

TABLE OF CONTENTS
Dedication 2
Acknowledgements 3
List of Figures 5
List of Tables 6
Abstract 7
Chapter 1: Introduction 8
1.1. Background 8
1.2. Arousal-Biased Competition Theory 10
1.3. The goal of the study 13
Chapter 2: Evidence for arousal-biased competition in perceptual learning 15
2.1. Study overview 15
2.2. Method 17
2.3. Results 19
2.4. Discussion 24
Chapter 3: Emotional arousal amplifies the effects of biased competition in the
brain 29
3.1. Study overview 29
3.2. Method 31
3.3. Results 39
3.4. Discussion 45
Chpater 4: The effect of emotional arousal on biased-competition 49
4.1. Study overview 49
4.2. Method 51
4.3. Results 58
4.4. Discussion 62
Chapter 5: Conclusion
65
References 69
Appendix A: Chapter 2. Estimated individual patameters instead of the nested model testing 98
Appendix B: Chapter 3. Whole-brain analysis of dot-probe task 99
Appendix C: Chapter 4. Comparison between standard scoring and GLM method in SCR analyses 101
Appendix D: Chapter 4. Reaction times as a function of salient target image 102
Appendix E: Chapter 4. PPA ROI for each hemisphere 103
Appendix F: Chapter 4. ROI analyses in the LOC 104
Appendix G: Chapter 4. Functional connectivity results 105
5

LIST OF FIGURES
Figure 1: Chapter 2. Stimulus examples in different task-difficulties and task paradigm 78
Figure 2: Chapter 2. Averaged “target” responses for each orientation in the probe trials 79
Figure 3: Chapter 2. Estimated tuning curves for averaged “target” responses 80
Figure 4: Chapter 2. Averaged median learning-trial response times 81
Figure 5: Chapter 3. Schematic illustration of fear-conditioning and dot-probe task 82
Figure 6: Chapter 3. SCR and whole-brain analysis during the fear-conditioning session;
averaged reaction times in the dot-probe session
83
Fgiure 7: Chpater 3. ROI (FFA and PPA) and functional connectivity results 84
Figure 8: Chapter 3. ROI (IPS) and scatter plot of difference values in IPS and FFA/PPA 85
Figure 9: Chapter 4. Schematic illustration of the spatial detection task and stimulus examples 86
Figure 10: Chapter 4. Individual PPA mask locations 87
Figure 11: Chapter 4. SCR, pupil diamter change and whole-brain analysis results during the
fear-conditioning session
88
Figire 12: Chapter 4. Reaction time, SCR and pupil diameter change results during the spatial
detection task
89
Figure 13: ROI (PPA), scatter plot of differeces values in SCR and PPA, and functional
connectivity analysis results
90
6

LIST OF TABLES
Table 1: Chapter 2. Parameters of the betst fit for averaged “target” response 92
Table 2: Chapter 3. Whole-brain significant clusters and locations of local maxima during the
fear conditioning session
93
Table 3: Chapter 3. Brain regions showing connectivity with the fusiform face area (FFA)
seed region during the dot-probe session
94
Table 4: Chapter 4. Results from hierarchical linear regression analyses 95
Table 5: Chapter 4. Whole-brain significant clusters and locations of local maxima during the
fear conditioning session
96

7

ABSTRACT

The carry-over effects of emotional arousal on visual processing are not
consistent across studies. For example, whereas some studies reveal emotion-induced
enhancement of subsequent visual perception (e.g., Becker, 2009; Phelps, Ling, &
Carrasco, 2006), others reveal impairment due to emotion (e.g., Bocanegra & Zeelenberg,
2009; Most, Chun, Johnson, & Kiehl, 2006). These studies suggest that emotion does not
uniformly influence visual processing, and highlight the unknown nature of how and why
emotional arousal can sometimes enhance and sometimes impair cognitive processing.
The arousal-biased competition (ABC: Mather & Sutherland, 2011) model has
been proposed to reconcile the inconsistencies encountered across studies in the existing
literature that examine the impact of emotional arousal effect on subsequent visual
processing. The ABC theory suggests that stimulus priority, determined by either both
bottom-up saliency (e.g., stimuli that move suddenly or are brighter than their
surroundings) or top-down goal relevancy (e.g., finding a friend in a crowd), is a
fundamental factor that moderates emotion’s carry-over effects (i.e., enhancement and
impairment) in visual processing.
The primary aim of this dissertation was to investigate whether and how the
priority level of visual stimuli interact with emotional arousal, and modulates subsequent
visual processing such as perception and attention based on the ABC model. In three
studies, it was found increased and augmented visual processing for prioritized stimulus
with elevated emotional arousal level, meanwhile decreasing visual processing for non-
prioritized one (i.e., ABC effects). In other words, emotional arousal affects the strength
of visual processing differently depending on the priority level of information. Together,
these studies not only report empirical evidence for the ABC model in both behavioral
and neural level, but also provide insight into how our visual process is modulated by
emotional arousal in the environment.
8

CHAPTER 1. INTRODUCTION

1.1. Background

Events or stimuli charged with emotional meaning stand out. For example,
emotional faces, emotional words and emotional scenes are more likely to be detected
(Ö hman, Flykt, & Esteves, 2001) and remembered (Kensinger & Corkin, 2003; Mather &
Nesmith, 2008) than neutral ones. This preferential processing for emotional information
is considered an adaptive strategy for survival and well-being (Ö hman et al., 2001).
More recent evidence indicates that emotion’s influence extends to visual
processes such as early perception and attentional selectivity as well. For instance,
Phelps, Ling, and Carrasco (2006) showed that presenting a fearful face rather than a
neutral face could make a subsequent neutral stimulus (e.g., a Gabor patch) more easily
perceived even at low contrast levels. Also, other studies that used different types of
visual tasks also reported that presenting task-irrelevant arousing cues enhanced
subsequent visual processing, as a result of increased vigilance in visual attention when
following the target (Becker, 2009; Olatunji, Ciesielski, Armstrong, & Zald, 2011;
Quinlan & Johnson, 2011). For example, Becker (2009) concluded that presenting a
fearful face cue prior to a neutral target among distracters (i.e. search array) increases
target detection efficiency, indicating that antecedent emotional arousal can increase
attentional selectivity for task-relevant information (i.e., target). Consistent with
behavioral studies, imaging studies have also found that emotionally arousing stimuli
increase subsequent visual cortex activation (Damaraju, Huang, Barrett, & Pessoa, 2009;
Kosslyn et al., 1996; Lim, Padmala, & Pessoa, 2009; Morris et al., 1998; Padmala &
Pessoa, 2008; but see , Phan, Wager, Taylor, & Liberzon, 2002). Even subliminal
9

presentation of arousing stimuli can lead to greater activity in primary visual cortex (T.-H.
Lee, Lim, Lee, & Choi, 2009; Pegna, Landis, & Khateb, 2008).
These research findings have led to the supposition that “core neuronal arousal in
the brain… involves a network incorporating primary visual areas, somatosensory,
implicit memory, and conflict monitoring regions” (Brooks et al., 2012, p. 2962).
Enhanced activity has also been found in the primary auditory cortex in response to
emotional sounds, leading to the idea that, under arousal, “increased activation within
primary areas might contribute to efficient processing of behaviorally relevant
information across different sensory modalities” (Ethofer et al., 2012, p. 196). In sum, a
favored notion in the field is that emotional arousal potentiates activity in sensory regions
via the amygdala, which in turn enhances subsequent visual processing (for more details,
see Phelps et al., 2006).
However, the ‘enhancement-only’ perspective reviewed above does not consider
how arousal might also impair processing, despite much behavioral evidence that arousal
can impair perceptual processing (see Mather & Sutherland, 2011). For example, studies
have shown that arousal impairs visual processing of subsequently presented neutral
targets rather than inducing enhancement. Much of this evidence on the emotion-induced
impairment in visual processing comes from studies that use the rapid serial visual
presentation paradigm (RSVP) (Ciesielski, Armstrong, Zald, & Olatunji, 2010; Most et
al., 2006; Most, Chun, Widders, & Zald, 2005; Most, Smith, Cooter, Levy, & Zald, 2007
Levy, & Zald, 2007; Smith, Most, Newsome, & Zald, 2006 & Zald, 2006). In RSVP,
relative to neutral distracter, inserting a task-irrelevant arousing distracter such as a fear-
conditioned stimulus (Smith et al., 2006) or positive and negative stimulus (Ciesielski et
al., 2010; Most et al., 2007) lead to increased impairments when identifying subsequently
presented target stimuli, a phenomenon known as emotion-induced blindness (Bocanegra
& Zeelenberg, 2009; Most et al., 2005).
10

One possible mechanism of the arousal-induced visual deficit is that the presence
of an emotional stimulus draws available resources largely than a non-emotional one,
which reduces the amount of resources available to processes temporally and spatially
adjacent stimuli thereby leading to impairment. In other words, if a distracter stimulus is
charged with emotional meanings, more visual resources would be prioritized to said
stimulus relative to a non-emotional, perceptually similar, stimulus (A. K. Anderson &
Phelps, 2001; Barnard, Ramponi, Battye, & Mackintosh, 2005; Lang, Bradley, &
Cuthbert, 1997; T.-H. Lee, Lim, Lee, Kim, & Choi, 2009; Ö hman et al., 2001;
Vuilleumier, 2002, 2005; Vuilleumier & Huang, 2009). In this manner a resource
competition between the non-emotional target stimulus and the emotional distracter
stimulus leads to increased interference of the ongoing goal-relevant processing (Mather,
2007; see also Most & Wang, 2011; Pourtois, Schettino, & Vuilleumier, 2012;
Vuilleumier & Driver, 2007). Indeed, recent research findings found that both emotional
arousal induced-enhancement and impairment in visual processing can be introduced
simultaneously in the same study depending on the level of resource competition between
emotionally arousing distracters and neutral targets (Bocanegra & Zeelenberg, 2009;
Ciesielski et al., 2010; Most & Wang, 2011).
In summary, recent studies have shown that emotion’s influence carries over to
spatially or temporally adjacent non-emotional stimuli. However, it is not clear from
these previous studies how and why emotional can produce these opposing effects on
visual processing.

1.2. Arousal-biased competition theory

To acknowledge contradictory findings in the literature and evaluate the
independent role of emotional arousal in visual processing as well as other cognitive
11

process such as memory, Mather and Sutherland (2011) introduced a new theory, called
the arousal-biased competition theory (ABC theory). The ABC theory provides a more
general account of how and why emotional arousal can enhance or impair subsequent
visual processing as well as other cognitive process such as memory.
The ABC theory is based on biased-competition theory (Bundesen, 1990; G. Deco
& Rolls, 2005; Desimone & Duncan, 1995; Itti, Koch, & Niebur, 1998; Miller & Cohen,
2001; Vecera & Farah, 1994). The basic idea of the biased-competition theory is that our
information processing is very selective due to limited resources. Therefore, multiple
stimuli in the visual field always compete with one another for the same limited resources,
in order to be selected and perceived actively. By doing so, important information can be
processed more properly (Bundesen, 1990; Desimone & Duncan, 1995). In other words,
our selective processing for specific information reflects competitive processes that are
biased in favor of important information (i.e., high-priority stimuli) at the expense of
trivial information (i.e., low-priority stimuli) (Itti & Koch, 2000; Itti et al., 1998). There
are several different computational accounts for biased competition, such as object-based
(e.g., Vecera & Farah, 1994), location-based, (e.g., Desimone & Duncan, 1995) and
interactive model based (Gustavo Deco & Lee, 2002). In addition, recent research on
visual attention argues such that emotional significance leads to attentional gain to itself
without competitive processing (e.g., Wieser, McTeague, & Keil, 2011) and the biased
visual attention to a certain information could happen even in the absence of actual
competing information (e.g., Keitel, Andersen, Quigley, & Müller, 2013). Although there
are different ideas and debates about mechanisms of attentional gain, the importance of
biased competition theory is that when certain stimulus dominate one part of the network,
aspects of its representation will be strengthened elsewhere in the brain (Duncan, 2006).
There are two important factors, which can bias the competition and influence
information processing: bottom-up saliency and top-down goals.
12

A bottom-up saliency stimulus that move suddenly or brighter than their
surroundings, helps to determine which information is dominant among information (e.g.,
Baluch & Itti, 2011; Beck & Kastner, 2009; Fecteau & Munoz, 2006). A computational
model known as the saliency map (Itti & Koch, 2000; Itti et al., 1998) explains how
perceptually salient stimulus (or its location) draws our attention and continues into the
ongoing process; incoming information is first analyzed by early visual neurons to
represent the perceptual contrast at each location for varieties of elementary visual
features (e.g., luminance, color, orientation, motion, etc.). Then, computed values across
visual feature maps determine which stimulus (or its location) is the most predominant
(the most salient) across the visual map. Once the most salient target is determined,
stimuli compete for activation via a center-surround competitive process in which
excitation at the salient location leads to further excitation while suppressing its
surrounding stimuli within each of the feature maps. Finally, the involuntary attentional
focusing occurs for the most dominant stimulus.
In addition to bottom-up saliency, top-down relevancy such as finding a friend in
a crowd (i.e. task-specific goal) is another factor that helps win other stimuli (Chelazzi,
Duncan, Miller, & Desimone, 1998; Dehaene, Kerszberg, & Changeux, 1998; Gazzaley
et al., 2007; Hung, Driver, & Walsh, 2005; Kastner, De Weerd, Desimone, & Ungerleider,
1998; Miller & Cohen, 2001; Pessoa, Kastner, & Ungerleider, 2003). However, the
bottom-up saliency and top-down goal are neither mutually exclusive nor fully
independent factors; they can interact to determine the priority of stimulus (Folk,
Remington, & Johnston, 1992).
Building upon the biased competition process, the ABC theory proposes that
biased-competition processes could be intensified by emotional arousal (whether elicited
by external stimuli, internal motivations, or stress hormones) when there is one stimulus
with high priority (either in a way of bottom-up saliency or top-down relevancy) because
stimuli will gain even more resources under arousing conditions than it would otherwise.
13

The ABC theory thus makes a specific prediction: arousal modulates the ongoing
competitive processes of mental representation leading to simultaneous outcomes of
enhanced mental representation of high priority stimuli (“winner-take-more”) and
impaired processing of less priority stimuli (“loser-take-less”). When a person becomes
emotionally aroused, any high priority information will be more strongly perceived and
represented across information processing stages, while inducing more inhibitory process
for non-priority information. In other words, the ABC theory hypothesizes that emotional
arousal does not enhance information processing indiscriminately, but that emotional
arousal should modulate information processing differently, depending on whether those
stimuli are winners or losers in the current competition among stimuli.
Initial behavioral memory experiments support this ABC hypothesis. Sutherland
and Mather (2011) found that when participants were asked to report as many letters as
they could from a briefly flashed array of letters, they were more likely to report
perceptually salient letters (those presented in dark grey against the white background) if
they had just heard an arousing sound and less likely to report non-salient letters (those
presented in light grey) than if they had just heard a neutral sound. Similarly, recent
studies on long-term memory also found that emotional arousal induced by negative and
positive stimulus leads to both enhancement and impairment of memory consolidation at
the same time, depending on target stimulus’ priority (Sakaki, Fryer, & Mather, 2013).

1.3. Goal of the current study

The general aim of my studies was to test the predictions that stimulus priority,
as detetmined by either bottom-up saliency and/or top-down goal relevance, is a
fundamental factor in producing both emotional arousal induced enhancements and
impairments in visual processing, as suggested by ABC theory (Mather & Sutherland,
14

2011). Thus, the hypothesis is that emotional arousal does not enhance visual processing
indiscriminately. Instead, emotional arousal should modulate visual perception differently
depending on whether those stimuli are dominating the current competition among
stimuli or not.
Given the research goal, three studies were conducted. The study in Chapter 2
tests the prediction that ABC works in basic visual processing using a psychophysical
perceptual learning paradigm in which the target stimulus was followed by either
arousing- and non-arousing cue images. The study in Chapter 3 describes a functional
magnetic resonance imaging (fMRI) study that focused on neural underpinnings of ABC
during a dot-probe task in which two task-irrelevant visual cues were presented
simultaneously with different saliency levels. The study in Chapter 4 was conducted to
follow up on fMRI study in Chapter 3 with different visual attention task. Together, these
studies not only report empirical evidence for the ABC model in both behavioral and
neural level, but also provide insight into how our visual processing is affected by
emotional arousal.

15

CHAPTER 2

EVIDENCE FOR AROUSAL-BIASED COMPETITION
IN PERCEPTUAL LEARNING
1

2.1. Study Overview

In the current study, the ABC prediction in the domain of visual search was
tested by examining how arousal affects perceptual learning of salient targets versus non-
salient targets. The general outline of a procedure was adapted from a previous study
(Navalpakkam & Itti, 2007). In the current version of the search task, there were both low
and high salience target conditions, and both arousing and non-arousing sessions. During
the learning trials of the task, participants were trained to detect a target line oriented at
55° among 24 distracters oriented either at 50° (in the low salience condition) or at 80°
(in the high salience condition). To test learning of the tilt of the target line, probe trials
were interspersed in a random manner between learning trials. The probe trials had five
different lines in a circular array, and participants’ task was to find the target line. To
investigate the effects of arousal on learning performance, negative arousing or neutral
non-arousing pictures were presented before stimuli arrays in the learning phase.
The prediction was that there would be arousal effects on perceptual
representations as a function of target prominence and the competitive processes
enhancing high salience stimuli and suppressing low salience stimuli. Specifically, in the
high-salience condition, the prediction was that experiencing arousal would enhance
perceptual learning of the highly salient target features. As competitive processes

1
This study has been published; Lee, Itti & Mather (2012), “Evidence for arousal-biased competition in perceptual
learning”, Front. Psychology, 3(241). Minor modifications have been made from the published version.
16

between stimuli representations influence the variability or noise in the perceptual
representations as well as their signal strength (Ling & Blake, 2009), the enhanced
perceptual learning would be evident in decreased noise in the tuning curves (evident in
decreased bandwidth of the curves) as well as in increased amplitude of the tuning curves
at the correct 55° point. In contrast, in the low-salience condition, arousal would impair
learning target features, decreasing amplitude and increasing noise.
In addition, in the low-salience condition, Navalpakkam and Itti (2007)
documented an interesting phenomenon they called “optimal feature gain,” in which the
neural tuning curve that represents the target is shifted away from the distracter features,
when the target and distracters are similar. Thus, for instance, when the participants
completed visual search trials in which the target was a 55° line seen among 50°
distracters, Navalpakkam and Itti found that the peak amplitude of participants’ tuning
curves for the target was not 55°, as might be expected, but instead was shifted to 60°.
This shift in representation away from the distracter optimizes discrimination because the
55° target and 50° distracter now fell on a region of the tuning curve that has a higher
slope than the peak of the curve, and similar stimuli are most easily discriminated in
high-slope regions of a tuning curve. However, this discrimination advantage for high-
slope regions of tuning curves disappears with increasing noise level in the representation
(Butts & Goldman, 2006). Thus, given the prediction that arousal would make it harder to
distinguish the non-salient target from its distracters because of increased noise in the
tuning curve for the target, arousal should also reduce the likelihood that participants will
show ”optimal feature gain” in the low-salience condition.

