INTERACTION OF ATTENTION AND EMOTION ACROSS DEVELOPMENT AND DISORDER

A Dissertation submitted to the Faculty of the

Graduate School of Arts and Sciences of Georgetown University

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy in Neuroscience

By

Ericka D. Ruiz, M.A.

Washington, DC July 6, 2012

ii

INTERACTION OF ATTENTION AND EMOTION

ACROSS DEVELOPMENT AND DISORDER

Ericka D. Ruiz, M.A.

Thesis Advisor: Chandan J. Vaidya, Ph.D.

ABSTRACT

This dissertation examines how attention and emotion interact in early visual

processing across typical development and in Attention Deficit/Hyperactivity Disorder

(ADHD). To investigate early visual processing, we utilized the attentional blink (AB)

paradigm. The AB is the loss of awareness of a target immediately following detection

of a first target. However, the AB does not occur if the second target contains

emotional information. This modulation of the AB indicates that emotional

information has privileged access to early visual attention. Chapter 2 reports that

independent of the emotional expression, children showed a larger AB than adults for

human faces, suggesting reduced attentional resources in early visual processing.

There was, however, no developmental difference in the extent to which the AB was

reduced for angry faces, indicating that early visual attention to emotionally salient

information is mature in children. Further, typically developing children who were

more anxious, displayed a reduced AB for faces with neutral expression, indicating

heightened early visual attention to socially important information. Chapter 3 reports

that children with ADHD were similar to controls in the magnitude of the AB for

neutral faces and its reduction for angry faces. Thus, early visual attention to human

iii

faces and its modulation by emotional expression is intact in ADHD. Further, ADHD

children with more inattention symptoms showed a larger modulation of the AB by

emotional information. These results have implications for models of how attention

and emotion interact in early visual attentional processing in typical development and

ADHD.

iv

ACKNOWLEDGEMENTS

This dissertation is first dedicated to God, whom I owe everything to. He

opened the doors so that I could continue my education at Georgetown University,

allowed me to find favor and receive financial support to complete my doctoral

training, and has given me the strength, courage and perseverance to complete this

great endeavor.

It brings me great pleasure to acknowledge the generosity, dedication and

guidance of the many individuals who contributed to my thesis work. I cannot

overstate my gratitude to my mentor, Chandan Vaidya. I greatly admire and appreciate

her knowledge, patience, support, and kindness. Many special thanks to my committee

members, Guinevere Eden, Monique Ernst, Ludise Malkova, and Barbara Schwartz,

who supported me in so many ways through this process. I am also grateful to

Professor Merry Sleigh, for introducing me to research and teaching me to love

neuroscience.

I also thank the Interdisciplinary Program in Neuroscience faculty at

Georgetown, and in particular those who taught me more about this expanse ocean we

call neuroscience. This dissertation would not have been possible without the team of

students and research staff in the Developmental Cognitive Neuroscience Lab. Most

notably, I am thankful to those who worked closely with me in the last years, including

Jane DeWire, Bri Flores, Helen Burton, Kelly Barnes, Melanie Stollstorff, and Philip

v

Lee. I give a heartfelt thanks to all the Georgetown graduate and undergraduate

students with whom I shared this experience.

My very deepest thanks goes to my family, whose unconditional love and

endless support motivates my work and yet reminds me of all other things that are

important in life. Thank you to my best friend and love, my husband Christian Ruiz,

who has been my anchor and helped me to balance our family while continuing my

education. To my children, Franco, Esteban, Christian (Kiki) and Joaquin, for being

my motivation to complete this dissertation. To my proud parents, Erick and Angela,

who taught me that education is far greater than riches. To my sister, Clarissa, who has

helped me unconditionally. To my in-laws, Axires and Alicia, whose kindness and

love have infused me with strength. To my Aunt Rina, my cousin Ericka, and friend

Rosita, who selflessly cared for my children so that I could focus on my work. Last

but not least of all, thanks to my Pastor and friend, Dr. Nestor Alvarado, his wife

Ericka and daughters, Michelle and Elizabeth, who have always been there for all

kinds of support. I know there are others who have helped so that I may complete my

degreee, to those I say thank you and give witness that their prayers were not in vain.

vi

TABLE OF CONTENTS

CHAPTER I: INTRODUCTION............................................................................................. 1

CHAPTER II: DEVELOPMENTAL DIFFERENCES IN THE EMOTIONAL MODULATION OF

ATTENTIONAL FUNCTION

Abstract………………................................................................................................. 16

Introduction................................................................................................................... 17

Method…...................................................................................................................... 22

Results…....................................................................................................................... 29

Discussion..................................................................................................................... 35

CHAPTER III: EMOTIONAL MODULATION OF THE ATTENTIONAL FUNCTION IN

CHILDHOOD ATTENTIONAL DEFICIT/HYPERACTIVITY DISORDER

Abstract………………................................................................................................. 42

Introduction................................................................................................................... 43

Method…...................................................................................................................... 48

Results…....................................................................................................................... 54

Discussion..................................................................................................................... 60

CHAPTER IV: GENERAL DISCUSSION............................................................................. 66

REFERENCES.................................................................................................................. 75

vii

LIST OF FIGURES

Figure 1. Rapid serial visual presentation (RSVP) stream for the Attentional Blink

Task………………………………………………………………………….…....….. 28

Figure 2. Mean percentage correct scores as a function of group (adult vs. child),

emotional face of T2 (angry vs. neutral), and lag (2, 4, 8)........................................... 30

Figure 3. Mean percentage correct scores for emotional face of T2 (angry vs. neutral)

and group (adult vs. child)............................................................................................ 33

Figure 4. Correlation between STAI Trait Anxiety and the size of the attentional blink

in children……………................................................................................................. 35

Figure 5. Rapid serial visual presentation (RSVP) stream for the Attentional Blink

Task………………………………………………………………………….…....….. 53

Figure 6. Mean percentage correct scores as a function of group (Control vs. ADHD),

emotional face of T2 (angry vs. neutral), and lag (2, 4, 8)........................................... 55

Figure 7. Correlation between the AB Size with Neutral Faces and Trait Anxiety based

on STAI scores…………………………………………….......................................... 58

Figure 8. Correlation between the Influence of Emotion on Early Attentional

Processing and Inattention symptoms as measured by the ADHD Rating Scale......... 60

1

CHAPTER 1: INTRODUCTION

Emotional events capture our attention instantly. A mundane drive home from

work can become memorable when there is a car accident on the side of the road. A

stern face looking at us in a crowd immediately draws our attention to that person and

makes us more likely to remember that person more than anyone else in that crowd.

When emotional value is added to a stimulus, that stimulus becomes more significant.

The increased significance allows us to remember it more. Attention can be facilitated

by emotion such that emotionally charged stimuli can be consolidated into our memory

faster. The facilitation by emotion may arise from the evolutionary need to identify

important stimuli in the environment, whether it is negative, like a snake, or positive,

like a mother’s face. In this dissertation, I will investigate how emotion influences

attention at the perceptual level across typical development and in a common

attentional disorder of development, Attention Deficit Hyperactivity Disorder

(ADHD).

ATTENTION Attention is the process by which we reduce the flow of information into the

sensory system of the brain by enhancing the relevant or important components of the

input stream while eliminating the distracting ones (Taylor & Fragopanagos, 2005).

Attention can be voluntary and controlled by either top-down or bottom-up processes

(Corbetta & Shulman, 2002). Top down processes are more goal-directed and rely on

a dorsal brain network (Corbetta, Patel, & Shulman, 2008; Corbetta & Shulman, 2002).

Bottom up processes on the other hand are reflexive, sensory driven and rely on a

2

ventral brain network. The ventral network underlies emotional arousal and

motivational processes (Weissman & Prado, 2012). When we study for an exam we

engage our dorsal network to focus on only the material that is necessary for us to

know for the exam. The ventral network is engaged when we see bright neon shirt or

when we see someone crying in a crowd. The difference between these two networks

is the effort required, when we engage our dorsal network, it is more effortful-we must

direct our attention voluntarily, (endogenous). Engaging the ventral network is less

effortful; stimuli capture our attention even if we do not want to direct our attention to

it (exogenous). There has been an increasing interest among researchers to understand

the types of stimuli that capture attention but also how they do it as well.

Attention processing is believed to have two components, an early and late

stage of processing (Corbetta et al., 2008; Corbetta & Shulman, 2002). In the early

stage, information enters through sensory inputs and is selected without full perceptual

analysis. An example of this would be the cocktail party effect, where in a busy and

confusing environment we are alerted by our name being called out; however, we do

not know who or where our name was called from (Kuyper, 1972). In the late stage of

attention processing, information can only be selected on after full perceptual analysis

and recoding into a semantic or categorical representation. A gating mechanism is

believed to be present between early and late stage processing that determines what

information pursues later stage processing (D. Broadbent & Broadbent, 1990).

3

Attention processing is limited in its capacity to process information (Johnston,

1986; Posner, 1993). Information cannot be simultaneously processed if multiple

inputs are receiving large amounts of information. When this does happen, a

bottleneck is believed to occur while determining what is selected for extended

processing to gain awareness and what is not selected (D. Broadbent & Broadbent,

1990). In the presence of high priority information, a mechanism has been established

early on to overcome the limited capacity of attention processing (Anderson, 2005).