17

2.2. Method

2.2.1. Observers and psychophysical sessions
Twenty observers (10 males, 10 females; ages 25–36) with corrected-to-normal
vision volunteered for this study and gave informed consent. Observers were naï ve to the
purpose of the experiment (except one, TL). Ten (six males and four females) were
assigned to the high-salience and the other ten (four males and six females) to the low-
salience condition. For each salience type, observers completed two emotion sessions
(arousing and non-arousing) in a counterbalanced order. Participants provided written
informed consent approved by the University of Southern California (USC) Institutional
Review Board and were paid for their participation.

2.2.2. Stimuli and Apparatus
Line stimuli consisted of five types of line orientation (30°, 50°, 55°, 60° and
80°). The images used in the learning trials (32 negative images for the arousing session
and 32 neutral images for the non-arousing session) and the additional images used in the
subsequent memory task (32 negative and 32 neutral) were selected from the
International Affective Pictures System (IAPS; Lang, Bradely, & Cuthbert, 1999) and the
Mather and Nesmith stimulus set (Mather & Nesmith, 2008). Nine additional participants
rated the images for arousal (on a scale of 1 = calm to 9 = arousing) and valence (on a
scale of 1 = unpleasant to 9 = pleasant). The 32 negative images had more negative
valence and higher arousal ratings (M
valence
= 1.97, SE
valence
= .38; M
arousal
= 7.77, SE
arousal
= .41) than the 32 non-arousing images (M
valence
= 5.45, SE
valence
= .33; M
arousal
=
1.88, SE
arousal
= .38). The size of each line stimulus and emotional images corresponded
to 1.5° × 0.6° and 30.5° × 22.5° visual angles, respectively. Based on the salience type
assigned to observers, the target line was presented among distracters tilted either 80° or
18

50° (Figure 1A). The stimuli were displayed on a 19-inch CRT monitor with a refresh
rate of 100 Hz. All observers were tested individually in a soundproof room, seated
approximately 65 cm away from a screen, using a chin-rest.

2.2.3. Procedure
Observers performed both learning trials and probe trials. Every so often, after a
random number of learning trials, knowledge about the target was measured in a probe
trial. Learning trials proceeded as follows: (A) A 500-ms fixation cross display; (B) a 200
ms-emotional picture; (C) a 1000 ms blank screen; (D) a search array containing one
target (55°) among 24 distracters (Figure 1B). To manipulate observers’ arousal levels
during the session, emotional pictures were presented in an approximately 60% partial
schedule in both the arousing and non-arousing sessions. In trials without a picture, the
search display was presented right after the first 500-ms fixation event.
Observers were instructed to find the target (55°) and press any key. To verify
that observers indeed found the target on every trial, following the key press, a grid of
fine-print numbers appeared briefly (300 ms) and observers were asked to report the
number at the target’s location. Feedback (“correct” or “incorrect”) on performance was
given after each trial. After a random number of learning trials, a probe trial was
presented. The probe trial consisted of a 500-ms fixation display, followed by a 500-ms
display of five items representing five lines (30°, 50°, 55°, 60°, and 80°) within a 6.0° ×
6.0° rectangular box, and then by a 300-ms display of five fine-print random numbers.
The task was the same as in the learning trials. Observers were asked to report the
number at the target location. Observers first completed 14 trials in a practice session,
followed by the main task phase. Both sessions started with these practice trials and in
both cases, no emotional pictures were shown during the practice session.
The line-search task consisted of ten 50-trial blocks (each with 34 learning trials
and 16 probe trials). Each observer performed the task with 160 probe trials randomly
19

presented in between 340 learning trials for each session (arousing and non-arousing).
Thus, 1000 trials (2 emotion sessions × 50 trials × 10 blocks) were administered for each
observer. Each observer saw either all low-salience or all high-salience targets. Observers
were allowed to take a break in between blocks. The order of emotion sessions was
randomly assigned across the observers. To avoid learning effects across sessions for the
target line, two different orientations (original and reversed) were adopted. For example,
when the observer performed and completed the first session with the original orientation
(e.g., 55°), the second session was administered with the reversed orientation (e.g., 125°).
Immediately after each session, observers performed a recognition memory task as a
manipulation check that they processed the pictures. For the recognition task, a randomly
selected half of the main task images served as old items intermixed with 16 new images.
The old and new items were presented in a random order and the observer was asked to
indicate “old” or “new” for each image.

2.3. Results

2.3.1. Probe trial performance.
The ability to correctly recognize the exact tilt of the target line in each of the
conditions was fist examined. For each observer, the percentage of “target” responses on
probe

trials was calculated for each orientation (30°, 50°, 55°, 60° and 80°) separately for
each emotion session (Figure 2). These were analyzed with salience type (2: high- and
low salience) as between-subject variables, and session (2: arousing and non-arousing)
and orientation (5: 30°, 50°, 55°, 60° and 80°) as within-subject variables. There was a
significant main effect of orientation, F (4, 72) = 145.20, p < .001, η
2
= .89, and a salience
× emotion × orientation interaction, F (4, 72) = 3.90, p < .01, η
2
= .18. Subsequent
simple-effects analyses for each salience type across the two session types revealed that,
in the high-salience condition, selecting the correct target (i.e., percent 55° responses)
20

was significantly greater in the arousing condition than in the non-arousing condition (p <
.05). In contrast, in the low-salience condition, emotion condition did not significantly
affect the percent of responses identifying the correct target. However, the arousing
condition led to a significant decrease in selecting the 60° target (or its corresponding
opposite line in the flipped condition; p < .001) in the low-salience condition.
To understand the nature of these results better, each observer’s tuning curve was
estimated to fit responses from each emotion session via a Gaussian function known to be
well represented in tuning curves:

𝑓 (𝑥 ) = 𝑎 𝑒 −(𝑥 −𝜇 )
2
2𝜎 2
(Equation 1)

where a represents response amplitude (i.e. the height of the curve’s peak), μ specifies the
position of the center of the peak, and σ is the bandwidth (i.e. standard deviation of the
curve). The goodness of fit was evaluated by the r
2
for each arousing condition and non-
arousing condition:

𝑟 2
= 1.0 −
∑(y
𝒊 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 −y
𝒊 𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑 )
2
∑[y
𝒊 𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑 −mean(y
𝒊 𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑 )]
2
(Equation 2)

To evaluate the curve fit model using the parametric values (i.e., a, μ and σ) for
each condition, a nested model testing (separate fits for each emotion condition vs. one fit
for both conditions collapsed together) was applied. Specifically, an F-test for nested
models was used to statistically compare the models based on the averaged r
2
s for the
arousing and non-arousing conditions. For two nested models with k
full
and k
reduced

parameters, the F statistic is defined as:

21

𝐹 (𝑑𝑓 1
, 𝑑𝑓 2
) =
(𝑟 𝑓𝑢𝑙𝑙
2
−𝑟 𝑟𝑒𝑑𝑢𝑐𝑒𝑑
2
)/ 𝑑𝑓
1
(1−𝑟 𝑓𝑢𝑙𝑙
2
)/ 𝑑𝑓
2
(Equation 3)

where df
1
= k
full
- k
reduced,
and df
2
= N - k
full
; N is the number of data points. All these
procedures were performed using the GraphPad Prism version 5.04 for Windows
(GraphPad Prism Software, La Jolla, CA; see also Motulsky & Christopoulos, 2004).
As illustrated in Figure 3, estimated tuning curves for the averaged “target”
responses across all observers revealed that emotional arousal modulated response
patterns differently depending on salience. When the target was conspicuous among
distracters (i.e., high-salience condition), arousal enhanced the accuracy and strength of
the target’s representation; this was evident in the decreased bandwidth, F (1, 94) = 4.91,
p < .05, and increased amplitude, F (1, 94) = 4.71, p < .05. On the contrary, when target
salience was low, arousal widened the tuning curve leading to specificity loss. This was
evidenced by increased bandwidth, F (1, 94) = 8.86, p < .005, and decreased amplitude, F
(1, 94) = 13.85, p < .0005. The position of the peak amplitude also shifted, F (1, 94) =
7.03, p < .01. This shift in the position of the peak amplitude indicated that when target
salience was low, arousal also disrupted the “optimal feature gain” exaggeration of
target-distracter differences seen in the non-arousing condition and in a previous study
not involving emotion (Navalpakkam & Itti, 2007). The parameters of the best fitting
functions are listed in Table 1
2
. In the following sections, the other aspects of behavioral
performance on the task were described.

2.3.2. Memory for the pictures
An ANOVA with salience type (2: high- and low salience) as a between-
observers variable and session type (2: arousing and non-arousing) as a within-observers
variable revealed that observers’ d-prime (d’) values from the picture recognition

2
Individually estimated parameters for each observer were also compared via a repeated-measures ANOVA which
revealed the same pattern of results as nested model testing (Appendix A).
22

memory task were significantly higher in the arousing picture sessions (d’ = 3.37, SE =
.09) than in the non-arousing picture sessions (d’ =2.80, SE = .14), F (1,18) = 8.58, p <
.001, η
2
= .52. There was no significant main effect of salience type and saliency-related
interaction with (both p >.3). Thus, as seen across many previous studies, memory was
better for the emotional pictures than the neutral pictures (for a review, see Reisberg &
Heuer, 2004). For the purposes of the current study, however, the relevant finding was
that participants had similar memory for the pictures across the two salience conditions.

2.3.3. Learning trial performance
Averaged median response times (RTs) for the learning trials were calculated for
each session for both high- and low-salience conditions. A repeated measures ANOVA
on target search latencies was conducted with salience type (2: high- and low salience) as
a between-observers variable, and session type (2: arousing and non-arousing) as a
within-observers variable. Not surprisingly, there was a main effect of salience type, F (1,
18) = 134.17, p < .001, η
2
= .88, with slower response times in the low-salience condition
(M = 1443.45, SE = 132.16) than in the high-salience condition (M = 581.53, SE =
36.37). However, there was no significant main effect of session type and no significant
interaction between the two variables (Figure 4).
Overall, observers had near-ceiling accuracy (M = .977, SE = .005) on the
learning trials. More specifically, in the high salience condition, the averaged correct
ratio was .991 in arousing condition and .988 in non-arousing condition. In the low
salience condition, the mean was .975 in the arousing condition, and .956 in the non-
arousing condition. A repeated-measures ANOVA with salience type (2: high- and low
salience) as a between-observers variable and session type (2: arousing and non-arousing)
as a within-observers variable revealed that there was a main effect of session type, F (1,
18) = 11.12, p < .005, η
2
= .38, and a main effect of salience type, F (1, 18) = 10.27, p <
.005, η
2
= .36. There was an interaction with salience type, F (1, 18) = 5.63, p < .05, η
2
=
23

.24. Subsequent simple-effects analyses for each salience type across the two session
types revealed that, in the low-salience condition, the correct ratio was significantly
greater in the arousing condition than in the non-arousing condition (p < .05). In contrast,
in the high-salience condition, emotion condition did not significantly affect the percent
of responses identifying the correct target (p > .1). However, it is not clear if this
interaction is simply an artifact of the near-ceiling accuracy in the high-salience
condition, as the near-perfect accuracy in this condition may have diminished the effects
of arousal on accuracy (which appear to be in the direction of enhancing performance, as
in the low salience condition). In summary, arousal generally increased accuracy in the
search task, even in the low salience condition in which arousal impaired perceptual
learning about the target.

2.3.4. Comparing learning trials preceded by pictures to those not preceded by pictures
In the current study, although emotion type was manipulated across sessions, a
picture was not presented on every trial within each session. To provide more information
about whether the presence or absence of an image on a particular learning trial mattered
for the speed of the response, a follow-up ANOVA with salience type (2: high- and low
salience) as between-observer variables, and image presence (2: image present before
visual search and image absent) and session (2: arousing and non-arousing) as within-
observer variables was conducted on the learning phase median RT as the dependent
variable. There was a significant interaction of image presence (2: presence and absent)
and salience condition (2: high and low salience), F (1, 18) = 8.00, p < .05, η
2
= .31.
However, there was no session main effect, F (1, 18) = .84, n.s., nor any interactions with
session (Ps > 0.4). To clarify the nature of the image presence and salience condition
interaction, a separate repeated measures ANOVA for each salience condition with image
presence as a factor was conducted. There were no statistically significant effects in the
high salience condition (Ps > .01). In contrast, in the low salience condition, there was a
24

main effect of image presence, F (1, 9) = 11.06, p < .01, η
2
= .55. In this condition, the
RT was generally slower with an image absent (M = 1592.90 ms, SE = 199.05) than
present (M = 1345 ms, SE = 147.49) regardless of emotion condition. However, there was
no interaction or main effect of session. Thus, in addition to not detecting session
differences in reaction times during the learning phase, trial-by-trial differences in
reaction time based on whether the picture was emotional or not were not found –
indicating emotion did not significantly influence response speed in the learning trials.

2.4. Discussion

The current study tested the effects of arousal in visual perception based on the
ABC prediction that arousing stimuli increase the effects of competition among stimuli.
In the high-salience condition, the target line was tilted 55° and the distracter lines were
tilted 80°. In this type of visual display, the target had high perceptual contrast with the
surrounding stimuli and so center-surround competition should increase the perceptual
salience of the target compared with its surrounding stimuli (Itti & Koch, 2000). Arousal-
biased competition theory (Mather & Sutherland, 2011) predicts that arousal should
further increase the activation of this perceptual “winner,” making it more precisely
represented and encoded.
In the high salience condition, when asked to identify which of five alternative
lines was the target discrepant line in the visual search trials, in both the arousing and
non-arousing sessions participants were most likely to select the correct 55° tilted line.
However, in the arousing session, participants were significantly more likely to select the
correct 55° option than the other options, leading to an higher amplitude and a lower
bandwidth for their psychophysical tuning curve representing the target line tilt. In the
low-salience condition, the target line (tilted 55°) and the distracter lines (tilted 50°) were
25

similar. In this situation of competition between stimuli with similar perceptual contrast,
center-surround competition mechanisms should mutually inhibit both target and
distracter locations (Itti & Koch, 2000). If, as predicted by arousal-biased competition,
arousal amplifies the effects of these competition processes, then learning of low-salience
targets should be worse under arousing than non-arousing situations. Consistent with
these predictions, in the arousing sessions, observers learned the target line tilt less
precisely than in the non-arousing sessions. Thus, emotional arousal had opposite effects
on perceptual learning of salient and non-salient stimuli. Previous research indicates that
competitive processes in binocular rivalry lead not only to relative differences in signal
strength between the dominant and suppressed stimuli, but also to less noise in the
representation of the dominant stimulus than in the representation of the suppressed
stimulus (Ling & Blake, 2009). Consistent with this, in the current study, arousal
decreased the noise in the tuning curves of salient stimuli but increased it for non-salient
stimuli.
Previous studies have shown that if people see emotionally arousing pictures
while they are trying to remember several neutral stimuli, they are less able to recognize
the neutral stimuli at the end of the trial (Dolcos, Kragel, Wang, & McCarthy, 2006;
Dolcos & McCarthy, 2006). However, impaired working memory between learning and
probe trials cannot account for the present findings, as in the high salience condition,
arousal enhanced memory for the target line. Instead, arousal-biased competition
provides a framework to account for when arousal will impair working memory and
when it will enhance it. The prediction is that arousal will impair working memory when
multiple equally salient stimuli are competing for representation, such as on working
memory trials with several neutral faces as the memoranda and distracting arousing or
neutral pictures (Dolcos et al., 2006; Dolcos & McCarthy, 2006). Arousal can even
impair memory for associated features of arousing stimuli when the features of multiple
arousing stimuli are competing for representation (Mather et al., 2006; Mitchell, Mather,
26

Johnson, Raye, & Greene, 2006). However, when arousing stimuli compete with neutral
stimuli in an N-back working memory task, the arousing stimuli, which presumably have
higher priority due to both salience and goal-relevance, are remembered better than the
neutral stimuli (Lindstrom & Bohlin, 2011).
Research on perception reveals similar issues regarding how arousing stimuli can
both modulate competition among independent neutral stimuli and also compete directly
against those stimuli. For instance, previous research indicates that arousing stimuli such
as fearful faces can enhance perception of subsequent neutral stimuli (e.g., Padmala &
Pessoa, 2008; Phelps et al., 2006). However, these studies did not evaluate how arousal
affected the competition among more and less salient stimuli. The prediction from the
current study was that arousal would enhance perception only of the most salient stimuli
while impairing perception of less salient stimuli. Nevertheless, a critical issue here, as in
the working memory studies, is that arousing stimuli also compete for representation.
Thus, when pictures are rapidly displayed in a sequence, arousing pictures impair
perception of subsequent targets (Ciesielski et al., 2010; Most et al., 2006; Most et al.,
2005; Most & Junge, 2008; Most et al., 2007; Smith et al., 2006). The timing between a
cue inducing arousal and a subsequent neutral target is critical in determining whether the
arousing cue itself dominates everything else, or whether it can enhance perception of a
salient target. For instance, in one study (Bocanegra & Zeelenberg, 2009), when the
interval between the cue and the target was 50 or 500 ms, participants were less likely to
correctly identify the target when the cue was arousing. However, increasing the interval
to 1000 ms led to enhanced identification of targets following arousing cues. In the
current study, the inter-trial interval was 1000 ms, at which point the arousing stimulus
was no longer in direct competition with subsequent stimuli.
It is interesting that there was any effects of arousing stimuli on response times to
detect the visual search target, whereas two previous studies (Becker, 2009; Olatunji et
al., 2011) found that showing fearful faces 600 ms or immediately before a search array
27

enhanced target detection. Olatunji et al. found that this advantage was specific to fear
face cues and did not appear for anger or disgust face cues. Thus, it may be that the
enhanced search detection is specific to fear and so was not elicited by the mixed
negative emotionally arousing pictures presented in the current study. Furthermore, it is
worth noting that enhanced visual search after fearful face cues was not replicated in
another study (Quinlan & Johnson, 2011). In any case, the fact that there was no
significant effect of arousal on visual search speed rules out the possibility that the
perceptual learning effects in the current study were mediated by target detection speed
differences across emotion conditions. Also, search accuracy did not show arousal-biased
competition effects; instead, arousal seemed to have a general enhancing effect on initial
search accuracy, which may have been due to enhancing effects of arousal on sustained
attention. The lack of arousal-biased competition effects in initial search speed or
accuracy suggests that the differences in perceptual learning induced by emotional
arousal were due to competitive processes acting on representations after target detection.
In this study, there was an additional interesting finding in the low-salience
condition. Here, the visual search parameters were the same as in Navalpakkam and Itti’s
(2007) study, in which they found evidence that, in difficult search without any emotion
induction, people shift their perceptual representation of the target item such that it is less
accurate, but more optimal for discriminating the target from its distracters. Standard
models of attention assume that attention increases the activity of neurons tuned to
respond to the target’s features (Carrasco, 2011). Navalpakkam and Itti modeled
situations in which the target and the distracters are highly similar, such as search for a
55° target among 50° distracters. Their model suggests that boosting activity of neurons
tuned for the exact target feature can be suboptimal when the target and distracters are
very similar. In this case, the optimal strategy is to increase the signal strength of neurons
representing features like the target, but that differ more from the distracters than the
target does. In the case of a 55° target among 50° distracters, this would mean it would be
28

optimal to boost the responsiveness of neurons tuned to respond to 60° lines, as these
neurons should have the steepest part of their tuning curve coincide with the small
differences in the feature value between the target and distracter (see Purushothaman &
Bradley, 2005).
Navalpakkam and Itti confirmed their model in behavioral studies in which
people showed this “optimal feature gain” strategy when learning the features of targets
that were very similar to distracters. This strategy requires relatively sharp tuning curves,
as with broader tuning curves there would be little difference in the tuning curve slope
height at 50° (the distracter) between neurons tuned for 55° and 60° lines. Indeed, other
modeling work indicates that similar stimuli are most easily discriminated in high-slope
regions of the tuning curve only when there are low noise levels in tuning curves (Butts
& Goldman, 2006). In the current study, Navalpakkam and Itti’s “optimal feature gain”
effects were replicated in the non-arousing low-salience condition, such that observers
were more likely to incorrectly identify the target as having a 60° tilt rather than its actual
55° tilt. However, in the arousing condition, representations of the target line were
significantly less shifted away from the distracter tilt, and revealed a significantly broader
tuning curve with lower amplitude. This finding suggests that, in difficult discrimination
tasks, emotional arousal disrupts people’s ability to make subtle shifts in perceptual
representations that optimize discrimination of targets from distracters.