Information that is identified as high priority like emotional information, can be

selected or filtered rather easily so that it gain access and pass the bottleneck efficiently

(Pratto & John, 1991). The bottleneck is manifested in the attentional blink (AB)

phenomenon when one target is being processed and subsequent target is presented

immediately after the first target. There is a quick influx of information and the second

target cannot be detected. However if the second target is of high priority like one

containing emotional information, the AB effect can be reduced or even abolished.

Therefore, it is important to understand what makes emotional information gain a

priority status in attention processing.

Emotion

Emotion can be thought of as states that coordinate homeostasis in a complex

environment (Kuyper, 1972). The experience of emotions or the states of emotional

consciousness are what we commonly call “feelings” (Taylor & Fragopanagos, 2005).

When studying selective attention “emotion” can refer to the emotional quality of a

4

stimulus or emotional state of an individual (Yiend, 2010). In this dissertation,

emotion refers to the emotional quality of the stimulus.

Emotion has been densely studied and certain structures and circuits have

emerged as areas important in the processing of emotion. Both cortical and subcortical

structures have been implicated in processing emotions. Higher order sensory cortices

are involved in the perceptual representation of the stimuli and its features. Cortical

structures like the ventrolateral (VLPFC), orbitofrontal (OFC), and prefrontal cortices

are involved in modulating emotional responses in subcortical structures like the

amygdala and ventral striatum. The VLPFC cortex is a modulator of the emotional

responses in the amygdala. When there is reduced activation in the VLPFC it is

thought to reflect a reduced top-down control of the amygdala (Marsh, Gerber, &

Peterson, 2008). The OFC along with the amygdala and ventral striatum mediate the

association of the perceptual representation with an emotional response, cognitive

processing and behavioral motivation (Adolphs, 2003). The amygdala in particular is

suggested to be the encoder of emotional value before or even without the full object-

level perception of the stimuli that permits the conscious awareness of a stimuli

(Taylor & Fragopanagos, 2005). Therefore an extensive circuit is utilized in

processing emotion.

Faces have been used ubiquitously in researching emotion as experimental

stimuli for several reasons. Faces contain not only information about identity, gender,

or age but also convey information about emotion (Ekman & Oster, 1979; Patrik

5

Vuilleumier & Pourtois, 2007). A primary means of communication is done through

the emotional expressions on the face (Ekman & Oster, 1979; Somerville, Fani, &

McClure-Tone, 2011). Basic emotions like anger, fear, disgust, happiness, sadness and

surprise are easily identified across cultures and ages (Ekman & Friesen, 1971). Faces

can be controlled for visual characteristics and because of this there are several

normalized sets readily available to researchers (Somerville et al., 2011). Emotional

faces can be easily used across different age groups and clinical populations because

they do not require semantic processing and do not contain graphic images that can be

disturbing for younger research participants. The availability and ease of using faces

has produced a large literature that allows us to further understand emotion with

different techniques from behavioral to electrophysiological to neuroimaging.

The human brain has developed a way to process emotional faces because of

their evolutionary and social significance early in development. Animal research

shows that among primate species there is a relationship between the size of their

neocortex and their social group (Adolphs, 2003; Dunbar, 2009). The evolutionary

hypothesis is that the larger the social group the greater the need for social skills and

cognitive processes and thus greater brain development to subserve social cognition

(Dunbar, 2009; Whiten, 1997). Human developmental research shows the importance

of faces from infancy in the development of early preference for faces and face-like

stimuli (Batty & Taylor, 2006; Cohen Kadosh & Johnson, 2007). At birth, infants rely

on high contrast features to detect face-like shapes (Lamb & Sherrod, 1981). Infants at

6

3 months, cannot discriminate between emotional expressions, (Nelson, 1987) but will

change their behavior in response to different emotional expressions (Montague &

Walker-Andrews, 2001). By 4 months, infants are able to discriminate between

expressions (T. M. Field, Woodson, Greenberg, & Cohen, 1982) . Longer looking

times were seen in 7 month olds in response to fearful faces, (de Haan, Belsky, Reid,

Volein, & Johnson, 2004). Discrimination of emotional expression improves

dramatically throughout the first year of life, (Nelson, 1987; Somerville et al., 2011).

Emotional faces are evolutionarily and developmentally important stimuli in the

environment.

Facial expressions of emotion are so inherently salient that the human brain has

developed a way to process faces that is independent of basic object recognition.

Components of a face are initially processed individually and subsequently integrated

to perceive a face and its emotional expression (Haxby, Hoffman, & Gobbini, 2000).

Neuroimaging studies have outlined an extensive network that supports face

processing, comprised of visual object processing regions: inferior occipital gyrus,

superior temporal sulcus, anterior temporal pole and the fusiform face area, (LeDoux,

2000; Pessoa, 2002; Patrik Vuilleumier, 2005). Regions traditionally associated with

the ventral network of attention, like the amygdala, posterior cingulate and

orbitofrontal cortex, are also believed to process some aspects of faces (Blagrove &

Watson, 2010; Blasi et al., 2009; Hariri, Bookheimer, & Mazziotta, 2000; LeDoux,

2000). Electrophysiological studies have identified certain potentials in the parts of

7

these regions to be indicative of face processing (Darque, Del Zotto, Khateb, & Pegna,

2012; M. J. Taylor, Batty, & Itier, 2004). Event related potentials (ERPs) are

measured using electroencephalography (EEG) that measure the electrical activity over

time using electrodes placed on the scalp. ERPs have different waveforms that are

either positive or negative which are called components. In the early stages of face

processing, intracranial ERPs recorded from the cortical surface of the inferior

temporal and fusiform gyri have shown that a negative component occurs 200 ms after

the presentation of faces (N200). Another potential has been found with intracranial

depth recordings that is specific to faces, the P180, in the basal occiptotemporal cortex.

One more signal is the N170 that is recorded at the scalp from the occipito-temporal

cortex. The N170 in both adults and children is largest at parieto-temporal locations,

however the latencies decrease from childhood to adulthood. The decrease in latency

with increasing age suggests a faster and more efficient face processing mechanism. In

addition to the N170 there is a positive component that is involved in early face

processing, the “P1”. In children it is rather large and can be found earlier than the

N170. Like the N170, the P1 decreases with age. The amplitudes of these potentials

also differ between adulthood and childhood. The P1’s amplitude decreases with age

while the N170 decreases from early childhood (approximately 4 years of age) to mid

childhood and then increases again from the teenager to adulthood. Collectively all

these results suggest an extensive face processing network that matures over time to be

able to detect finer details, (Somerville et al., 2011; Taylor et al., 2004).

8

It is therefore important to understand how the emotional face processing

mechanism develops and how it might be affected in a developmental disorder.

Attention Deficit Hyperactivity Disorder

Attention Deficit/Hyperactivity Disorder, ADHD, is one of the most common

neuropsychiatric disorders in childhood, affecting 3-7% of school-aged children.

ADHD was first described as “hyperkinesis disorder of childhood,” then Attention

Deficit Disorder, (ADD) and as it currently identified in the DSM-IV, ADHD. ADHD

is characterized by symptoms of inattention, hyperactivity and impulsivity. Depending

on the ratio of symptoms, the DSM-IV recognizes three subtypes, a hyperactive-

impulsive (more hyperactive/impulsive symptoms), inattentive (more symptoms of

inattention), and combined (presence of both hyperactive/impulsive and inattention

symptoms) subtype. ADHD’s symptoms interfere with scholastic and social

functioning and significantly impact quality of life. For over 20 years, ADHD has

been conceptualized as a disorder of the prefrontal cortex and is still diagnosed on

behavioral reports from educators, parents, and clinicians. Children with ADHD are

known to have problems filtering information, selective visual attention, (the ability to

focus on relevant information while ignoring irrelevant distractors) (Mason,

Humphreys, & Kent, 2005) all related to a core cognitive deficit in a top down process,

executive control (Schachar, Tannock, Marriott, & Logan, 1995). Executive control is

ascribed to a nigrostriatal network, which utilizes a similar structure like the dorsal

network of attention, the lateral frontal cortex. Recently, researchers have questioned

9

the belief that ADHD is exclusively a result of the deficit in executive control.

Evaluation of early studies of selective visual attention, showed that children with

ADHD despite being distracted by irrelevant stimuli were no more vulnerable to

irrelevant information than controls (Castel, Lee, Humphreys, & Moore, 2011).

Children with ADHD have a stronger preference for small immediate rewards over

larger delayed rewards (E. J. S. Sonuga-Barke, 2002) and high-rate intrusive and

impulsive behavior (excessive talking, interruptions, obnoxious behavior), deficient

communication skills (including eye contact and motor regulation), biased and

deficient social cognitive skills (decreased self-awareness, deficient social problem-

solving skills, biased attributions of others’ intentions, inattentiveness to social cues),

and poor emotional regulation (Guevremont & Dumas, 1994; Hoza, 2007; Nixon,

2001; Soliva et al., 2009; Wheeler & Carlson, 1994). All these behaviors cannot be

explained exclusively from a deficit in executive control, therefore a deficit or atypical

process must be present in an area outside of executive control.