29

CHAPTER 3

EMOTIONAL AROUSAL AMPLIFIES THE EFFECTS OF BIASED
COMPETITION IN THE BRAIN
3

3.1. Study overview

The current study followed up on previous behavioral findings of the Study in
Chapter 2. To focus on the neural underpinnings of the interactions between emotional
arousal and priority on visual processing, functional magnetic resonance imaging (fMRI)
method was adopted. Arousal biased competition (ABC) theory predicts that
enhancement in brain activation seen under arousal should be specific to high priority
stimuli and their locations, with concurrent diminished processing in the brain to non-
priority stimuli.

To test the hypothesis that emotion leads to simultaneous enhancements and
impairments in visual processing, two task-irrelevant visual cues were presented
simultaneously with different saliency levels. Face and place images were used as the two
visual cues based on previous research showing that the fusiform face area (FFA)
responds selectively to faces (Kanwisher, McDermott, & Chun, 1997) and
parahippocampal place area (PPA) responds selectively to spatial place images (Epstein
& Kanwisher, 1998), allowing brain activation in response to each of these cues to be
identified. To differentiate the priority of the two cues, there was always a brief
luminance increase (i.e., a yellow colored frame) in the salient cue’s location. In addition,

3
This study has been published; Lee, Sakaki, Cheng, Velasco & Mather (2014), “Emotional arousal amplifies the
effects of biased competition in the brain”, Social cognitive and affective neuroscience. Minor modifications have been
made from the published version.
30

the current study always used face images as the more salient cues given their intrinsic
evolutionary value (for a review, see Palermo & Rhodes, 2007). During the task,
participants had to identify the location of a green dot target, shown on the same (salient-
location target) or opposite (non-salient-location target) side as the salient cue. As the dot
appeared in the salient and non-salient locations equally often, the salience of the visual
cues was not predictive of the dot location.
Notably, the current study used a fear-conditioned auditory stimulus to avoid the
possible confounding effects (e.g., Zeelenberg & Bocanegra, 2010) of using the same
sensory modality to induce emotional arousal and to measure perception (e.g., inducing
arousal via a visual cue and testing perception via a visual target). Furthermore, low-level
features such as color, luminance, and object salience may vary on average across the two
types of stimuli (arousing and non-arousing stimulus), and it might be those perceptual
qualities that enhance perceptual processing of arousing stimuli, rather than the arousal
per se. For instance, arousing sounds may be louder and arousing pictures may have
brighter colors than neutral pictures (e.g., Delplanque, N’diaye, Scherer, & Grandjean,
2007). One way to eliminate this concern is to use fear conditioning to endow a
previously neutral tone or image with affective meaning. Pessoa and colleagues have
shown that visual stimuli that were previously conditioned to predict a shock elicit
greater amygdala and visual cortex activity, even when no shock occurs on that particular
trial (Lim et al., 2009; Padmala & Pessoa, 2008). These findings rule out perceptual
confounding factors for emotion-potentiated visual processing. The current study thus
avoided possible influences of low-level visual adaptation to the arousal cue on target
processing by using auditory fear-conditioned tones to induce arousal and visual targets
to test visual processing.
Based on the ABC model, the prediction was that arousal induced by the CS+
would lead to stronger visual processing for the salient face cue, as indicated by increased
FFA activation. Simultaneously arousal would reduce processing for the non-salient
31

place cue, as indicated by decreased PPA activation. One tenet of biased competition
theory is that when an object gains dominance within one part of the network, aspects of
its representation will be strengthened elsewhere (Duncan, 2006). Therefore, when an
object dominates competition, it will bias processing to favor other information from the
same location (Szczepanski, Konen, & Kastner, 2010). In the current study, this means
that the perceptual salience of a face stimulus attracting attention to the right side of the
screen should bias processing to favor information appearing in that location and lead to
faster detection of the target when it appeared behind the salient cue than the non-salient
cue. Of particular importance given the hypotheses, this detection advantage should be
greater for trials following CS+ than for trials following CS- tones. The hypotheses were
tested in an initial behavioral experiment in addition to an fMRI experiment.

3.2. Method

3.2.1. Participants
The behavioral experiment involved 52 healthy participants (14 Males, 38
Females; M
age
= 20.50, range = 18 - 30) and the fMRI experiment involved 20 healthy
subjects (9 Males, 11 Females; M
age
= 21.95, range = 18 - 35). All subjects gave
informed consent in accordance with USC Institutional Review Board guidelines.

3.2.2. Stimuli and Apparatus
Two tones (500 Hz and 1500 Hz) were adopted as conditioned stimuli (i.e., CSs)
to avoid possible confounding effects of using stimuli in the same sensory modality to
induce emotional arousal and to measure perceptual processing (e.g., Zeelenberg &
Bocanegra, 2010). To separate the BOLD fMRI response for salient versus non-salient
32

stimuli, face images (known to elicit selective responses in the fusiform face area; 140
female and 140 male) and places images (known to elicit selective responses in the
parahippocampal gyrus; 139 buildings and 139 houses) were used as cue stimuli. The
face and place stimuli were selected from multiple stimuli libraries (Ebner, Riediger, &
Lindenberger, 2010; Konkle, Brady, Alvarez, & Oliva, 2010; T.-H. Lee, Lee, Lee, Choi,
& Kim, 2006; Lundqvist, Flykt, & Ö hman, 1998; Tottenham, Borscheid, Ellertsen,
Marcus, & Nelson, 2002). All stimuli were gray-scaled and normalized to the mean
luminance of all images. In the main attention task session, 64 face and 64 place stimuli
were randomly selected from the larger pool of stimuli and assigned to the conditions for
each participant. The schedule of stimulus presentation and data collection was controlled
by the PsychToolbox extensions (Brainard, 1997; Pelli, 1997) based on Matlab 2010b
(The MathWorks Corp. Natrick, MA. The mild electric shock used as an unconditioned
stimulus was delivered to the third and fourth fingers of the left hand via a shock
stimulator (E13-22; Coulbourn Instruments, Allentown, PA), which included a grounded
RF filter.

3.2.3. Procedure
In the current study, two experiments were conducted; the first one was conducted
in the lab (behavioral experiment), and the other one was conducted in the scanner (fMRI
experiment). Each participant completed a fear-conditioning session (one run with 30
trials) and a dot-probe session (two runs for the behavioral experiment; three runs for the
fMRI experiment; 64 trials per run). An additional localizer scan was administered during
the fMRI experiment.

3.2.3.1. Fear conditioning. An initial fear-conditioning session established the
emotionally arousing nature of the CS+ tone with a trace-conditioning paradigm. In this
session, either the low- or high-pitched tone was paired with electric shock. Which tone
33

was paired with shock was counterbalanced across participants. Each trial in the
conditioning session began with onset of two placeholders (3.8° X 3.8°; 7° eccentricity)
against a gray background to match contextual information between conditioning session
and subsequent dot-probe session. Participants were then presented with one of the CS
tones for 0.7 s, followed by a 1.2 s inter-stimulus interval. After this interval, a shock was
delivered for 0.5 s if the tone was assigned to the CS+ condition (Figure 5A) and
followed by a fixation jittered to appear for 10, 11 or 12 s. On the CS- tone trials, there
was no shock. The 1.2 s interval before the shock was chosen to allow participants’
arousal level induced by the CS+ tone to increase before the face-scene cues appeared in
the main dot-probe task. In order to ensure that participants attended to the tones, they
were asked to indicate the type of tone (i.e., low- or high pitched) with a button press
immediately after they were presented with a tone. A total of 30 trials were presented in a
random order: 10 CS+ with shock, 10 CS+ without shock, and 10 CS- tones. Thus, CS+
tones were followed by a shock with a 50% partial reinforcement schedule. Prior to the
experiment, participants were informed which tone was predictive of the electric shock,
but they were not informed about the probability of shock on each trial. The intensity of
"highly unpleasant but not painful" electric shock was determined individually
(behavioral experiment: M
intensity
= 2.26 mA, range 1.1 - 4.0 mA; fMRI experiment: M
intensity
= 2.30 mA, range 1.4 - 4.0 mA). Trials that included shocks were excluded in
subsequent analyses.

3.2.3.2. Dot-probe task. After the fear-conditioning task, participants performed the dot-
probe task. A total of 128 trials were presented over two runs in the behavioral
experiment, and a total of 192 trials were presented over three runs in the fMRI
experiment; each run consisted of 32 CS+ trials (16 salient-location target and 16 non-
salient-location target trials) and 32 CS- trials, and thus a total of 64 trials per run were
presented in a random order. A trial began with onset of two placeholder outlines (3.8° X
34

3.8°; 7° eccentricity), followed by either the CS+ or CS- tone playing for 0.7 s, and a 1-s
blank screen. Then, a face-place image pair was presented in the two placeholder frames
simultaneously for 0.1 s. Finally, a dot target was shown 0.1 s after offset of the face-
place pair (SOA = 0.2 s) for 1.0 s on the same or opposite side as the salient face cue
(Figure 5B). Participants were asked to identify the location of the dot target (0.5° X
0.5°) by pressing a left or right button. A fixation cross (randomly jittered; 2 - 8 s) was
presented between trials. Each face was randomly paired with one of the place images
assigned to the same condition; the location of each stimulus was also randomly
determined for each participant.
To enhance the saliency of cue stimuli, there was always a brief luminance
increase consisting of a yellow colored frame in the salient cue’s location (Figure 5B). In
addition, a face image was always used as the salient cue given its own intrinsic
evolutionary value (for a review, see Palermo & Rhodes, 2007). To minimize extinction
of conditioned responses, three additional CS+ trials with shock were presented randomly
in each run. Other than the shock, these booster trials were identical to the main trials,
and were excluded from further analysis. The booster trials were always followed by a
10-s blank interval. The face and scene cue stimuli in the booster trials were selected
from images not used in the main task.

3.2.3.3. Localizer session. An additional face/scene localizer run followed the dot-probe
task. The localizer consisted of 24 blocks (12 face-task blocks and 12 scene-task blocks).
Each block contained eight trials that lasted 11.6 s and were separated from each other by
a 10-s blank screen. Each block began with a 2-s task cue to indicate which task to
perform, followed by a series of face or scene images; each stimulus was shown for 1.2 s.
Participants were asked to indicate the sex of faces in the face-task blocks and the type
(building or house) of places in the scene-task blocks. Participants were explicitly
informed that no shocks would be administered during the run.

35

3.2.4. Psychophysiology data
During both experiments, individual skin conductance response (SCR) was also
acquired to confirm the success of the emotional arousal manipulation (Lim et al., 2009)
with MRI-compatible electrodes placed on the index and middle finger of the left hand.
All physiological data were recorded at 1 kHz sampling rates through the MP-150 system
(BIOPAC system, Goleta, CA), connected to a grounded RF filter, and MR-compatible
leads and electrodes. For SCR, the data were detrended, smoothed with a median filter
over 50 samples to filter out MRI-induced noise. On each trial, the SCR was calculated
by subtracting a baseline (from 0 - 1 s after stimulus onset) from the peak amplitude
during the 1 - 8 s time window. Due to a technical failure, recording could not be
completed for one participant in the fMRI experiment.

3.2.5. MRI Data
3.2.5.1. Acquisition. All MRI data were acquired on a Siemens 3T Magnetom Trio with
stimuli presented on a liquid crystal display monitor (1024 X 768 pixels at 60 Hz)
positioned behind the head of participants and viewed using a mirror attached to a 32-
channel matrix head coil at the USC Dana & David Dornsife Cognitive Neuroscience
Imaging Center. High-resolution (T1-MPRAGE) structural images were acquired first
(TR = 1950 ms; TE = 2.26 ms; FA = 7°; 1-mm isotropic voxel; 256-mm field of view).
Next, functional images were acquired with gradient-echo echo-planar T2*-weighted
imaging. Each functional volume consisted of 40 interleaved (no skip) 2.5 mm axial T2*-
weighted slices (TR = 2000 ms; TE = 25 ms; FA = 90°; matrix size = 64 X 64; field of
view = 192 mm).

3.2.5.2. Preprocessing. The first eight volumes were discarded to allow for T1
equilibration. Standard preprocessing was conducted using FSL FMRIBs Software
Library (FSL v5.0; www.fmrib.ox.ac.uk/fsl); slice-time correction, motion correction
36

with MCFLIRT, spatial smoothing with a Gaussian kernel of FWHM 6 mm, high-pass
temporal filtering with a filter width of 100 s and skull stripping of structural images with
BET, and registering each functional image to both the participant's high-resolution
structural image and the standard Montreal Neurological Institute (MNI) 2-mm brain.
MELODIC ICA2 (Beckmann & Smith, 2004) was applied to remove noise components.

3.2.5.3. Fear conditioning data analysis. For fear conditioning data, a standard two-stage
mixed-effects analysis was performed. The general linear model (GLM) of the BOLD
signal for each CS tone type including trace-interval period was estimated at the first
(fixed) level with a double-gamma hemodynamic response function. Six motion
parameters were added to the design matrix, following the example of numerous previous
fear-conditioning studies (Büchel, Dolan, Armony, & Friston, 1999; Dunsmoor,
Bandettini, & Knight, 2008; Lim, Padmala, & Pessoa, 2008; Lim et al., 2009), including
those that localized "fear-network" regions (for a review see Sehlmeyer et al., 2009) in
using motion parameters. One limitation to this approach that should be noted is that
adding motion regressors to the design matrix as covariates of no interest may lead to
under-estimates of the cluster activity insofar as participants’ motion is correlated with
anticipating a shock on CS+ trials (e.g., Johnstone et al., 2006). In addition, a timeline
demarcating trials involving an electrical shock was added as a covariate of no interest.
The participants' data were then inputted into a random-effect model for group analysis
(Beckmann & Smith, 2004; Woolrich, Behrens, Beckmann, Jenkinson, & Smith, 2004).
Group level analysis was thresholded using cluster detection statistics, with a height
threshold of Z > 2.3 and a cluster probability of p < .05 (one-tailed) (Worsley, 2001),
corrected for whole-brain multiple comparisons using Gaussian Random Field Theory
(GRFT) unless otherwise noted.

37

3.2.5.4. Dot-probe task analysis for category specific ROIs. For the main dot-probe task
data, a region-of-interest (ROI) analysis was performed. To do so, stimulus-dependent
changes in BOLD signal were modeled at the first (fixed) level with regressors for cue
stimulus presentation (a face-house pair) and their respective temporal derivatives for
each arousal condition (i.e., CS+ and CS-) separately. Trials were collapsed across those
with dots in the salient-cue location and the non-salient-cue location. Motion parameters
and booster shock trials were also included in the design matrix as covariates of no
interest. The effects of each regressor were estimated over three runs, except for two
participants who each had one run excluded due to extensive movement.
Using FSL Featquery, percent signal change values were extracted from the FFA
and PPA region of each hemisphere separately for the arousal and non-arousal conditions
as a weighted average of the surrounding voxels, with weights determined by a 4-mm
FWHM Gaussian kernel mask. The ROI mask for each FFA and PPA region were
individually defined from the localizer session as the peak voxel in ventral temporal
cortex that was most selective for faces (face block > scene block; Z=2.57, uncorrected)
and for scenes (scene > face) in each hemisphere, respectively. In the left hemisphere,
ROIs could be defined for all participants for both FFA (mean peak MNI voxel
coordinates: [-42 -54 -24]) and PPA ([-26 -44-14]). In the right hemisphere, ROIs could
be defined for all participants for the PPA (mean peak [26 -40 -14]) and for all but one
for the FFA (mean peak [40-54 -22]).
Although the main goal of the present study was to determine the effects of
saliency-arousal interactions within ROIs, a group-level analysis (random-effects) was
also performed to model general task-related activation at a group level (see Appendix
B).

3.2.5.5. Dot-probe task analysis for intraparietal sulcus ROI. To examine responses in
intraparietal sulcus (IPS) as a function of emotional arousal and the location of salient cue
38

presentation, another GLM was estimated; stimulus-dependent changes in BOLD signal
were modeled at the first (fixed) level with regressors for cue stimulus presentation and
their respective temporal derivatives as a function of salient cue location (left, right) and
arousal condition (i.e., CS+ and CS-). The effects of each regressor were first estimated
for each participant over three runs.
To define the IPS ROI region, regressors only for target dot and their respective
temporal derivatives collapsed across arousal conditions were modeled separately as a
function of dot location (left or right). The right IPS mask was then defined for each
individual, based on a contrast between left vs. right target dot location. Specifically, the
peak voxel (3mm, Gaussian sphere mask) was found in the contrast (left > right target
location; Z=1.64, uncorrected) within a standard anatomical brain mask of IPS (as
provided by FSL; Jülich histological atlas) that was most selective in the right
hemisphere. In turn, the left IPS mask was defined individually based on a reversed
contrast (right > left target location). A right IPS mask (mean peak voxel coordinates: [-
40 -56 46]) was identified in 19 out of 20 participants for the left > right target contrast
and a left IPS mask was identified in 14 out of 20 participants for the right > left contrast
(mean peak [32 -48 44]).