Current ADHD research has begun to consider investigating bottom-up

processes like those involved in the ventral network of attention as a potential site of a

deficit for children with ADHD. The mesolimbic network, comprised of the

orbitofrontal cortex, anterior cingulate, ventral striatum, and amygdala, are believed to

contribute to an emotional and motivation dysfunction in ADHD (E. J. S. Sonuga-

Barke, 2003). Of particular concern for this dissertation is the amygdala because of its

10

role both in the ventral network of attention and mesolimbic emotional dysfunction in

ADHD.

ADHD is mainly known by its executive control deficits but there is also

behavioral evidence for emotional dysfunction in ADHD. Boys with ADHD have

more difficulty on tasks that match emotions to situations even when impulsive

responding was controlled (Yuill & Lyon, 2007). This suggests emotional

dysregulation above and beyond executive dysfunction. Social functioning and

emotional regulation vary across ADHD subtypes, such that the combined subtype is

characterized by being less popular, more aggressive, and being more negative after

being disappointed (Maedgen & Carlson, 2000). A performance measure of

impulsivity (Stop Signal Task) accounted for only a small amount of variability in

emotional regulation when a child with ADHD performed a task that was designed to

make the child frustrated (Kühle et al., 2007). In addition, Stop-signal performance did

not change from before to after performing the frustration-inducing task even when the

children with ADHD were told to regulate their emotions explicitly. In a different

study, medication treatment with either methylphenidate or atomoxetine (whose action

influences both the top down and bottom up networks) improved positive emotion

expression on a parent-rated Expression and Emotion Scale for Children (Kratochvil et

al., 2007). Together these studies provide evidence that emotional dysfunction in

ADHD is not accounted for by executive control mediated by dorsal top down

11

processes. Direct investigation of emotional processing in ADHD is necessary to

further provide a comprehensive view of ADHD pathophysiology.

Studies assessing the bottom-up processes directly in ADHD have mainly

utilized rewards. Tangible rewards like money, tokens, or food incentives (Konrad,

Gauggel, Manz, & Schöll, 2000; Luman, Oosterlaan, & Sergeant, 2005), have been

used to improve ADHD’s executive control deficits. However recently there is an

emerging literature indicating the increased salience of socio-emotional information

and its effect on executive control tasks. One group directly compared the effect of

non-social (money) and social (smiling faces) rewards in typically developing children

and adolescents on a response inhibition task. Interestingly they found that both types

of reward increased performance on the response inhibition task and the degree of

influence of the social reward depended on how empathetic a child or adolescent is

(Kohls, Peltzer, Herpertz-Dahlmann, & Konrad, 2009). This provides one of the first

pieces of evidence that socio-emotional information is salient and can enhance

performance in children. Among ADHD children, when a social motivation condition

(children were made to believe they were competing with other children) was

introduced in an interference suppression task (a child adapted flanker), children with

ADHD were able to perform equally well as controls (Geurts, Luman, & van Meel,

2008). Lastly when both monetary and social (positive faces) rewards were directly

compared in children with ADHD, social rewards were found to be more salient and

produced greater improvements than monetary rewards on a response inhibition task

12

(Kohls, Herpertz-Dahlmann, & Konrad, 2009). Socio-emotional information is just as

salient as rewards that engage bottom up processes that recruit the mesolimbic network

in ADHD. By using socio-emotional stimuli we also have insight into how emotional

information is processed by children with ADHD. It is therefore, necessary to study

early emotional processing in ADHD to understand whether the emotional dysfunction

is a by-product of increased bottom up processes that overload the top-down process of

executive control of children with ADHD or if bottom up processes are intact and the

dysfunction present is the result of a weak executive control.

Attentional Blink Paradigm

When the visual system is presented with a large amount of information, it is

only capable of perceiving and consciously remembering a small selection. Think of

the last time you went to a wedding, you can remember parts of the party, the dress the

bride wore, the cake, the tossing of the garter and bouquet. But can you remember:

whether the groom wore a bow tie or tie, whether the flower girl had a colored sash,

whether the bouquet had colored ribbon or not. The selection of the information

involves attention, and if allocation of attentional resources is insufficient,

“inattentional blindness” can occur. If it was a large elaborate wedding you may not

have paid enough attention to the little details because there were too many. A task

that elucidates this phenomenon is the rapid serial visual presentation paradigm

(RSVP), also known as the attentional blink paradigm (Raymond, Shapiro, & Arnell,

1992). In this task, stimuli are presented quickly at about a rate of 10 stimuli per

13

second (100ms). In the RSVP stream, there are two targets, T1 and T2 that are

presented among distractors, with T2 appearing always after T1 at different time lags.

There is a reduced number of T2 targets that are consciously reported when they occur

between 200 and 500ms after T1, resulting in the attentional blink. The attentional

blink demonstrates the temporal limitation of the attentional resources readily available

for information to reach consciousness.