3.2.5.6. Functional connectivity analysis. To characterize dynamic interregional
interactions, a beta series correlation analysis (Gazzaley et al., 2007; Rissman, Gazzaley,
& D'Esposito, 2004) was applied. To do so, a new design matrix was created where a
visual cue event per each trial was coded as a unique covariate, resulting in 192
independent variables (i.e., 96 cues with CS+ and 96 with CS-). The global mean signal
level over all brain voxels was calculated for each time point and was included to reduce
the confounding effects of the global signal change. Motion parameters and booster
shock trials were also included in the design matrix as covariates of no interest. Finally,
extracted mean activities (i.e., mean parameter estimates) of each trial from a seed region
39

(peak voxel of each individual functional mask) were used to compute correlations
between the seed's signal and signal of all other voxels in the whole brain, thus
generating condition-specific seed correlation maps. Correlation magnitudes were
converted into z scores using the Fisher's r-to- z transformation. Condition-dependent
changes in functional connectivity were assessed using random effects analyses, which
were thresholded at the whole-brain level using clusters determined by Z > 2.3 and a
cluster significance threshold of p = .05 (corrected; one-tailed).

3.3. Results

3.3.1. Behavioral experiment
3.3.1.1. Fear conditioning results. Fear conditioning successfully modulated arousal as
indicated by greater SCR in response to CS+ tones than in response to CS- tones, t (51) =
9.19, p < .001 (Figure 6A).

3.3.1.2. Dot-probe task results. The reaction times (RTs) from error trials (.01%) or those
with more than 2.5 SDs above or below each participant's mean were removed (.03%)
before obtaining the mean RTs for each condition for each participant. A repeated-
measures ANOVA, Arousal Condition (2: CS+, CS-) X Trial Type (2: salient-location
target, non-salient-location target) was conducted. The manipulation of salience worked,
as there were faster RTs in salient-location-target (343.39 ms) than non-salient-location-
target trials (354.73 ms), as indicated by a main effect of Trial Type, F (1, 51) = 29.04, p
< .001. There also was a significant arousal-by-trial type interaction, F (1, 51) = 7.79, p
< .01 (Figure 6B). Subsequent pairwise comparisons revealed CS+ tones marginally
significantly facilitated RTs during the salient-location-target trials (p = .084), but
impaired RTs during the non-salient-location-target trials (p = .05). Thus, as predicted by
40

the ABC model, participants were faster to respond to the target dot when it appeared in
the location of the more salient cue, and simultaneously slower to respond to the target
when it appeared in the location of the non-salient cue in the arousing than in the non-
arousing trials.

3.3.2. fMRI experiment
3.3.2.1. Fear conditioning results: whole-brain analysis. As expected, the fear-
conditioned tone (i.e., CS+) compared with the other tone (CS-) elicited more brain
activity (Figure 6C; see Table 2 for local maxima regions in the clusters), in "fear-
network" regions (see Sehlmeyer et al., 2009) including bilateral insular (R: [34 24 4], Z
= 4.93, Cluster 1; L: [-30 22 -6], Z = 5.03, Cluster 2), bilateral frontal operculum cortex /
inferior frontal gyrus (R: [48 18 -2], Z = 5.01, Cluster 1; IFG; L: [-44 20 0], Z = 3.99,
Cluster 2) and bilateral caudate (L: [-8 8 2], Z = 4.16; R: [10 12 4], Z = 4.71; both Cluster
1). Increased activation in the right amygdala ([28 0 -16], Z = 2.36; Cluster 1), and
anterior cingulate gyrus (ACC; [4 6 38], Z = 4.40; Cluster 1) were observed as well. Due
to the auditory nature of the CSs in the current study, greater activation in bilateral
Heschl’s gyrus (i.e., cortical center of primary auditory cortex; L: [-40 -22 8], Z = 3.17,
Cluster 3; R: [46 -22 12], Z = 4.23, Cluster 2) was found for the CS+ tone than for the
CS- tone. Confirming the success of the arousal manipulation via fear conditioning in the
current study, CS+ trials yielded greater SCR than CS- trials, t (18) = 2.20, p < .05
(Figure 6D).

3.3.2.2. Dot-probe task results: behaviors. The RTs from error trials (.04%) or those with
more than 2.5 SDs above or below each participant's mean were removed (.02%) before
obtaining the mean RTs for each condition for each participant. A repeated-measures
ANOVA (2 Arousal Condition X 2 Trial Type) revealed a main effect of Trial Type, F
(1,19) = 18.98, p < .001, reflecting faster RTs in salient-location-target (370.49 ms) than
41

non-salient-location-target trials (388.35 ms), and also a main effect of Arousal
Condition, F (1,19) = 5.34, p < .05, reflecting faster RTs in arousing (i.e., CS+; 375.84
ms) than non-arousing trials (i.e., CS-; 383.00 ms) (Figure 6E). A planned pairwise
comparison revealed that the facilitation in RTs during the CS+ salient-location-target
trials was significant (p < .05). Overall, participants were faster to respond to the target
dot when it appeared in the location of the more salient face cue and this detection
advantage was greater for trials following CS+ than for trials following CS- tones. There
was no significant difference between CS+ and CS- trials when the target appeared in the
location of the less salient place cue. However, the arousal-by-trial type interaction did
not reach statistical significance in the fMRI experiment.

3.3.2.3. Dot-probe task results: ROI analysis. To probe how emotional arousal interacted
with stimulus saliency, brain activity during the CS+ and CS- trials was quantified within
a set of ROIs in the PPA and FFA. These ROIs were individually defined based on
localizer run results. Percent signal changes extracted from these ROI masks in the left
and right hemisphere indicated that CS+ trials led to stronger FFA activation (i.e.,
response to the salient face cue) than did the CS- trials. Simultaneously, CS+ trials led to
decreased PPA activation (i.e., response to the non-salient place cue) than did CS- trials.
This pattern was confirmed by a Arousal Condition (2: CS+, CS-) X Region (2: FFA,
PPA) X Hemisphere (2: left, right) repeated-measures ANOVA, which revealed a
significant Arousal Condition X Region X Hemisphere, F (1,18) = 5.20, p < .05, and
Arousal Condition X Region interaction effect, F (1,18) = 10.36, p < .005, and a main
effect of Region, F (1,18) = 81.74, p < .001. To further examine the three-way
interaction, a repeated-measure ANOVA with Arousal Condition (CS+, CS-) X 2 Region
(FFA, PPA) was conducted separately for each hemisphere. In the left hemisphere, it
revealed a main effect of Region, F (1,19) = 52.50, p < .001, and a significant cross-over
interaction, F (1,19) = 11.24, p < .005, indicating that the effect of the arousal-by-trial
42

type interaction differed for salient and non-salient stimuli; subsequent pairwise
comparisons showed that there was increased activation in left FFA in the CS+ compared
with the CS- trials (p < .05), and decreased activation in left PPA in the CS+ than in the
CS- trials (p < .05). That is, the results showed that emotional arousal both increases
brain activity associated with the salient stimuli (i.e., face stimuli in this case) and
decreases brain activity associated with the non-salient stimuli (i.e., place stimuli; Figure
7A). Brain responses from the right hemisphere also showed a main effect of region, F
(1, 18) = 33.99, p < .001. A similar cross-over pattern of interaction was observed that
was not quite significant (p = .087). Therefore, subsequent analyses below focused on the
left hemisphere.

3.3.2.4. Dot-probe task results: functional connectivity and trial-by-trial relationship
between brain response and RTs. The whole-brain connectivity analysis comparing the
CS+ trials and CS- trials revealed that the left FFA had greater positive functional
connectivity with the right amygdala in the CS+ trials compared with the CS- trials
(Figure 7B; Table 3). To provide additional information about overall connectivity, a
differential correlation map (CS+ > CS-) of the left FFA was examined with a lowered
threshold (Z = 2.3, uncorrected). Although this low-threshold map should be interpreted
with caution, it was found several interesting patterns. First, in the CS+ trials compared
with the CS- trials, the left FFA showed a greater positive functional connectivity with
brainstem regions including a region consistent with the location of the locus coeruleus
(LC), known for its broad range of modulatory role in emotion, memory and attention
processing, as well as its role in modulating arousal (for a review, see Sara, 2009). There
was also greater negative functional connectivity between the left FFA and left PPA in
CS+ than in CS- trials with this low threshold. Thus, enhanced processing for salient face
cues under arousal was associated with increased activity of the amygdala and a region in
the approximate location of LC as well as stronger inhibition of non-salient place cues.
43

To examine the relationship between brain responses in category-specific regions
(i.e., increased FFA and decreased PPA by emotional arousal) and speed of processing
stimuli in the location of the corresponding face or place, a hierarchical linear modeling
(HLM) analysis was conducted, treating each trial as a level-1 unit and each participant
as a level-2 unit. A separate analysis was done for salient-location-target and non-salient-
location-target trials. In both analyses, predictor variables included percent signal
changes extracted from left FFA and left PPA signals respectively, arousal condition
(CS+ or CS-), an interaction between FFA and the arousal condition, and an interaction
between PPA and the arousal condition for each trial. Trial RTs were used as the
dependent variable. This HLM analysis on salient-location-target trials revealed
significant effects of FFA, indicating that as FFA activity increased, RTs for the targets
shown in the face location speeded up. Furthermore, there was a significant interaction
between the arousal condition and FFA activity (Table 4), reflecting that greater
activation in FFA was more strongly associated with faster RTs for the face-location
targets in CS+ trials than in CS- trials. With a marginal significance level (p = .09), the
analysis also revealed an interaction between the arousal condition and PPA activity,
indicating that the stronger PPA activity led to slower RTs for the face-location targets
under arousal. A similar analysis on non-salient-location-target trials did not reveal any
significant results.

3.3.2.5. Dot-probe task results: Arousal amplified weighted attention to saliency. To
probe how attentional weighting to the salient cue interacts with emotional arousal, brain
activity during the CS+ and CS- trials was examined within right and left intraparietal
sulcus ROIs, regions associated with attentional orienting to contralateral salient stimuli.
A repeated-measure Arousal Condition (2: CS+, CS-) X Saliency Location (2: left, right)
ANOVA on the percent signal changes from the right IPS ROI mask revealed a
significant cross-over interaction, F (1,18) = 4.78, p < .05, on the right IPS; subsequent
44

pairwise comparisons showed significantly greater right IPS responses in the CS+
condition than in the CS- condition when the salient cue was presented on the left side (p
< .05). A similar analysis on the left IPS revealed a main effect of Arousal Condition, F
(1, 13) = 7.55, p < .05; and a marginally significant interaction, F (1, 13) = 3.50, p =
.084; a subsequent pairwise comparison revealed significantly greater left IPS response in
the CS+ condition than in the CS- condition when the salient cue was presented on the
right side (p < .05; Figure 8A). These results indicated that arousal amplifies the effects
of saliency even beyond category-specific regions such as PPA and FFA.
Additional correlation analyses were conducted to explore how the weighted
attentional processing to salient stimuli can influence processing in both FFA and PPA
regions. In this analysis, signal change values in IPS to salient cues (i.e., estimates in left
IPS when the salient cue was presented on the right side and in right IPS when the salient
cue was on the left side) were combined across right and left IPS, and signal change
differences were calculated for FFA, PPA and IPS regions by subtracting the percentage
signal change value for CS- from that of CS+. The robust method was used to correct for
outliers (Wilcox, 2012) via the robust correlation toolbox (Pernet, Wilcox, & Rousselet,
2012). A significant positive correlation between IPS and FFA regions was identified, r =
.48, p < .05, indicating that increased attentional processing of salient cues compared
with non-salient cues in IPS was associated with increased FFA activation.
Simultaneously, a significant negative correlation between the IPS and PPA regions, r = -
.61, p < .05, indicated that increased attentional prioritization of salient cues is associated
with reduced processing of non-salient cues in their associated representational region
(Figure 8B).

45

3.4. Discussion

Previous neuroimaging research on how arousal influences perceptual processing
demonstrated ways in which arousal enhances sensory processing and attention (Brooks
et al., 2012; Ethofer et al., 2012; Lim et al., 2009) but ignored the possibility that arousal
can also impair perceptual processing, despite evidence from behavioral research
showing that arousal has both enhancing and impairing effects (Mather & Sutherland,
2011). In the current fMRI study, a simple dot-probe task was used to test the hypothesis
that arousal amplifies the effects of competition among salient and non-salient stimuli in
perception, enhancing processing of salient stimuli while impairing processing of non-
salient stimuli. During the task, on each trial one face and one place image were
presented simultaneously as cue stimuli, and gave a brief luminance increase in the face
cue’s location to enhance its perceptual saliency. As predicted by ABC theory, there was
an arousal-by-saliency interaction in the fusiform face area (FFA) and parahippocampal
place area (PPA). On arousing compared with non-arousing trials (i.e., fear-conditioned
tone vs. neutral tone trials), responses in FFA (i.e., responses to the salient face cue) were
enhanced and responses in PPA (i.e., responses to non-salient place cues) were reduced
(Figure 7A). These findings indicate that arousal and saliency interact to determine the
strength of visual processing, an advance on previous studies, which typically had an
“enhancement-only” perspective on the effects of arousal on perception and did not
consider how arousal might also impair processing.
It was also found that responses in intraparietal sulcus (IPS) were more enhanced
for the salient cue (or its location) on arousing trials than non-arousing ones, indicating
that attentional engagement to the salient cue location was also augmented by the
arousal-saliency interaction (Figure 8A). Furthermore, IPS activity showed a significant
positive relationship with the FFA under arousal, while there was a negative relationship
with the PPA (Figure 8B). IPS plays a key role in attentional weighting toward
46

prioritized external stimuli (Chica, Bartolomeo, & Valero-Cabré, 2011; Geng et al., 2006;
Konen & Kastner, 2008; Szczepanski et al., 2010) regardless of stimulus content (Talmi,
Anderson, Riggs, Caplan, & Moscovitch, 2008). The current findings suggest that IPS’s
attentional weighting role increased during arousing trials, enhancing prioritized stimuli
at the cost of non-prioritized stimuli. This is consistent with previous research indicating
a synergistic role of attention and arousal in enhancing perception of salient stimuli
(Bermpohl et al., 2006; Lim et al., 2009; Mohanty, Egner, Monti, & Mesulam, 2009;
Phelps et al., 2006). Indeed, Lim et al. (2009) found that the amygdala influences
successive visual perception by mediating the frontoparietal attention network. Similarly,
CS+ trials induced greater attentional network activity than did CS- trials in the group-
level analysis of dot-probe task (Appendix B). Collectively, these findings from the
current study as well as previous research indicate that emotionally arousing stimuli
enhance subsequent perceptual processing of prioritized external stimuli.
At the behavioral level, participants were faster to respond to the target dot when
it appeared in the same location of salient face cue and slower to respond to the target
when it appeared in the difference location of salient cue. As predicted by the ABC
model, in the initial behavioral study, both salient-location-targets were responded to
faster and non-salient-location targets were responded to slower on arousing trials.
However, in the fMRI experiment, the arousal-by-salience interaction seen in the
behavioral study did not achieve significance, although there was a significant speeding
of responses to the salient-location trials and not to the non-salient-location trials (Figure
6E). The lack of replication of the interaction may be due to the smaller number of
participants in the fMRI experiment or to some other difference resulting from the
scanning environment. However, the HLM analyses indicate that both increased FFA and
decreased PPA activity during CS+ trials relative to CS- trials (i.e., the brain-based
arousal-saliency interaction effect) were associated with faster reaction times for
detecting the target in the salient-cue location during the fMRI experiment. In sum, these
47

relationships further support the idea that arousal-saliency interactions subserve the final
behavioral outcome.
Consistent with a large body of research implicating the amygdala in emotional
processing (Adam K Anderson et al., 2003; LaBar & Cabeza, 2006; Mather et al., 2004;
Phelps, 2006), the amygdala showed greater functional connectivity with the FFA during
CS+ trials compared with CS- trials, suggesting that emotional processing evoked by
fear-conditioned tones was involved in the emotional-saliency interactive processes in
visual perception. Although it should be interpreted with caution because of the limited
resolution (slice thickness = 2.5 mm) of the current scan protocol and a lenient threshold
(Z= 2.3, uncorrected), a region consistent with the location of the locus coeruleus, known
for its modulating role in arousal and attention (Sara, 2009), was identified with more
coordinated activity with the FFA during arousing than during non-arousing trials
(Figure 7B). These results indicate that emotional arousal influences subsequent visual
processing to amplify competitive processes that are biased in favor of high-priority
stimuli at the expense of low-priority stimuli. The cell bodies of noradrenergic neurons
within the LC have widely distributed, ascending projections to the forebrain regions
including the amygdala (Berntson, Sarter, & Cacioppo, 2003; Luppi, Aston-Jones,
Akaoka, Chouvet, & Jouvet, 1995). Thus, LC may help trigger involvement of the
amygdala in shaping successive visual processing. Alternatively, released norepinephrine
from LC may directly influence visual cortex. According to previous studies (Berridge &
Waterhouse, 2003), norepinephrine can simultaneously increase both excitatory and
inhibitory components of visual cortex neuronal responses. In particular, previous animal
research demonstrates that norepinephrine can change visual perception by altering
receptive field properties such as direction selectivity, velocity tuning and response
threshold (McLean & Waterhouse, 1994). Thus, it is also possible that norepinephrine
release elicited by an arousing sound modulates visual processing as a function of
stimulus priority.
48

The advantage of the current approach was that the source of the arousal and the
target items were separated. The salient items are perceptually identical on arousing and
non-arousing trials, allowing attribute any differences to the arousal rather than to the
perceptual qualities of the salient item itself. The current results indicate that emotional
arousal induced by one stimulus can influence the competitive processes engaged by
other stimuli, such that processing impairments are seen as well as enhancements.