The attentional blink is believed to reflect the transient impairment of post

perceptual attentional mechanisms active at a late stage (after the identification of the

stimulus) at the top-down process level. Attentional resources are depleted while T1 is

being processed. However if there is a salient T2, attentional capture occurs via the

reflexive allocation of attention toward the salient T2 reallocating attentional resources

so that it is processed (Anderson, 2005). Bottom-up processes like that of the ventral

network of attention are believed to be responsible for this enhanced early attentional

processing. Emotional T2s are an example of salient T2s that can be perceived at

early lag intervals, because of the allocation of attentional resources available that

reduce the attentional blink effect. The amygdala has been implied for the preferential

processing of emotion with the attentional blink paradigm (Anderson & Phelps, 2001),

when the amygdala has been compromised due to brain insult, there is no allocation of

attentional resources so that T2 can be fully processed. The attentional blink paradigm

through the use of emotional stimuli can provide insight into how attentional capture

occurs across development and ADHD.

14

Faces can convey various emotions making them even more efficient in

capturing visuo-spatial attention, to the extent that early stages of face processing are

considered to be automatic (Pessoa, 2002; Patrik Vuilleumier, 2005). Because of the

influence that emotional faces exert in early visual processing, it is believed that they

have a preferred status in the perceptual processing stream. Relative to other visual

objects, faces elicit an early enhanced negative component in the lateral occipital scalp

at about 170ms after stimulus presentation which is said to be face specific (Darque et

al., 2012). Therefore for this dissertation, faces will be used in the emotional

attentional blink paradigm.

The use of emotional faces in the attentional blink paradigm has facilitated the

study of how anxiety influences early perceptual processing. Angry schematic faces

reduce the AB effect in normal adults (Maratos, Mogg, & Bradley, 2008). Among

anxious individuals there is heightened sensitivity to negative emotions like anger and

fear (de Jong & Martens, 2007; B. De Martino, Kalisch, Rees, & Dolan, 2008; Fox,

Russo, & Georgiou, 2005). Highly anxious individuals show a reduced AB effect with

fearful faces compared to happy faces (Fox et al., 2005). Women with higher trait

anxiety also show a reduced attentional blink when T2 targets are faces with anger

compared to happy faces (de Jong & Martens, 2007; Fox et al., 2005). Anxious

individuals therefore perceive emotional faces as more salient and thus are processed

more efficiently because less competition is present for attentional resources (B. De

Martino et al., 2008; Fox et al., 2005; Maratos et al., 2008). It is unknown, however,

15

how anxiety influences early emotional perceptual processing across development and

in a disorder that suffers from attentional problems.

The attentional blink has been successfully adapted to investigate early

perceptual processing in children and in ADHD (Garrad-Cole, Shapiro, & Thierry,

2011; Li, Lin, Chang, & Hung, 2004). However to date there is no study investigating

the role emotion plays in early perceptual processing in children and in ADHD.

Research Goals & Rationale

The influence of emotion on attention and how they interact has been a major

focus in cognitive neuroscience. While there is a considerable amount of literature on

how emotion and attention interact across development, little is known on how

emotion captures attention in the perceptual stage of processing. Even less is known

about how this process may be affected in a developmental disorder in which

attentional function is disrupted, such as ADHD. In Study 1, we used the Attentional

blink paradigm to examine the effect of emotional faces on early visual attention in

children and adults. We also investigated whether these effects are associated with

anxiety in children and adults. Study 2 used the Attentional blink paradigm to examine

the effect of emotional faces on early visual attention in children with ADHD. We also

examined whether these effects are associated with ADHD symptoms and anxiety.

16

CHAPTER II: DEVELOPMENTAL DIFFERENCES IN THE EMOTIONAL MODULATION OF

THE ATTENTIONAL BLINK

Abstract

We examined developmental differences in early stages of attention processing

for socio-emotional information. Adults and children (8–14 years) performed an

attentional blink task that required identification of a dog face (T1) and either an angry

or neutral face (T2) among scrambled face distractors. The anticipated attentional

blink phenomenon was found with both neutral faces and angry faces, reduced

detection at earlier lags that increased across later lags, in both adults in children. We

found a developmental difference such that children took more time to detect faces

than adults. Among children, those with higher trait anxiety showed a smaller

magnitude of the attentional blink. Emotional stimuli reduced the attentional blink, as

expected, but this effect did not differ developmentally. However, overall detection of

emotional stimuli was higher for emotional than neutral stimuli, to a larger extent in

children relative to adults. Together, these findings are the first to reveal

developmental differences in early stages of attention processing and its modulation by

emotion.

17

Introduction

When a person blinks, there is information that the visual system will not perceive

and therefore not process; like an eye blink, attention appears to blink as well. When a

person is asked to search for a specific target within a rapid series visual presentation

(RSVP) of irrelevant stimuli (~100ms), upon successful detection of that target, a

second subsequent target will not be perceived nor processed, therefore resulting in an

attentional blink (D. E. Broadbent & Broadbent, 1987; Raymond et al., 1992).

Detection of the second subsequent target improves when there is more time in

between the presentation of the first and second target (e.g. 600ms) than less time (e.g.

200ms). The attentional blink is a natural phenomenon that occurs and has been

instrumental in studying early stages of attention processing.

Theoretical models of the attentional blink suggest a two-stage process that has its

limitations. Detection of a second subsequent target decreases because attentional

resources are depleted when the first target is being processed (Shapiro, Caldwell, &

Sorensen, 1997). All items in the RSVP stream are processed at stage 1, in order to

identify target features. Target selection occurs at Stage 2 and requires short-term

memory. Decreased T2 detection occurs because Stage 1 has priority and therefore

Stage 2 processing cannot begin until Stage 1’s is complete. A bottleneck effect is said

to occur at Stage 2 because it cannot process additional items from Stage 1 until it is

done processing (Arnell, Helion, Hurdelbrink, & Pasieka, 2004; Marvin M. Chun &

Potter, 1995; Jolicoeur, 1998; Martens & Wyble, 2010). It is believed that more

18

efficient consolidation of the first target should reduce the wait at the bottleneck,

resulting in a smaller attentional blink (Arnell, Howe, Joanisse, & Klein, 2006). The

more unique a target is among the distractors, the easier and thus faster processing that

occurs at Stage 2, which results in increased T2 detection at earlier lags (M M Chun,

1997; Marvin M. Chun & Potter, 1995). Recent research has begun to focus on using

salient stimuli like emotions as targets in the attentional blink to investigate early

perceptual processes.

Emotional information has been shown to influence the attentional blink. When

emotional information is introduced as the second subsequent target in the attentional

blink paradigm, the blink is reduced such that the second emotional stimuli can be

detected at earlier time points, (e.g. before 500ms), (Anderson & Phelps, 2001). One

hypothesis for the attenuated attentional blink with emotion is that the increased

salience of an emotional face among non-emotional face results in a low threshold for

detecting the emotional face, reducing the bottleneck effect (de Jong & Martens, 2007).

Another explanation for the reduced attentional blink is evidence suggesting an

alternate pathway that is faster in processing emotional stimuli that engages the

amygdala to reduce the need for attentional resources (Anderson & Phelps, 2001;

Pessoa, 2002). The reduced attentional blink has been confirmed with various types of

emotional stimuli: words (Benedetto De Martino, Strange, & Dolan, 2007), scenes (B.

De Martino et al., 2008), schematic faces (Maratos et al., 2008) and photographs of

19

faces (Stein, Peelen, Funk, & Seidl, 2010). The reduced attentional blink for emotional

information suggests emotion is prioritized in early stages of attention processing.

Developmental studies confirm that emotional information is prioritized in later

stages of attention processing, however little is known about earlier stages. Infants

prefer faces over other stimuli (Grossmann & Johnson, 2007; Johnson, 2005) and are

biased towards happy faces (Faroni, et al., 2007) indicating that full brain maturation is

not necessary to detect and prefer an emotional stimuli. A bias for angry faces

emerges later in early childhood, between 1-3 years, (Vaish, Grossman, & Woodward,

2008), suggesting that a bias for negative emotions develops from a very young age.

In older children (ages 10 to 13 years old) the bias for angry faces created more

interference, on an executive control task that used emotion as irrelevant stimulus, than

in young children (ages 6 to 9 years old) and adults (K. A. Barnes, Kaplan, & Vaidya,

2007). This study suggests that emotional information will compete for attentional

resources even when it is not relevant to the task. This competition for attentional

resources by emotion appears to be more vulnerable in older children than in adults.

Therefore it is necessary to examine developmentally whether emotional information

has a preferred status at early stages of attention processing.

While a few studies have examined early stages of attention processing using the

attentional blink phenomenon in children, none have investigated whether the

magnitude of the attentional blink changes across development with socio-emotional

information. Only one study has looked at how the attentional blink changes across

20

age (Garrad-Cole et al., 2011). In this study, children of seven, twelve, and

adolescents of fifteen years of age were compared to adults on an attentional blink task

that used shapes as targets. All but children of seven years of age recovered from the

attentional blink by 600 ms, these children took over 1000 ms to recover. This study

demonstrated that with increasing age, the attentional blink is reduced. This study

suggests that the profound blink found amongst younger children is due to inefficient

processing at Stage 2, where the bottleneck occurs, and that by 12 years of age children

are showing a similar attentional blink pattern to adolescents and adults. We expect to

replicate with our emotional attentional blink paradigm, that with increasing age the

attentional blink is reduced.