49

CHAPTER 4

THE EFFECT OF EMOTIONAL AROUSAL
ON BIASED-COMPETITION

4.1. Study overview

The goal of this study was to replicate and extend the observed arousal-saliency
interaction effects from the previous fMRI study (Study 2 in Chapter 3) by investigating
brain activation with different categorical stimulus.
To achieve the current goal, a simple visual detection task was adopted in which
place and household-object images were presented simultaneously with one serving as
the target and the other the task-irrelevant visual distracter (non-target). Unlike the
previous study in Chapter 3 in which face images were always the salient stimulus, the
current study contained 50% place salient trials and 50% object salient trials. To
differentiate the priority of the two visual images on a given trial, there was always a
brief luminance increase (i.e., a yellow colored frame) in the salient location and the
image in the salient location always had a high contrast/luminance level. Participants
were asked to identify the location of salient image (left of right) regardless of the image
contents (place or object).
Although object images are also known as categorical stimuli that induce
selective brain response in the later occipital complex (LOC; Grill-Spector, Kourtzi, &
Kanwisher, 2001), the current analyses focused on the brain response within the
parahippocampal place area (PPA), which is a selective region for place/scene images
consisting of various spatial layouts (Epstein & Kanwisher, 1998). Previous studies
50

suggest that the PPA responds selectively to gross spatial properties more than to object
identity, showing little modulation by object properties (Epstein, Harris, Stanley, &
Kanwisher, 1999; Erez & Yovel, 2014; Kravitz, Saleem, Baker, & Mishkin, 2011;
Linsley & MacEvoy, 2014; MacEvoy & Epstein, 2011). Conversely, the response of the
LOC and its sub-regions is mediated greatly not only by object shape property itself, but
also by various factors such as spatial information of the presented images (Dilks, Julian,
Paunov, & Kanwisher, 2013; Freud, Rosenthal, Ganel, & Avidan, 2014; Kim &
Biederman, 2011), simultaneous presentation with task-irrelevant information (i.e.,
clutter; Erez & Yovel, 2014) and other contextual factors such as bottom-up saliency
(Altmann, Deubelius, & Kourtzi, 2004). Hence, the LOC is a sub-optimal region for
measuring visual competition between places and objects. Nevertheless, the use of
objects allowed us to examine the influence of arousal and competition on PPA activity
during instances in which places were salient and non-salient, which was not possible in
Study 2 (Chapter 3) as faces were always salient.
To avoid confounding effects of using stimuli in the same sensory modality to
induce emotional arousal and to measure visual processing for subsequent neutral target
stimuli, the current study manipulated participants’ arousal level on a trial-by-trial basis
using tones conditioned to predict shock (CS+; i.e., arousing stimulus in a different
sensory modality) or no shock (CS-). The fear-conditioning phase establishing the
meaning of the tones was conducted prior to the main task.
The ABC model predicts that on CS+ trials, compared to CS- trials, PPA
activation will increase when the place image is salient (i.e., “winner-take(s)-more”). On
the contrary, when the place image is non-salient, ABC predicts that there will be reduced
activation in PPA during CS+ relative to CS- trials (i.e., “loser-take(s)-less”).

51

4.2. Method

4.2.1. Participants.
Twenty-eight healthy younger adults (M
age
= 24.39 years, range = 18 – 34; nine
females) participated in the current study. All participants had normal or corrected-to-
normal visual acuity. Participants provided written informed consent approved by the
USC Institutional Review Board and were paid for their participation.

4.2.2. Stimuli and apparatus
Two tones (500 Hz and 800 Hz) were adopted as conditioned stimuli (i.e., CSs) to
avoid possible confounding effects of using stimuli in the same sensory modality to
induce emotional arousal and to measure perceptual processing (e.g., Zeelenberg &
Bocanegra, 2010). 270 house/ building place images (known to elicit selective responses
in the parahippocampal place area; PPA) and 240 color photographs of various real-world
objects were used as main target stimuli. These images were obtained from several
websites and a previously published set of object stimuli (Brady, Konkle, Alvarez, &
Oliva, 2008) respectively. All stimuli were gray-scaled and normalized to the mean
luminance of all images. In the main spatial detection task, 160 object and 160 place
stimuli were randomly selected from a large pool of stimuli and assigned to the
conditions for each participant. The mild electric shock used as an unconditioned
stimulus (US) was delivered to the third and fourth fingers of the left hand via a shock
stimulator (E13-22; Coulbourn Instruments, Allentown, PA), which included a grounded
RF filter. The schedule of stimulus presentation and data collection were controlled by
the PsychToolbox extensions (Brainard, 1997; Pelli, 1997), which is based on Matlab
2010b (The MathWorks Corp. Natrick, MA).

4.2.3. Procedure.
52

In the current study, each participant completed a fear-conditioning task phase, a
spatial detection task phase, and PPA localizer. Details were as follows.
4.2.3.1. Fear conditioning. An initial fear-conditioning task established the emotionally
arousing nature of the CS+ tone with a delayed-conditioning paradigm. During the task
phase, either the low- or high-pitched tone was paired with electric shock (randomly
assigned across participants, low = 8 and high = 20). Each trial in the conditioning task
began with onset of fixation cross (0.8° X 0.8°) against a gray background, and
participants were then presented with one of the CS tones for 0.7 s. A shock was
delivered for 0.5 s if the tone was assigned to the CS+ condition followed by a blank
screen (i.e., inter-trial interval; ITI) jittered to appear for 8, 8.5 or 9 s. On the CS- tone
trials, there was no shock. In order to ensure that participants attended to the tones, they
were asked to indicate the type of tone (i.e., low- or high pitched) with a button press
immediately after they were presented with a tone. Total 36 trials were presented in a
random order: 12 CS+ with shock, 12 CS+ without shock, and 12 CS- tones. Thus, CS+
tones were followed by a shock with a 50% partial reinforcement schedule. Prior to the
experiment, participants were informed which tone was predictive of the electric shock,
but they were not informed about the probability of shock on each trial. The intensity of
"highly unpleasant but not painful" electric shock was determined individually (M
shock-
intensity
= 2.01 mA, range 0.8 - 4.0 mA). Trials that included shocks were excluded in
subsequent analyses.

4.2.3.2. Spatial detection task. After the fear-conditioning task, participants performed a
simple spatial detection task. A trial began with onset of fixation cross, followed by either
the CS+ or CS- tone playing for 0.7 s, and a 2-s blank screen (i.e., inter-stimulus interval;
ISI) to maximize the effect of the CS tones in eliciting emotional arousal (Bocanegra &
Zeelenberg, 2009). Then, a place-object image pair was presented in the two placeholder
frames simultaneously for 0.6 s (4.3° X 4.3°; 11.5° eccentricity). To increase the saliency
53

for one of the images, there was always a brief luminance increase for 0.1s consisting of a
yellow colored frame in the one of the holder frame. In addition, the salient image itself
among the place-object image pair had higher contrast level (80%) than the non-salient
image (20%; Figure 9A). Participants were asked to identify the location of the salient
image by pressing a left or right button. An ITI (randomly jittered; 2.5, 3.5 4.5 and 5.5 s)
was presented between trials. Each place image was randomly paired with one of the
object images assigned to the same condition; the location of each stimulus was also
randomly determined for each participant. A total of 160 trials were presented over five
runs, except for one YA who completed only four runs. Each run consisted of 16 CS+
trials (eight place salient trials and eight place non-salient trials; Figure 9B) and 16 CS-
trials in a random order. To minimize extinction of conditioned responses, three
additional CS+ trials with shock were presented randomly in each run. Other than the
shock, these booster trials were identical to the main trials, and were excluded from
further analysis. The booster trials were always followed by a 10-s blank interval. The
stimuli in the booster trials were selected from images not used in the main task.

4.2.3.3. Localizer session. A functional localizer run followed the spatial detection task to
identify the PPA region for each individual. The localizer consisted of six place, six intact
object and six scrambled object blocks, resulting in a total 18 blocks. 48 place and intact
object images were newly selected, and scrambled object images were generated by
shuffling a 0.27° X 0.27° boxed in a random fashion on the intact object images. Each
block contained 12 trials that lasted 14.4 s and were separated from each other by a 10-s
blank screen. Each stimulus was displayed for 1 s and followed by a 0.2-s blank screen.
Participants detected repeating place, intact object and scrambled object images
respectively in alternating blocks (i.e., one-back working memory task). To maximize
comparability, the presenting location of stimulus was the same as those in the main
detection task (left or right side). The position of each was counterbalanced (nine left side
54

and nine right side) and indicated by a 3-s location cue in the beginning of each block
followed by a 2-s blank screen. Participants were explicitly informed that no shocks
would be administered during the run.

4.2.4. Psychophysiology data
To confirm the success of the emotional arousal manipulation during both fear-
conditioning and main detection task runs, Individual skin conductance responses (SCR)
and pupil diameter changes were acquired as a peripheral indicator of sympathetic
activation to arousal-inducing stimulus (Boucsein, 2012; Bradley, Miccoli, Escrig, &
Lang, 2008)

4.2.4.1. SCR change. Individual SCR was recorded at 1 kHz sampling rates through the
MP-150 system (BIOPAC system, Goleta, CA), connected to a grounded RF filter, and
MR-compatible leads and electrodes. The collected SCR data was detrended, smoothed
with a median filter over 50 samples to filter out MRI-induced noise. In order to equalize
variance, response-strength indices were transformed by using a logarithm function [log
10

(1 + SCR)]. For the fear-conditioning data, the preprocessed SCR was calculated using a
conventional scoring method (T.-H. Lee, Baek, Lu, & Mather; Lim et al., 2009):
subtracting a baseline (from 0 - 1 s after stimulus onset) from the peak amplitude during
the 1 - 8 s time window on each trial. However, given the event-related design of the
main detection task and the sluggish nature of the SCR, the signal changes on one trial
overlapped with those from the next trial (and also the previous trial) during the main
detection task. To address this issue, SCR data in the detection task were analyzed using a
general linear convolution model (Bach, Flandin, Friston, & Dolan, 2010; Bach, Friston,
& Dolan, 2010; Bach, Talmi, Hurlemann, Patin, & Dolan, 2011) as implemented in
SCRalyze (version b2.1.8; http://scralyze.sourceforge.net). The preprocessed time series
was z-transformed to account for between-subjects variance in SCR amplitude. For each
55

arousal condition (CS+, CS-), a stick function encoding event onsets was convolved with
the canonical skin conductance response (CSCR) function and parameter estimates (i.e.,
beta value) were extracted for each participant. The booster shock trials were included in
the design matrix as covariates of no interest. To validate this approach, SCR changes in
the fear-conditioning phase were also estimated using the same method, and compared to
the results of the conventional approach (Appendix C). Due to a technical failure,
recording could not be completed for one participant.

4.2.4.2. Pupil diameter change. Individual pupil diameter changes were also recorded at
60 Hz sampling rate using an ASL model 504 eye-tracker system (Applied Science
Laboratories, Bedford, MA) during both the fear-conditioning run and detection task
runs. The collected pupil data was normalized and median-filtered over moving windows
of 10 samples to remove artifacts. For each trial, pupil diameter values were baseline-
corrected by subtracting the mean pupil size between 0.1 s before to 0.1 s after the tone
onset, from the entire waveform in each trial (Wang & Munoz, 2014), and averaged
across trials with all possible data points depending arousal condition (CS+, CS-). Any
loss of pupil signal on each trial due to occasional blinks was excluded from
consideration instead of replacing them by linear interpolated value (total 3.05% across
participants). For statistical analyses, mean change of pupil diameter were calculated in
designated time bins from 0 – 2.7 s for both fear-conditioning and detection task. Due to
technical constraints, the pupil data could not be obtained one participants. Due to
technical constraints, the pupil data could not be obtained for one participant.

4.2.5. MRI Data
4.2.5.1. Acquisition. All MRI data were acquired on a Siemens 3T Magnetom Trio with a
liquid crystal display projector (1024 × 768 pixels at 60 Hz) onto a rear project screen
behind the head of participants and viewed using a mirror attached to a 32-channel matrix
56

head coil at the USC Dana & David Dornsife Cognitive Neuroscience Imaging Center.
High resolution structural images (MPRAGE) were acquired first; repetition time (TR) =
1950 ms; echo time (TE) = 2.26 ms; flip angle (FA) = 7°; 1-mm isotropic voxel; field of
view (FOV) = 256 mm. Next, functional images were acquired with gradient-echo echo-
planar T2*-weighted imaging. Each functional volume consisted of 41 interleaved (no
skip) 4 mm axial T2*-weighted slices; TR = 2000 ms; TE = 25 ms; FA = 90°; matrix size
= 64 X 64; FOV = 256 mm. The fear conditioning run, each run of the spatial detection
task, and the functional localizer run were acquired with 180, 160 and 256 EPI volumes
respectively.
4.2.5.2. Preprocessing. The three volumes were discarded to account for equilibration
effects. FMRI data processing was carried out using FEAT (FMRI Expert Analysis Tool)
Version 6.00, part of FSL (FMRIB's Software Library, www.fmrib.ox.ac.uk/fsl). The
following pre-statistics processing was applied; motion correction using MCFLIRT;
slice-timing correction using Fourier-space time-series phase-shifting; non-brain removal
using BET; spatial smoothing using a Gaussian kernel of FWHM 5mm; grand-mean
intensity normalization of the entire 4D dataset by a single multiplicative factor; ICA
denoising using MELODIC ICA2 and an automated toolbox (an average of 10.13
components were removed from each participant; Tohka et al., 2008); registration to high
resolution structural using FLIRT; registration from high resolution structural to standard
Montreal Neurological Institute (MNI) 2-mm brain using FNIRT nonlinear registration.

4.2.5.3. Fear conditioning data analysis. For fear conditioning data, a standard two-stage
mixed-effects analysis was performed. The general linear model (GLM) of the BOLD
signal for each CS tone type was estimated at the first (fixed) level with a double-gamma
hemodynamic response function. Motion parameters and timeline demarcating trials
involving an electrical shock were included in the design matrix as covariates of no
57

interest. Data were finally combined across participants using random-effects at the group
level (FLAME 1+2 model; Z > 2.3 with corrected cluster p = .05, one-tailed).
In addition to univariate whole-brain analysis, the fear-conditioning run data was
also used to define individual amygdala ROI masks, for subsequent analyses such as
functional connectivity, based on prior human fear-conditioning studies indicating that
CS+ increases amygdala response than in CS- (for a review, see Sehlmeyer et al., 2009).
To define the amygdala ROI mask, all activated voxels within a standard anatomical
amygdala mask (as provided by FSL; Harvard-Oxford atlas with probability of .5) were
identified by contrasting the averaged brain activity during CS+ trial with CS- trial. The
final amygdala ROI mask was then defined as the maximum peak among those voxels
and its six surrounding voxels for each hemisphere (mean peak MNI voxel coordinates:
left [x = -24, y = -4, z = -20] and right [22, -4, -18]).

4.2.5.4. Spatial detection task analysis for category specific ROIs. For the main spatial-
detection task data, the ROI analyses for the PPA region were conducted on % mean
signal change value for each place-image saliency level (i.e., when place image was
salient and when place image was non-salient compared to object image; see Figure 9B).
For each participant, stimulus-dependent changes in BOLD signal were first estimated
using a GLM on preprocessed functional images with regressors for target stimulus
presentation and their respective temporal derivatives for each arousal condition (i.e.,
CS+ and CS-) and for each place saliency type separately. Motion parameters, booster
shock trials, error trials and tone onset timing were included in the design matrix as
covariates of no interest. The effects of each regressor were estimated over functional
runs (fixed-effects; one YA completed four runs).
To define the PPA ROI for each participant, all activated voxels above baseline
(i.e., Z > 0) along the parahippocampal gyrus/ collateral sulcus region were first
identified by contrasting the averaged brain activity in place blocks with intact object and
58

scrambled object blocks based on the separate functional localizer run. The final PPA
ROI was then defined as the maximum peak with its six surrounding voxels among those
activated voxels for each hemisphere (Figure 10). Across participants the mean peak
MNI voxel coordinates were left [x = -26, y = -46, z = -6] and right [26, -44, -8] (cf. the
mean coordinates in the study 2 were left [-26 -44-14] and right [26 -40 -14]).

4.2.5.5. Functional connectivity analysis. To characterize dynamic interregional
interactions, a beta series correlation analysis (Gazzaley et al., 2007; Rissman et al.,
2004) was performed. For each participant, a new design matrix was created where a
visual target event per each trial was coded as a unique covariate for each trial during
target conditions (place-salient target with CS+ , place-salient target with CS-, place non-
salient target with CS+ and place non-salient target with CS-). Motion parameters,
booster shock trials, error trials and tone onset timing were also included in the design
matrix as covariates of no interest. Outliers for functional connectivity values were
defined a priori as average connectivity greater than 2.5-SD from the group mean.
Finally, extracted mean activities (i.e., mean parameter estimates) of each trial from a
seed region (peak voxel of each individual functional mask) were used to compute
correlations between the seed's signal and signal of all other voxels in the whole brain,
thus generating condition-specific seed correlation maps. Correlation magnitudes were
converted into z scores using the Fisher's r-to- z transformation. Individual seed
correlation maps were combined across participants using random-effects at the group
level (FLAME 1+2 model; Z > 2.3 with corrected cluster p = .05, one-tailed).

4.3. Results

4.3.1. Fear conditioning result: effectiveness of conditioning.
59

The main purpose of fear conditioning data analyses was to confirm the success
of the fear-learning for CS+ in both aging groups. To do this, we first analyzed
psychophysiology data with a-priori hypothesis that CS+ triggers a series of autonomic
response such as increased SCR and expanded pupil diameter (i.e., one-tailed t-test ;
Boucsein, 2012; Bradley et al., 2008). In addition to these physiology data, we analyzed
fear-conditioning fMRI data to examine increases brain activation in ‘fear-network’
regions by CS+ compared to CS- (see Sehlmeyer et al., 2009).
Averaged SCR showed that CS+ tone led to greater SCR change than did the CS−
trials (Figure 11A). This observed pattern was confirmed by a paired t-test, which
revealed a significant difference, t (26) = 3.53, p < .005, reflecting greater SCR for CS+
than for CS- (0.07 vs. 0.04 microS). Consistent with the SCR, pupil diameter was greater
with CS+ tone than with CS-, t (26) = 2.24, p < .05 (Figure 11B). However, there was no
correlation between the SCR and pupil diameter changes. In the whole-brain analysis, the
CS+ tone compared with the CS- elicited more brain activation in left anterior insular as
known for one of the core regions in ‘fear-network’ (Figure 11C and Table5; Sehlmeyer
et al., 2009).
In sum, both the psychophysiological and whole-brain results indicated that the
arousal acquisition for CS+ tone was successful.

4.3.2. Spatial detection task results
4.3.2.1. Behavior performance and psychophysiology. The reaction times (RT) from error
trial or those with more than 850 ms or less than 50 ms were removed (3.7%) before
averaging the mean RT for each condition for each participant. A repeated-measure
ANOVA, Arousal Condition (2: CS+, CS-) X Place Saliency Type (2: salient place target,
non-salient place target), revealed only a main effect of Arousal Condition, F (1,27) =
9.02, p < .01, indicating that reaction times to the salient image location (i.e., the image
surrounded by yellow frame with high contrast) were faster on CS+ trials than CS- trials
60

(305.19 ms vs. 314.61 ms) irrespective of place saliency type (Figure 12A & Appendix
D).
The SCR data, estimated using a general linear convolution model (see method),
showed that, during the detection task, the CS+ tone produced greater SCR activity than
the CS-, t (26) = 4.92, p < .001 (Figure 12B). Similarly, the CS+ increased pupil
diameter at marginal level than the CS-, t (26) = 1.52, p = .069 (Figure 12C). These data
indicate that the arousing response to CS+ established during the conditioning phase was
somewhat maintained during the detection task.
Additionally, the relationship between SCR changes and RT was examined. To do
so, the Pearson correlation coefficient was calculated with the difference values
calculated by subtracting each mean RT and mean SCR in CS- trial from those of CS+
trial. As a result, the RT showed a significant negative correlation with the SCR change, r
(27) = -.53, p < .005, indicating that increased arousal levels of individuals induced faster
detection for the salient target during the task (Figure 12D).

4.3.2.2. ROI analysis. To probe how emotional arousal interacted with stimulus
saliency in visual processing, brain activity during the place salient and place non-salient
trials was quantified for each arousing conditiong (i.e., CS+ and CS-) within a set of
ROIs in the individually defined PPA masks. Using FSL Featquery, percent signal
change values were extracted from the PPA ROI masks of each hemisphere separately for
each arousing conditions (i.e., CS+, CS-) depending on the place saliency type (i.e.,
salient place target, non-salient place target; Figure 9B). Extracted percent signal
changes showed that CS+ tone led to stronger PPA activation when place images were
salient targets compared PPA activity on CS- tone trials (i.e., winner-take-more effect).
On the contrary, when place images were non-salient disstracters, CS+ induced inhibited
PPA activation relative to non-arousing CS- trials (i.e., loser-take-less effect). These
brain activity patterns were the same for both hemispheres (Appendix E). These brain
61

response patterns were statistically confirmed as we found a significant cross-over
Arousal Condition X Place Saliency Type interaction effect, F (1, 27) = 5.62, p = .025,
η
p
2
= .17. There was no Hemisphere-related interaction effect with other factors, and thus
each hemisphere values were collapsed for subsequent analyses. Importantly, there was
no main effect of Arousal Condition (p = .499), indicating that the arousal-saliency
interaction in YA was due to different arousal impact depending on the saliency level of
place image; subsequent comparisons showed a significant brain activity difference
between CSs when salient image was place (M
CS+
= .31 vs. M
CS-
= .28; p = .015, one-
tailed), whereas there was no significant difference for non-salient place condition (M
CS+

= .26 vs. M
CS-
= .27; p =.185, one-tailed).
Additional correlation analyses revealed that PPA activation for CS+ tone during
the salient place trial was positively correlated with amygdala activation, r (28) = .49, p
< .01 (Figure 13B). In sum, PPA activity was modulated based on the arousal-saliency
interaction effect. Although the PPA was the main region of interest, the LOC signals
were compared as a function of saliency and arousal. As expected, the LOC region
showed non-specific response for the objects image regardless of saliency image type
(Appendix F).

4.3.2.3. Functional connectivity analysis. In the salient place target trials, the functional
connectivity analysis comparing the CS+ trials and CS- trials with PPA seed region
revealed that the PPA showed negative functional connectivity with a region of lateral
occipital lobe that closely overlapped with the LOC in CS+ than in CS- trial, indicating
that enhanced PPA response for salient place target under arousal was associated with
inhibited LOC response (i.e., region for non-salient object target processing on a given
trial; Figure 13C). Although the LOC ROI analyses did not showed clear arousal-
saliency interactive effects, these connectivity results suggest the competition between
LOC and PPA increased under arousal.
62

In addition to these results, the PPA activation showed significant positive
functional couplings with the right prefrontal cortex (PFC)/ frontal pole and left
dorsolateral prefrontal cortex (DLPFC) in both salient place and non-salient place trials
under arousal respectively (Appendix G). This suggests that in addition to the role of
saliency, top-down selective attention processes may be involved in facilitating ABC.

4.4. Discussion

The current fMRI study was a replication and extension of previous findings in
Chapter 3. It aimed to test the hypothesis that arousal effects combined with saliency are
more than region specific- or stimulus property specific effects. To do so it employed a
novel dual-stimulus detection paradigm and focused on testing arousal induced
enhancement and impairment in a single brain region, the PPA. During the task,
participants were simultaneous exposed to one salient and one non-salient image. Before
these visual stimuli were shown, one of two tones played on a trial-by-trial basis: one that
predicted a shock (arousing tone; CS+) or one that did not (CS-). Participants were
instructed to indicate the location of the salient image by button press in accordance with
the image locations (left and right).
The results confirmed that the arousal manipulation of the current study was
successful, as participants showed higher SCR response and greater pupil diameter
changes for the CS+ than the non-arousing tone (CS-) during both fear-conditioning and
detection runs. Further support came from the whole-brain univariate analysis of the fear-
conditioning run, showing that the responses in the anterior insular region, one of critical
regions in the fear network, were greater with the CS+ compared to the CS-.
At the behavioral level of spatial detection, participants were faster to identify
the location of salient stimulus under arousal. Notably this detection advantage was
63

negatively correlated with individual SCR changes during the task (Figure 12D). That is,
the more emotional arousal experienced by participants, the faster they performed the
detection of the salient target. Given the experimental design, it was only possible to look
at the enhancement property of arousal in the salience detection behavioral measure.
The fMRI analyses supported the ABC prediction such that whereas responses in
the PPA were enhanced in the place salient condition on arousing trials compared to non-
arousing ones, greater suppression was observed when the place image was not salient
during arousal trials compared to neutral trials. These differential effects from arousal-
priority dynamics were confirmed statistically by showing a significant cross-over
interaction effect of arousal condition (CS+ vs. CS-) and saliency (when place image was
salient vs. non-salient). Although the enhancement effect was even stronger than the
inhibition effect, these results are in line with the hypothesis of the ABC theory that
emotion-induced visual perception depends on the priority level of stimulus.
Because there was a simultaneous presentation of both place and object images,
the object image was necessarily non-salient if the place image was salient on a given
trial and vice versa. Thus, the LOC responses were also predicted to be reduced under
arousal at the same time. However, additional ROI analyses for the LOC failed to find a
clear inhibition patterns for non-salient object image under arousal and vice versa.
Instead, the LOC responses were increased under arousal irrespective the Place Saliency
Type (i.e., a main effect of Arousal Condition). Given the fact that whereas the LOC
could be indiscriminate for both object shape and other factors (Altmann et al., 2004;
Dilks et al., 2013; Erez & Yovel, 2014; Freud et al., 2014; Kim & Biederman, 2011),
whereas the PPA responds more specifically for spatial features (Epstein et al., 1999;
Erez & Yovel, 2014; Kravitz et al., 2011; Linsley & MacEvoy, 2014; MacEvoy &
Epstein, 2011), it is not surprising that the ABC effect was not observed within the LOC
(see Appendix F). Interestingly, the functional connectivity analyses with the PPA seed
region showed that increased PPA response under arousal (i.e., CS+ > CS-) for salient
64

place target was negatively correlated with the LOC activation. That is, there were
somewhat inhibitory processes within the LOC for non-salient object image processing
simultaneously based on the arousal-saliency interaction. One possibility of this
inconsistency between the results of univariate ROI analyses and functional connectivity
analyses is that response pattern differing on a voxel-by-voxel basis within the LOC was
difficult to be observed by the univariate approach (i.e., the averaged BOLD signal across
clusters of voxels). Future studies are needed to pattern classification or decoding
approach such as multi-voxel pattern analysis (MVPA) to examine accurately the LOC
responses.
Taken together with previous findings in Chapter 3 the present results indicate
that mental representation of prioritized stimulus is strengthened by emotional arousal
elsewhere in the brain, whereas processing of non-prioritized stimulus was more reduced
with arousal.

65

CHAPTER 5. CONCLUSION

One of the most critical aspects of our visual processing is that it allows us to be
selective about what we attend to. Being able to focus on some aspects of incoming
perceptual stimuli is critical for being able to process and respond to high priority stimuli
under threatening or critical circumstances. However, the selective process that biases in
favor of high-priority stimuli is done at the expense of low-priority stimuli. Such a trade-
off occurs because of our limited processing resources. According to the ABC model,
such competitive processes are augmented by emotional arousal. Thus, the model
predicts that arousal simultaneously enhances processing of salient stimuli and impairs
processing of relatively less-salient stimuli.
The study in Chapter 2 tested the ABC predictions by having observers complete
many trials in a visual search task in which the target either always was salient (a 55°
tilted line among 80° distracters) or non-salient (a 55° tilted line among 50° distracters).
Each observer completed one session in an emotional condition, in which visual search
trials were preceded by negative arousing images, and one session in a non-emotional
condition, in which the arousing images were replaced with neutral images (with session
order counterbalanced). Test trials in which the target line had to be selected from among
a set of lines with different tilts revealed that the emotional condition enhanced
identification of the salient target line tilt but impaired identification of the non-salient
target line tilt. Thus, arousal enhanced perceptual learning of salient stimuli but impaired
perceptual learning of non-salient stimuli.
The study in Chapter 3 presented findings from an fMRI experiment to follow up
on behavioral findings in Chapter 2. In particular, the ABC model was tested with a
simple dot-probe task. On each trial, participants were simultaneously exposed to one
face image as a salient cue stimulus and one place image as a non-salient stimulus. A
border around the face cue location further increased its bottom-up saliency. Before these
66

visual stimuli were shown, one of two tones played: one that predicted a shock
(increasing arousal) or one that did not. An arousal-by-saliency interaction in category-
specific brain regions (FFA for salient faces and PPA for non-salient places) indicated
that brain activation associated with processing the salient stimulus was enhanced under
arousal whereas activation associated with processing the non-salient stimulus was
suppressed under arousal.
Consistent with the findings in Chapter 3, the study in Chapter 4 also observed
the same pattern of arousal-priority interaction effect with a different region of interest,
PPA, by showing that arousing compared to non-arousing trials increased PPA activation
when the place target image was salient. On the contrary, if the place image was not
salient, then PPA activation on arousing trials was diminished compared to the non-
arousing trials. The importance of this finding, in addition to the success of replicating
the study in Chapter 3 using a different type of stimuli, was that when a certain stimulus
dominates one part of the network, aspects of its interaction with arousal could be
represented elsewhere in the brain; the study in Chapter 3 showed simultaneous
enhancement in the FFA and inhibition in the PPA, but the study in Chapter 4 showed
that the ABC effect could be occurred even within the same brain region, the PPA,
depending on the priority level. That is, the arousal-priority interaction is a more global
effect throughout mental representation over the brain rather than being limited to a
certain brain region. Indeed the study in Chapter 3 also showed that responses in the IPS
region were also modulated by interactions between arousal and the location of the
salient cue image irrespective of its categorical types (e.g., face or place). In sum, the two
fMRI studies presented here demonstrated for the first time that arousal can enhance
information processing for prioritized stimuli while impairing processing of non-
prioritized stimuli.
Although the current dissertation provides much insight and evidence converging
to support the ABC hypothesis, it is possible that the arousal-priority interaction effect on
67

visual processing is no longer apparent in later life. There is growing evidence suggesting
that age-related visual impairment in terms of perceptual dedifferentiation for multiple
sensory input (Chee et al., 2006; Goh, Suzuki, & Park, 2010; Park et al., 2004; and see
also Madden, 2007), and therefore older adults may experience difficulty in maintaining
their visual attention on relevant information or to filter out unwanted visual information
(Schmitz, Cheng, & De Rosa, 2010; Schmitz, Dixon, Anderson, & De Rosa, 2014).
Furthermore, studies examining the effects of emotion on aging indicate that emotionally
positive information more often attract visual attention and dominate memory in older
adults, known as the ‘positivity effect’ in aging (for more details, see Mather, 2012). As
noted, the present studies used negative stimuli such as negative images or fear-
conditioned tone as they generally induce stronger arousal responses than positive stimuli
(Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001; Lang, Bradley, & Cuthbert, 1998).
Thus, it was not clear whether the current findings are due to the effects of negative
valence or emotional arousal. Previous research reveals that highly arousing positive and
negative stimuli affect subsequent perceptual processing in similar ways; for instance,
like negative arousing pictures, erotic pictures impair perception of visual targets (Most
et al., 2007). However, additional research is needed to test whether, like negative
arousing stimuli, positive arousing stimuli amplify biased competition processes. Future
studies are needed to elucidate the ABC effect on subsequent visual processing in older
adults.
In summary, this dissertation showed that how the priority level of information
interacts with emotional arousal, and changes subsequent visual processing such as
perception and attention based on the ABC model. Three studies demonstrated
augmented visual processing for prioritized stimuli during states of elevated emotional
arousal along with decreased visual processing for non-prioritized stimuli (i.e., ABC
effects). Moreover, the current findings support the prediction of the ABC model that
when people experience emotional arousal, the processes of competition are amplified
68

such that prioritized stimuli are represented even more and non-prioritized stimuli even
less accurately than they would be otherwise. Thus, it goes beyond previous research to
show that arousal does not uniformly enhance perceptual processing, but instead does so
selectively in ways that optimizes visual processing to highly salient stimuli.

69

REFRENCES
Altmann, C. F., Deubelius, A., & Kourtzi, Z. (2004). Shape saliency modulates contextual
processing in the human lateral occipital complex. Journal of Cognitive Neuroscience,
16(5), 794-804
Anderson, A. K., Christoff, K., Stappen, I., Panitz, D., Ghahremani, D., Glover, G., . . . Sobel, N.
(2003). Dissociated neural representations of intensity and valence in human olfaction.
Nat Neurosci, 6(2), 196-202
Anderson, A. K., & Phelps, E. A. (2001). Lesions of the human amygdala impair enhanced
perception of emotionally salient events. Nature, 411(6835), 305-309
Bach, D. R., Flandin, G., Friston, K. J., & Dolan, R. J. (2010). Modelling event-related skin
conductance responses. International Journal of Psychophysiology, 75(3), 349-356
Bach, D. R., Friston, K. J., & Dolan, R. J. (2010). Analytic measures for quantification of arousal
from spontaneous skin conductance fluctuations. International Journal of
Psychophysiology, 76(1), 52-55
Bach, D. R., Talmi, D., Hurlemann, R., Patin, A., & Dolan, R. J. (2011). Automatic relevance
detection in the absence of a functional amygdala. Neuropsychologia, 49(5), 1302-1305
Baluch, F., & Itti, L. (2011). Mechanisms of top-down attention. Trends in neurosciences, 34(4),
210-224
Barnard, P., Ramponi, C., Battye, G., & Mackintosh, B. (2005). Anxiety and the deployment of
visual attention over time. Visual Cognition, 12(1), 181-211
Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad is stronger than
good. Review of general psychology, 5(4), 323
Beck, D. M., & Kastner, S. (2009). Top-down and bottom-up mechanisms in biasing competition
in the human brain. Vision Res, 49(10), 1154-1165
Becker, M. W. (2009). Panic search: fear produces efficient visual search for nonthreatening
objects. Psychol Sci, 20(4), 435-437
Beckmann, C. F., & Smith, S. M. (2004). Probabilistic independent component analysis for
functional magnetic resonance imaging. IEEE Trans Med Imaging, 23(2), 137-152
Bermpohl, F., Pascual-Leone, A., Amedi, A., Merabet, L. B., Fregni, F., Gaab, N., . . . Northoff,
G. (2006). Dissociable networks for the expectancy and perception of emotional stimuli
in the human brain. Neuroimage, 30(2), 588-600
Berntson, G. G., Sarter, M., & Cacioppo, J. T. (2003). Ascending visceral regulation of cortical
affective information processing. European Journal of Neuroscience, 18(8), 2103-2109
Berridge, C. W., & Waterhouse, B. D. (2003). The locus coeruleus-noradrenergic system:
modulation of behavioral state and state-dependent cognitive processes. Brain Res Brain
Res Rev, 42(1), 33-84
Bocanegra, B. R., & Zeelenberg, R. (2009). Dissociating emotion-induced blindness and
hypervision. Emotion, 9(6), 865-873
Boucsein, W. (2012). Electrodermal activity: Springer.
Bradley, M. M., Miccoli, L., Escrig, M. A., & Lang, P. J. (2008). The pupil as a measure of
emotional arousal and autonomic activation. Psychophysiology, 45(4), 602-607
70

Brady, T. F., Konkle, T., Alvarez, G. A., & Oliva, A. (2008). Visual long-term memory has a
massive storage capacity for object details. Proceedings of the National Academy of
Sciences, 105(38), 14325-14329
Brainard, D. H. (1997). The Psychophysics Toolbox. Spat Vis, 10(4), 433-436
Brooks, S. J., Savov, V., Allzén, E., Benedict, C., Fredriksson, R., & Schiöth, H. B. (2012).
Exposure to subliminal arousing stimuli induces robust activation in the amygdala,
hippocampus, anterior cingulate, insular cortex and primary visual cortex: a systematic
meta-analysis of fMRI studies. Neuroimage, 59(3), 2962-2973
Büchel, C., Dolan, R. J., Armony, J. L., & Friston, K. J. (1999). Amygdala–hippocampal
involvement in human aversive trace conditioning revealed through event-related
functional magnetic resonance imaging. The Journal of neuroscience, 19(24), 10869-
10876
Bundesen, C. (1990). A theory of visual attention. Psychol Rev, 97(4), 523-547
Butts, D. A., & Goldman, M. S. (2006). Tuning curves, neuronal variability, and sensory coding.
PLoS Biol, 4(4), e92
Carrasco, M. (2011). Visual attention: the past 25 years. Vision Res, 51(13), 1484-1525
Chee, M. W., Goh, J. O., Venkatraman, V., Tan, J. C., Gutchess, A., Sutton, B., . . . Park, D.
(2006). Age-related changes in object processing and contextual binding revealed using
fMR adaptation. Journal of Cognitive Neuroscience, 18(4), 495-507
Chelazzi, L., Duncan, J., Miller, E. K., & Desimone, R. (1998). Responses of neurons in inferior
temporal cortex during memory-guided visual search. J Neurophysiol, 80(6), 2918-2940
Chica, A. B., Bartolomeo, P., & Valero-Cabré, A. (2011). Dorsal and ventral parietal
contributions to spatial orienting in the human brain. The Journal of Neuroscience,
31(22), 8143-8149
Ciesielski, B. G., Armstrong, T., Zald, D. H., & Olatunji, B. O. (2010). Emotion modulation of
visual attention: categorical and temporal characteristics. PLoS One, 5(11), e13860
Damaraju, E., Huang, Y. M., Barrett, L. F., & Pessoa, L. (2009). Affective learning enhances
activity and functional connectivity in early visual cortex. Neuropsychologia, 47(12),
2480-2487
Deco, G., & Lee, T. S. (2002). A unified model of spatial and object attention based on inter-
cortical biased competition. Neurocomputing, 44, 775-781
Deco, G., & Rolls, E. T. (2005). Neurodynamics of biased competition and cooperation for
attention: a model with spiking neurons. J Neurophysiol, 94(1), 295-313
Dehaene, S., Kerszberg, M., & Changeux, J.-P. (1998). A neuronal model of a global workspace
in effortful cognitive tasks. Proceedings of the National Academy of Sciences, 95(24),
14529-14534
Delplanque, S., N’diaye, K., Scherer, K., & Grandjean, D. (2007). Spatial frequencies or
emotional effects?: A systematic measure of spatial frequencies for IAPS pictures by a
discrete wavelet analysis. Journal of neuroscience methods, 165(1), 144-150
Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annu Rev
Neurosci, 18, 193-222
Dilks, D. D., Julian, J. B., Paunov, A. M., & Kanwisher, N. (2013). The occipital place area is
causally and selectively involved in scene perception. The Journal of Neuroscience, 33(4),
1331-1336
71

Dolcos, F., Kragel, P., Wang, L., & McCarthy, G. (2006). Role of the inferior frontal cortex in
coping with distracting emotions. Neuroreport, 17(15), 1591-1594
Dolcos, F., & McCarthy, G. (2006). Brain systems mediating cognitive interference by emotional
distraction. J Neurosci, 26(7), 2072-2079
Duncan, J. (2006). EPS Mid-Career Award 2004: brain mechanisms of attention. The Quarterly
Journal of Experimental Psychology, 59(1), 2-27
Dunsmoor, J. E., Bandettini, P. A., & Knight, D. C. (2008). Neural correlates of unconditioned
response diminution during Pavlovian conditioning. Neuroimage, 40(2), 811-817
Ebner, N., Riediger, M., & Lindenberger, U. (2010). FACES—A database of facial expressions in
young, middle-aged, and older women and men: Development and validation. Behavior
Research Methods, 42(1), 351-362
Epstein, R., Harris, A., Stanley, D., & Kanwisher, N. (1999). The Parahippocampal Place Area::
Recognition, Navigation, or Encoding? Neuron, 23(1), 115-125
Epstein, R., & Kanwisher, N. (1998). A cortical representation of the local visual environment.
Nature, 392(6676), 598-601
Erez, Y., & Yovel, G. (2014). Clutter modulates the representation of target objects in the human
occipitotemporal cortex. Journal of Cognitive Neuroscience, 26(3), 490-500
Ethofer, T., Bretscher, J., Gschwind, M., Kreifelts, B., Wildgruber, D., & Vuilleumier, P. (2012).
Emotional voice areas: anatomic location, functional properties, and structural
connections revealed by combined fMRI/DTI. Cerebral Cortex, 22(1), 191-200
Fecteau, J. H., & Munoz, D. P. (2006). Salience, relevance, and firing: a priority map for target
selection. Trends Cogn Sci, 10(8), 382-390
Folk, C. L., Remington, R. W., & Johnston, J. C. (1992). Involuntary covert orienting is
contingent on attentional control settings. Journal of Experimental Psychology: Human
Perception and Performance, 18(4), 1030
Freud, E., Rosenthal, G., Ganel, T., & Avidan, G. (2014). Sensitivity to Object Impossibility in
the Human Visual Cortex: Evidence from Functional Connectivity.
Gazzaley, A., Rissman, J., Cooney, J., Rutman, A., Seibert, T., Clapp, W., & D'Esposito, M.
(2007). Functional interactions between prefrontal and visual association cortex
contribute to top-down modulation of visual processing. Cerebral Cortex, 17(suppl 1),
i125-i135
Geng, J. J., Eger, E., Ruff, C. C., Kristjánsson, A., Rotshtein, P., & Driver, J. (2006). On-line
attentional selection from competing stimuli in opposite visual fields: Effects on human
visual cortex and control processes. J Neurophysiol, 96(5), 2601-2612
Goh, J. O., Suzuki, A., & Park, D. C. (2010). Reduced neural selectivity increases fMRI
adaptation with age during face discrimination. NeuroImage, 51(1), 336-344
Grill-Spector, K., Kourtzi, Z., & Kanwisher, N. (2001). The lateral occipital complex and its role
in object recognition. Vision Res, 41(10-11), 1409-1422
Hung, J., Driver, J., & Walsh, V. (2005). Visual selection and posterior parietal cortex: Effects of
repetitive transcranial magnetic stimulation on partial report analyzed by Bundesen's
theory of visual attention. The Journal of Neuroscience, 25(42), 9602-9612
Itti, L., & Koch, C. (2000). A saliency-based search mechanism for overt and covert shifts of
visual attention. Vision Res, 40(10-12), 1489-1506
72

Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene
analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 20(11),
1254-1259
Johnstone, T., Ores Walsh, K. S., Greischar, L. L., Alexander, A. L., Fox, A. S., Davidson, R. J.,
& Oakes, T. R. (2006). Motion correction and the use of motion covariates in multiple‐
subject fMRI analysis. Human brain mapping, 27(10), 779-788
Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: a module in
human extrastriate cortex specialized for face perception. The Journal of Neuroscience,
17(11), 4302-4311
Kastner, S., De Weerd, P., Desimone, R., & Ungerleider, L. G. (1998). Mechanisms of directed
attention in the human extrastriate cortex as revealed by functional MRI. Science,
282(5386), 108-111
Keitel, C., Andersen, S. K., Quigley, C., & Müller, M. M. (2013). Independent effects of
attentional gain control and competitive interactions on visual stimulus processing.
Cerebral Cortex, 23(4), 940-946
Kensinger, E. A., & Corkin, S. (2003). Effect of negative emotional content on working memory
and long-term memory. Emotion, 3(4), 378-393
Kim, J. G., & Biederman, I. (2011). Where do objects become scenes? Cerebral Cortex, 21(8),
1738-1746
Konen, C. S., & Kastner, S. (2008). Representation of eye movements and stimulus motion in
topographically organized areas of human posterior parietal cortex. The Journal of
Neuroscience, 28(33), 8361-8375
Konkle, T., Brady, T. F., Alvarez, G. A., & Oliva, A. (2010). Scene Memory Is More Detailed
Than You Think The Role of Categories in Visual Long-Term Memory. Psychol Sci,
21(11), 1551-1556
Kosslyn, S. M., Shin, L. M., Thompson, W. L., McNally, R. J., Rauch, S. L., Pitman, R. K., &
Alpert, N. M. (1996). Neural effects of visualizing and perceiving aversive stimuli: a PET
investigation. Neuroreport, 7(10), 1569-1576
Kravitz, D. J., Saleem, K. S., Baker, C. I., & Mishkin, M. (2011). A new neural framework for
visuospatial processing. Nature Reviews Neuroscience, 12(4), 217-230
LaBar, K. S., & Cabeza, R. (2006). Cognitive neuroscience of emotional memory. Nature
Reviews Neuroscience, 7(1), 54-64
Lang, P. J., Bradely, M. M., & Cuthbert, B. N. (1999). International affective picture system
(IAPS) : Instruction manual and affective ratings. University of Florida, Center for
Reseach in Psychophysiology, Gainesville
Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1997). Motivated attention: Affect, activation,
and action. Attention and orienting: Sensory and motivational processes, 97-135
Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1998). Emotion, motivation, and anxiety: brain
mechanisms and psychophysiology. Biological psychiatry, 44(12), 1248-1263
Lee, T.-H., Baek, J., Lu, Z.-L., & Mather, M. How arousal modulates the visual contrast
sensitivity function. Emotion
Lee, T.-H., Lee, K., Lee, K.-Y., Choi, J.-S., & Kim, H.-T. (2006). Korea University Facial
Expression Collection: KUFEC. Lab of Behavioral Neuroscience, Department of
Psychology, Korea University.
73

Lee, T.-H., Lim, S.-L., Lee, K.-Y., & Choi, J.-S. (2009). Facilitation of visual processing by
masked presentation of a conditioned facial stimulus. Neuroreport, 20(8), 750-754
Lee, T.-H., Lim, S.-L., Lee, K., Kim, H.-T., & Choi, J.-S. (2009). Conditioning-induced
attentional bias for face stimuli measured with the emotional Stroop task. Emotion, 9(1),
134
Lim, S.-L., Padmala, S., & Pessoa, L. (2008). Affective learning modulates spatial competition
during low-load attentional conditions. Neuropsychologia, 46(5), 1267-1278
Lim, S.-L., Padmala, S., & Pessoa, L. (2009). Segregating the significant from the mundane on a
moment-to-moment basis via direct and indirect amygdala contributions. Proceedings of
the National Academy of Sciences, 106(39), 16841-16846
Lindstrom, B. R., & Bohlin, G. (2011). Emotion processing facilitates working memory
performance. Cogn Emot, 25(7), 1196-1204
Ling, S., & Blake, R. (2009). Suppression during binocular rivalry broadens orientation tuning.
Psychol Sci, 20(11), 1348-1355
Linsley, D., & MacEvoy, S. P. (2014). Evidence for participation by object-selective visual cortex
in scene category judgments. Journal of Vision, 14(9), 19
Lundqvist, D., Flykt, A., & Ö hman, A. (1998). The Karolinska Directed Emotional Faces -
KDEF. Karolinska Institutet. Department of Clinical Neuroscience, Psychology section.
Luppi, P.-H., Aston-Jones, G., Akaoka, H., Chouvet, G., & Jouvet, M. (1995). Afferent
projections to the rat locus coeruleus demonstrated by retrograde and anterograde tracing
with cholera-toxin B subunit and Phaseolus vulgaris leucoagglutinin. Neuroscience,
65(1), 119-160
MacEvoy, S. P., & Epstein, R. A. (2011). Constructing scenes from objects in human
occipitotemporal cortex. Nature Neuroscience, 14(10), 1323-1329
Madden, D. J. (2007). Aging and visual attention. Curr Dir Psychol Sci, 16(2), 70-74
Mather, M. (2007). Emotional arousal and memory binding: An object-based framework.
Perspectives on Psychological Science, 2(1), 33-52
Mather, M. (2012). The emotion paradox in the aging brain. Ann N Y Acad Sci, 1251, 33-49
Mather, M., Canli, T., English, T., Whitfield, S., Wais, P., Ochsner, K., . . . Carstensen, L. (2004).
Amygdala responses to emotionally valenced stimuli in older and younger adults.
Psychol Sci, 15(4), 259-263
Mather, M., Mitchell, K. J., Raye, C. L., Novak, D. L., Greene, E. J., & Johnson, M. K. (2006).
Emotional arousal can impair feature binding in working memory. Journal of Cognitive
Neuroscience, 18(4), 614-625
Mather, M., & Nesmith, K. (2008). Arousal-Enhanced Location Memory for Pictures. J Mem
Lang, 58(2), 449-464
Mather, M., & Sutherland, M. R. (2011). Arousal-biased competition in perception and memory.
Perspect Psychol Sci, 6(2), 114-133
McLean, J., & Waterhouse, B. D. (1994). Noradrenergic modulation of cat area 17 neuronal
responses to moving visual stimuli. Brain research, 667(1), 83-97
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annu
Rev Neurosci, 24, 167-202
Mitchell, K. J., Mather, M., Johnson, M. K., Raye, C. L., & Greene, E. J. (2006). A functional
magnetic resonance imaging investigation of short-term source and item memory for
negative pictures. Neuroreport, 17(14), 1543-1547
74

Mohanty, A., Egner, T., Monti, J. M., & Mesulam, M.-M. (2009). Search for a threatening target
triggers limbic guidance of spatial attention. The Journal of Neuroscience, 29(34), 10563-
10572
Morris, J. S., Friston, K. J., Buchel, C., Frith, C. D., Young, A. W., Calder, A. J., & Dolan, R. J.
(1998). A neuromodulatory role for the human amygdala in processing emotional facial
expressions. Brain, 121 ( Pt 1), 47-57
Most, S. B., Chun, M. M., Johnson, M. R., & Kiehl, K. A. (2006). Attentional modulation of the
amygdala varies with personality. Neuroimage, 31(2), 934-944
Most, S. B., Chun, M. M., Widders, D. M., & Zald, D. H. (2005). Attentional rubbernecking:
cognitive control and personality in emotion-induced blindness. Psychon Bull Rev, 12(4),
654-661
Most, S. B., & Junge, J. A. (2008). Don't look back: Retroactive, dynamic costs and benefits of
emotional capture. Visual Cognition, 16(2-3), 262-278
Most, S. B., Smith, S. D., Cooter, A. B., Levy, B. N., & Zald, D. H. (2007). The naked truth:
Positive, arousing distractors impair rapid target perception. Cognition & Emotion, 21(5),
964-981
Most, S. B., & Wang, L. (2011). Dissociating spatial attention and awareness in emotion-induced
blindness. Psychol Sci, 22(3), 300-305
Motulsky, H., & Christopoulos, A. (2004). Fitting Models to Biological Data using Linear and
Nonlinear Regression. A Practical Guide to Curve Fitting. New York: Oxford University
Press.
Navalpakkam, V., & Itti, L. (2007). Search goal tunes visual features optimally. Neuron, 53(4),
605-617
Ö hman, A., Flykt, A., & Esteves, F. (2001). Emotion drives attention: detecting the snake in the
grass. Journal of Experimental Psychology: General, 130(3), 466
Olatunji, B. O., Ciesielski, B. G., Armstrong, T., & Zald, D. H. (2011). Emotional expressions
and visual search efficiency: specificity and effects of anxiety symptoms. Emotion, 11(5),
1073-1079
Padmala, S., & Pessoa, L. (2008). Affective learning enhances visual detection and responses in
primary visual cortex. The Journal of Neuroscience, 28(24), 6202-6210
Palermo, R., & Rhodes, G. (2007). Are you always on my mind? A review of how face
perception and attention interact. Neuropsychologia, 45(1), 75-92
Park, D. C., Polk, T. A., Park, R., Minear, M., Savage, A., & Smith, M. R. (2004). Aging reduces
neural specialization in ventral visual cortex. Proc Natl Acad Sci U S A, 101(35), 13091-
13095
Pegna, A. J., Landis, T., & Khateb, A. (2008). Electrophysiological evidence for early non-
conscious processing of fearful facial expressions. International Journal of
Psychophysiology, 70(2), 127-136
Pelli, D. G. (1997). The VideoToolbox software for visual psychophysics: transforming numbers
into movies. Spat Vis, 10(4), 437-442
Pernet, C. R., Wilcox, R., & Rousselet, G. A. (2012). Robust correlation analyses: false positive
and power validation using a new open source Matlab toolbox. Frontiers in psychology, 3
Pessoa, L., Kastner, S., & Ungerleider, L. G. (2003). Neuroimaging studies of attention: from
modulation of sensory processing to top-down control. The Journal of Neuroscience,
23(10), 3990-3998
75

Phan, K. L., Wager, T., Taylor, S. F., & Liberzon, I. (2002). Functional neuroanatomy of emotion:
a meta-analysis of emotion activation studies in PET and fMRI. Neuroimage, 16(2), 331-
348
Phelps, E. A. (2006). Emotion and cognition: insights from studies of the human amygdala. Annu
Rev Psychol, 57, 27-53
Phelps, E. A., Ling, S., & Carrasco, M. (2006). Emotion facilitates perception and potentiates the
perceptual benefits of attention. Psychol Sci, 17(4), 292-299
Pourtois, G., Schettino, A., & Vuilleumier, P. (2012). Brain mechanisms for emotional influences
on perception and attention: What is magic and what is not. Biological psychology
Purushothaman, G., & Bradley, D. C. (2005). Neural population code for fine perceptual
decisions in area MT. Nat Neurosci, 8(1), 99-106
Quinlan, P. T., & Johnson, D. P. (2011). The effect of inducing panic search on the detection of
fear-relevant and neutral images. Visual Cognition, 19(6), 762-784
Reisberg, D., & Heuer, F. (2004). Memory for emotional events. New York: Oxford University
Press.
Rissman, J., Gazzaley, A., & D'Esposito, M. (2004). Measuring functional connectivity during
distinct stages of a cognitive task. Neuroimage, 23(2), 752-763
Sakaki, M., Fryer, K., & Mather, M. (2013). Emotion Strengthens High-Priority Memory Traces
but Weakens Low-Priority Memory Traces. Psychological science, 0956797613504784
Sara, S. J. (2009). The locus coeruleus and noradrenergic modulation of cognition. Nature
reviews neuroscience, 10(3), 211-223
Schmitz, T. W., Cheng, F. H., & De Rosa, E. (2010). Failing to ignore: paradoxical neural effects
of perceptual load on early attentional selection in normal aging. The Journal of
Neuroscience, 30(44), 14750-14758
Schmitz, T. W., Dixon, M. L., Anderson, A. K., & De Rosa, E. (2014). Distinguishing attentional
gain and tuning in young and older adults. Neurobiology of Aging
Sehlmeyer, C., Schöning, S., Zwitserlood, P., Pfleiderer, B., Kircher, T., Arolt, V., & Konrad, C.
(2009). Human fear conditioning and extinction in neuroimaging: a systematic review.
PLoS One, 4(6), e5865
Smith, S. D., Most, S. B., Newsome, L. A., & Zald, D. H. (2006). An Emotion-Induced
Attentional Blink Elicited by Aversively Conditioned Stimuli. Emotion, 6(3), 523-527
Szczepanski, S. M., Konen, C. S., & Kastner, S. (2010). Mechanisms of spatial attention control
in frontal and parietal cortex. The Journal of Neuroscience, 30(1), 148-160
Talmi, D., Anderson, A. K., Riggs, L., Caplan, J. B., & Moscovitch, M. (2008). Immediate
memory consequences of the effect of emotion on attention to pictures. Learning &
Memory, 15(3), 172-182
Tohka, J., Foerde, K., Aron, A. R., Tom, S. M., Toga, A. W., & Poldrack, R. A. (2008).
Automatic independent component labeling for artifact removal in fMRI. Neuroimage,
39(3), 1227-1245
Tottenham, N., Borscheid, A., Ellertsen, K., Marcus, D., & Nelson, C. (2002). Categorization of
facial expressions in children and adults: Establishing a larger stimulus set. Journal of
Cognitive Neuroscience, 14, S74
Vecera, S. P., & Farah, M. J. (1994). Does visual attention select objects or locations? Journal of
Experimental Psychology: General, 123(2), 146
76

Vuilleumier, P. (2002). Facial expression and selective attention. Current Opinion in Psychiatry,
15(3), 291-300
Vuilleumier, P. (2005). How brains beware: neural mechanisms of emotional attention. Trends
Cogn Sci, 9(12), 585-594
Vuilleumier, P., & Driver, J. (2007). Modulation of visual processing by attention and emotion:
windows on causal interactions between human brain regions. Philosophical
Transactions of the Royal Society B: Biological Sciences, 362(1481), 837-855
Vuilleumier, P., & Huang, Y.-M. (2009). Emotional attention uncovering the mechanisms of
affective biases in perception. Curr Dir Psychol Sci, 18(3), 148-152
Wang, C.-A., & Munoz, D. P. (2014). Modulation of stimulus contrast on the human pupil
orienting response. European Journal of Neuroscience, 40(5), 2822-2832
Wieser, M. J., McTeague, L. M., & Keil, A. (2011). Sustained preferential processing of social
threat cues: bias without competition? Journal of cognitive neuroscience, 23(8), 1973-
1986
Wilcox, R. (2012). Introduction to robust estimation and hypothesis testing: Academic Press.
Woolrich, M. W., Behrens, T. E., Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2004).
Multilevel linear modelling for FMRI group analysis using Bayesian inference.
Neuroimage, 21(4), 1732-1747
Worsley, K. J. (2001). Statistical analysis of activation images. New York: Oxford University
Press.
Zeelenberg, R., & Bocanegra, B. R. (2010). Auditory emotional cues enhance visual perception.
Cognition, 115(1), 202-206

77

FIGURES
78

FIGURE 1

(A) Stimulus examples in different task-difficulties and output from a computational model (Itti
& Koch, 2000) to derive a saliency map from feature orientation. For the high-salience condition,
the distractor lines were tilted at 80°, creating a 25° difference between them and the 55° target;
therefore iterative spatial competition leads the target’s location to gain further strength while
suppressing surrounding regions. In the low-salience condition, the target and the distractor differ
in tilt only by 5°, and therefore the similarly activated locations in the saliency map inhibit each
other, leading to mutual suppression of all locations in the saliency map. The dashed red circle
indicates the target location, but it was not seen by participants. (B) Learning trials involved
visual search for the 55° target, allowing perceptual learning about that target; probe trials were
interspersed with the learning trials and tested recognition of the target tilt as observers had to
select the 55° target from among a set of five differently tilted lines. Note that stimuli are not
drawn to scale here.

79

FIGURE 2

Averaged “target” responses for each orientation in the probe trials as a function of
emotion and salience. Error bars represent SEM

80

FIGURE 3

Estimated tuning curves for averaged “target” responses as a function of emotion in the
high-salience condition (left) and low-salience condition (right).

81

FIGURE 4

Average across participants of within-participant median learning-trial response times, as
a function of emotion and salience. Error bars represent SEM.

82

FIGURE 5

Schematic illustration of one trial for each fear-conditioning (A) and dot-probe task (B).
In the dot-probe task, participants will be asked to detect the dot's location. According to
the location of both salient face cue and target dot, two different trial types are
determined (salient-location target vs. nonsalient-location target).
83

FIGURE 6

(A) SCR results and (B) Dot-probe task results in the behavioral experiment. (C) Whole-
brain analysis results from the fear-conditioning session. Voxels that showed stronger
activation during CS+ trials than CS- trials during the fear-conditioning task. The regions
overlap with both the salience network (Shirer et al., 2012) and the fear network
(Sehlmeyer et al., 2009). (D) SCR results and (E) Dot-probe task results from the fMRI
experiment. Error bars denote the standard within-subject error term.
*
p < .05,
**
p < .01,
***
p < .001

84

FIGURE 7

(A) ROI results in both FFA as a response area for salient face cues and PPA for non-
salient place cues. (B) Functional connectivity results. The amygdala region (red arrow)
showed greater positive functional connectivity with FFA during CS+ trials than during
CS- trials. The lowered threshold map showed greater positive functional connectivity
with FFA during CS+ than CS- trials in brainstem regions including locus coeruleus
(green arrow) and a greater negative functional connectivity in left PPA region (blue
arrows). *p < .05, **p < .005, †p = .087.
85

FIGURE 8

(A) ROI results in IPS in response to right versus left salient cues in each hemisphere. (B)
Scatter plot of difference values in percentage signal change (CS+ minus CS-) illustrating
the relationship between IPS and FFA/ PPA regions (right). Gray-shaded area indicates
95% bootstrapped CIs. Outlier data (rectangles) were corrected using the robust method
when the correlation was calculated.
*
p < .05,
††
p = .084.
86

FIGURE 9

(A) Schematic illustration of one trial. (B) Stimulus type for the spatial detection task.
The salient image had higher contrast level (80%) than non-salient image (20%)

A
C
B
When place image
was salient
When place image
was non-salient
87

FIGURE 10

Individual PPA ROI masks. Note that different colors indicate each individual.

88

FIGURE 11

(A) SCR results. (B) Pupil diameter change in time series. (C) Whole-brain analysis
results during the fear-conditioning task. The circled area indicates the significantly
activated region at Z = 2.3 with cluster correction (p = .05). The other activated regions
here were thresholded at uncorrected p < .001 for illustrative purpose. Error bars denote ±
within-subject SEM. ** p < .005.

89

FIGURE 12

(A) RT results (B) SCR results (C) Pupil diameter change in time series (D

*
B
CS+
CS-
A
Detection
**
C D
90

FIGURE 13

(A) ROI analyses of PPA activity as a function of place image saliency (salient place
target trial vs. non-salient place trial). The y-axis of ROI plots represents averaged
differences between CS+ and CS-, and thus the value above zero indicates signal
enhancement with the CS+ and value below zero indicates inhibition. (B) Scatter plot of
difference values between CS+ and CS- in both PPA and Amygdala region. (C)
Functional connectivity analyses with PPA seed region. In the salient-place trials, the
LOC region showed negative functional connectivity during CS+ trials than during CS-
trials. Error bars denote ± within-subject SEM. * p < .05; ** p < .01.

91

TABLES

92

CHAPTER2. TABLE 1
Parameters of the best fit for the averaged “target” response in probe trials for the
arousing versus the non-arousing sessions and p values from the comparisons of each
parameter using nested model testing.

Saliency Params
Emotion
Arousing None
High
μ 56.46 57.77
a .39 .33*
σ 5.89 11.87*
Low
μ 56.94 60.51***
a .31 .39****
σ 13.27 9.35***

****p < .0005, ***p <.005, **p <.01, *p <.05

.

93

CHAPTER3. TABLE 2
Whole-brain significant clusters and locations of local maxima during the fear
conditioning session. k = number of voxels; L = left; R = right; H = hemisphere
MNI
k Cluster Regions of Local Maxima Peak Z H x y z
CS+ > CS-
21111 1 Frontal orbital cortex 5.34 R 36 22 -8
Frontal operculum cortex / IFG 5.01 R 48 18 -2

4.80 R 44 20 4
Insular 4.93 R 34 24 4
Caudate 4.71 R 10 12 4
Superior parietal lobule 4.52 R 30 -44 68
1587 2 Insular 5.03 L -30 22 -6
3.68 L -36 8 4
3.26 L -38 10 -6

Frontal operculum cortex 3.99 L -44 20 0

Temporal pole 3.57 L -58 6 -2
Precentral gyrus 3.27 L -58 -2 10
899 3 Supra marginal gyrus, anterior 3.54 L -68 -28 18

Supra marginal gyrus, posterior 3.49 L -64 -44 18

3.13 L -66 -44 24
Heschl's gyrus 3.17 L -40 -22 8
Parietal operculum cortex 3.11 L -48 -32 18
Planum polare 2.92 L -40 -20 -4
714 4 Lingual gyrus 3.18 L 0 -72 -4
3.14 L -4 -72 -4
3.11 L -12 -78 -8
3.04 R 10 -76 -8

3.01 R 8 -70 -6
Cerebellum 3.13 L -4 -64 -14
CS- > CS+
1391 1 Superior frontal gyrus 4.1 L -26 24 56
3.65 L -22 22 46
3.53 L -22 32 52

3.31 L -16 34 44
Cerebral white matter 3.25 L -20 26 6
Frontal pole 3.07 L -20 38 56
1099 2 Temporal fusiform cortex, posterior 3.98 L -34 -40 -14
Sub-Gyral 3.53 L -30 -40 2

Cerebral white matter 3.21 L -22 -28 24

3.1 L -18 -44 12
Inferior temporal gyrus 3.09 L -46 -52 -12
Lingual gyrus 3.04 L -28 -50 6
94

CHAPTER3. TABLE 3

Brain regions showing connectivity with the fusiform face area (FFA) seed region during
the dot-probe session. L = left; R = right; H = hemisphere

CS+ > CS- MNI
Regions Z H x y z
Thalamus 3.83 R 16 -26 -2
Hippocampus 3.49 R 32 -16 -14
Amygdala 3.35 R 26 -12 -12
Putamen 2.79 R 30 -10 -8
Heschl's gyrus 2.31 R 38 -24 12

95

CHAPTER3. TABLE 4
Results from hierarchical linear regression analyses; signal estimates in each region (FFA
and PPA) and arousal condition (CS+ and CS-) were the predictors; reaction times for
salient-location-target trials were the dependent variable. Note that arousal conditions
were coded as 1 (CS+) and -1 (CS-), and thus the negative beta value indicates that
reaction times are faster during the CS+ compared to CS- and vice versa.

Model (predictor) beta SE t
FFA -9.55 4.50 -2.12
*

PPA 6.12 8.94 .69
Arousal (CS-, CS+) -3.54 4.68 -.76
FFA X Arousal -13.39 3.58 -3.74
**

PPA X Arousal 9.51 5.38 1.77
†

**
p < .005,
*
p < .05,
†
p = .09

96

CHAPTER4. TABLE 5
Whole-brain significant clusters and locations of local maxima during the fear
conditioning session. k = number of voxels; L = left; R = right; H = hemisphere
MNI
Z H k x y z
CS+ > CS-
Insular Cortex 4.36 R 27 32 24 -2
Frontal Orbital Cortex 4.25 R 87 32 26 -6
Precentral Gyrus 3.77 R 35 36 -22 66
Postcentral Gyrus 3.20 R 69 46 -22 64
Frontal Operculum Cortex 2.81 R 17 40 24 6
Frontal Pole 2.74 R 9 52 38 -6

CS- > CS+
Postcentral Gyrus 4.90 L 670 -48 -26 62
3.69 R 96 66 -6 18
Precentral Gyrus 4.42 L 295 -40 -20 64
3.79 R 169 60 -2 38
Lateral Occipital Cortex superior 4.15 L 1191 -38 -76 40
3.71 R 583 44 -76 36
Superior Frontal Gyrus 3.75 L 55 -20 28 50
3.16 R 14 24 12 62
Precuneous Cortex 3.58 R 427 10 -54 12
3.57 L 537 -4 -66 20
Frontal Pole 3.48 L 240 -24 40 42
3.38 R 208 26 42 36
Superior Parietal Lobule 3.30 L 97 -28 -48 62
Lingual Gyrus 3.26 L 159 -26 -46 -6
3.07 R 57 20 -54 -12
Middle Frontal Gyrus 3.21 R 43 32 18 56
3.00 L 23 -28 30 40
Temporal Occipital Fusiform Cortex 3.02 R 59 24 -50 -16
2.54 L 8 -24 -48 -12
Paracingulate Gyrus 2.98 L 84 -8 54 4
2.75 R 27 4 50 -4
Subcallosal Cortex 2.98 R 28 4 24 -20
2.65 L 31 -4 30 -16
Frontal Medial Cortex 2.96 R 59 6 40 -14
2.87 L 58 -6 34 -16
Cuneal Cortex
2.95 L 37 -2 -80 34

2.41 R 2 2 -72 24
PCC 2.90 L/R 34 -4 -52 22
Occipital Fusiform Gyrus 2.87 R 8 22 -68 -12
Hippocampus 2.74 L 3 -24 -38 -6
Lateral Occipital Cortex inferior 2.72 L 36 -42 -80 4
ACC 2.68 L/R 10 2 34 -8
Intracalcarine Cortex 2.59 L 17 -6 -78 14
2.49 R 7 10 -66 8
Superior Temporal Gyrus posterior 2.59 R 16 68 -18 8
Planum Temporale 2.55 R 3 62 -20 8
Parahippocampal Gyrus posterior 2.54 L 1 -22 -40 -12
Occipital Pole 2.45 L 1 -32 -92 22
Central Opercular Cortex 2.43 R 1 62 -12 10
Supramarginal Gyrus anterior 2.43 L 1 -50 -30 42
Angular Gyrus 2.33 L 1 -54 -58 20
97

APPENDIX

98

APPENDIX A:

CHAPTER 2. THE ESTIMATED INDIVIDUAL PARAMETERS
INSTEAD OF THE NESTED MODEL TESTING

Averaged estimated parameters: curve position (µ), amplitude (a) and bandwidth
(σ) of tuning curves as a function of conditions. For the curve position parameter (i.e.,
μ), there was a main effect of emotion, F(1,18) = 9.05, p < .01, and an interaction
between emotion and salience F(1,18) = 7.87, p < .05. For the curve amplitude parameter
(i.e., a), there was a significant emotion by salience interaction, F(1,18) = 12.28, p <
.005. The curve bandwidth parameter (i.e., σ) also showed a significant emotion by
salience interaction, F(1,18) = 8.99, p < .01. Error bars represent SEM.

99

APPENDIX B:

CHAPTER 3. WHOLE-BRAIN ANALYSIS OF DOT-PROBE TASK

Whole-brain analysis results in the dot-probe task. Although the main goal of the present study
was to determine the effects of saliency-arousal interactions within ROIs, a group-level analysis (random-
effects) was also performed to model general task-related activation at a group level. This whole-brain
analysis showed greater activation during CS+ trials than CS- trials in an extended network of regions (see
also Table S1 for local maxima regions in the clusters). These cluster activations included so-called
"salience network" regions (Shirer, Ryali, Rykhlevskaia, Menon, & Greicius, 2012) including ACC ( [-2 4
32], Z = 4.92), bilateral insular (L: [-38 16 -2], Z = 3.59; R:[40 8 -10], Z = 4.00), bilateral thalamus (L: [-8 -
18 -2], 3.41; R: [6 -24 6], Z = 3.08), and posterior cingulate gyrus (PCC; [-2 16 48], Z = 3.40).
Additionally, regions involved in the attention network (Corbetta & Shulman, 2002; Pessoa, Kastner, &
Ungerleider, 2002) including right middle frontal gyrus (MFG; [46 28 34], Z = 2.76), right superior parietal
lobule (SPL; [30 -50 68], Z = 3.12), bilateral IPS (L: [-36 -54 46], Z = 3.69; R: [40 -52 46], Z = 4.05),
bilateral IFG (L: [-52 16 4], Z = 3.08; R: [58 16 12], Z = 3.81) had greater activity during the CS+ trials.
Finally, right amygdala ([22 -8 -10], Z = 2.42), bilateral caudate (L: [-8 6 6], Z = 3.19; R: [8 10 6], Z =
3.62) and right Heschl’s gyrus ([50 -20 10], Z = 2.61) also had greater activity during CS+ trials than
during CS- trials.

Whole-brain significant clusters and locations of local maxima during dot-probe session. k =
number of voxels; L = left; R = right; H = hemisphere
MNI
k Cluster Regions of Local Maxima Peak Z H x y z
CS+ > CS-
35525 1
ACC
4.92 L/R -2 4 32

4.9 L/R 0 16 22

Parietal operculum cortex
4.8 L -62 -32 24

Superior frontal gyrus
4.78 L/R 0 30 56
100

Frontal pole
4.76 L -40 40 16

Frontal operculum cortex / IFG
4.71 L -44 18 2
CS- > CS+
691 1
Subcallosal cortex
3.86 L/R 0 14 -18

Frontal medial cortex
3.41 L -4 52 -12

3.39 R 4 42 -18

Frontal pole
3.2 L -2 60 -12

Subcallosal cortex
3.03 R 6 30 -14

2.84 L -4 30 -18

101

APPENDIX C:

CHAPTER 4. COMPARISON BETWEEN STANDARD SCORING AND
GLM METHOD

Scatter plot of SCR difference between values from standard scoring and estimates from
general linear convolution method during the fear-conditioning run.

102

APPENDIX D:

CHAPTER 4. REACTION TIMES AS A FUNCTION OF SALIENT
TARGET IMAGE (SALIENT-OBJECT AND -PLACE TARGET)

Averaged reaction times between CSs depending on the contents of salient image (object
and place image)

103

APPENDIX E:

CHAPTER 4. PPA ROI FOR EACH HEMISPHERE

Mean signal change values depending on salient condition within PPA ROI for each
hemisphere.

104

APPENDIX F:

CHAPTER 4. ROI ANALYSES IN THE LOC

Although the main ROI
analyses were focused on
the PPA region, the brain
activations in the LOC
were examined
additionally since
previous literatures have
shown that the LOC
responds to object images
(Grill-Spector, Kourtzi, &
Kanwisher, 2001), which
were presented on each
trial as a paired target
stimulus. The LOC mask
in each participant was defined by contrasting the averaged brain activity in intact object
blocks with scrambled object blocks and place blocks (mean peak MNI voxel coordinates
of LOC: left [x = -46, y = -72, z = -4] and right [46, -72, -2]; see Figure in left).
Additional ROI analysis within LOC was conducted with a Arousal Condition (2: CS+,
CS-) X Salient Image Type (2: salient place target, non-salient place target) X
Hemisphere (2: left, right) repeated measures ANOVA. As a result, there was only a
main effect of Arousal Condition, F (1, 27) = 4.70, p < .05, indicating that arousing tone
increased LOC activation generally (M
CS+
= .32 vs. M
CS-
= .28) regardless of Salient
Image Type and Hemisphere. These results suggested that the LOC response in the
current study was non-specific for the object image as predicted might be the case.

105

APPENDIX G:

CHAPTER 4. CONNECTIVITY RESULTS

From the connectivity analyses, it was found that the PPA activation had significant
positive functional couplings with the right prefrontal cortex (PFC)/ frontal pole and left
dorsolateral prefrontal cortex (DLPFC) in both salient place and non-salient place trials
under arousal respectively. In addition to these positive relationships, CS+ tone leaded to
decreased coupling with the early visual regions on the salient-place condition.

106

APPENDIX REFERENCE

Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven
attention in the brain. Nat Rev Neurosci, 3(3), 201-215
Grill-Spector, K., Kourtzi, Z., & Kanwisher, N. (2001). The lateral occipital complex and
its role in object recognition. Vision Res, 41(10-11), 1409-1422
Pessoa, L., Kastner, S., & Ungerleider, L. G. (2002). Attentional control of the processing
of neutral and emotional stimuli. Cognitive Brain Research, 15(1), 31-45
Shirer, W., Ryali, S., Rykhlevskaia, E., Menon, V., & Greicius, M. (2012). Decoding
subject-driven cognitive states with whole-brain connectivity patterns. Cerebral
Cortex, 22(1), 158-165

Abstract (if available)

Abstract The carry‐over effects of emotional arousal on visual processing are not consistent across studies. For example, whereas some studies reveal emotion‐induced enhancement of subsequent visual perception (e.g., Becker, 2009

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Asset Metadata

Creator Lee, Tae-Ho (author)

Core Title The emotional arousal effects on visual processing

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Psychology

Publication Date 04/21/2015

Defense Date 02/25/2015

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag arousal biased competition,emotion,emotional arousal,emotion‐induced enhancement and impairment,human fear‐conditioning,OAI-PMH Harvest,perceptual learning,visual perception,visual processing

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Advisor Mather, Mara (committee chair), Itti, Laurent (committee member), Monterosso, John (committee member), Saxbe, Darby (committee member)

Creator Email taehol@usc.edu,tothememory@gmail.com

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