More insight about the attentional blink in children can be drawn from studies that

have focused upon two developmental disorders, Attention Deficit Hyperactivity

Disorder (ADHD; (Mason et al., 2005) and dyslexia (Visser, Boden, & Giaschi, 2004).

Children with ADHD show mixed results with one study showing no differences in the

attentional blink relative to gender-matched controls when letters were used as both

targets and distractors (Mason et al., 2005). Another study has demonstrated evidence

for a prolonged blink using letters as target and distractors as well (Li et al., 2004).

Relative to their age-matched controls, children with dyslexia had a larger attentional

blink when shapes were used as targets among random dot distractors (Visser et al.,

2004). Thus, the attentional blink phenomenon occurs in children as young as 8 years

and developmental disorders with attentional and language problems appear to alter it.

21

In healthy children, individual differences in the magnitude of the attentional blink are

associated with impulsivity, a trait associated with ADHD (Chhabildas, Pennington, &

Willcutt, 2001; Gomez, 2003; Oades, Slusarek, Velling, & Bondy, 2002). Specifically,

higher levels of impulsivity in adolescence were related to two characteristics of the

attentional blink: worse subsequent target detection at early lags and a prolonged blink

(Ray Li, Chen, Lin, & Yang, 2005). Thus, children with higher levels of impulsivity

were characterized as having a larger and more long-lasting attentional blink.

Impulsivity is characteristic of immaturity and therefore, it is possible that younger

children will show a larger and prolonged attentional blink relative to older children

and/or adults. Currently, there is no study investigating differences in early stages of

attention processing using the emotional attentional blink in children.

Differences seen in early stages of attention processing, through the use of the

emotional attentional blink paradigm, have been attributed to individual differences

among healthy adults in their levels of anxiety. One study assessed healthy females

who either scored high or low on measures of anxiety and revealed a bias for a specific

emotion among anxious individuals(Fox et al., 2005). Highly anxious females have a

shortened attentional blink for fearful faces while less anxious females have a

shortened attentional blink for both fearful and happy faces. In another similar study,

selecting high and low socially anxious healthy females, both high and low socially

anxious women had a shortened attentional blink in the presence of an angry face, (de

Jong & Martens, 2007). This study further established the anger superiority effect,

22

which is the tendency for healthy individuals to selectively attend to angry faces,

(Hansen & Hansen, 1988), seen in other studies with other paradigms (Bradley, Mogg,

White, Groom, & de Bono, 1999; Schutter, Putman, Hermans, & van Honk, 2001). It

is unknown whether individual differences in anxiety may also explain the differences

in early stages of attention processing in healthy children with emotional information.

In the present study, we examined developmental differences in the attentional

blink for faces with and without emotional expressions. We hypothesized that children

would have a larger attentional blink with neutral faces than adults because their brains

are still developing and thus are less efficient than adult brains to process a subsequent

target. With regards to the influence of emotion, we hypothesize that children will

benefit the most from the emotional information resulting in a smaller attentional blink

than adults. Lastly, we hypothesize that both state and trait anxiety will be related to

the size of emotional attentional blink, such that more anxious individuals will be more

sensitive to emotional faces and thus have a smaller attentional blink.

Methods

Participants

Thirty eight 7-14 year old children were recruited from the Washington DC

metropolitan area through advertisements. Thirty five undergraduate students (16

female; mean age = 19.3, SD = 1.1 years) were recruited from Georgetown University

Psychology classes. The final sample included 31 children (15 female; mean age =

23

11.5, SD = 1.9 years, Range= 8-14; mean IQ = 111, SD = 14.3, Range= 91-138) and 33

adults (16 female; mean age = 19.4, SD = 1.1 years, Range= 18-22) following

exclusion of 7 children and 2 adults who did not meet performance criteria (see below

in results). All participants were paid $10 for participation. Written informed consent

from adults and parents of children, and assent from children were obtained according

to Georgetown University Institutional Review Board guidelines.

Participants with neurological and psychiatric conditions were excluded by

self-report for adults and standardized measures for children. Exclusion criteria for

children was: 1) Full scale IQ below 85 (estimated using Vocabulary and Block Design

subtests of the Wechsler Abbreviated Scale of Intelligence, (WASI; (Weschler, n.d.).

2) Presence of neurological disorder by parent-report and psychiatric disorder based

upon the Behavior Assessment System for Children (BASC; (Reynolds & Kamphaus,

1992) including conduct disorder. 3) Score below 50 on the Word Attack and Letter

Word Identification subtests of the Woodcock-Johnson reading test to screen for

reading problems (WJ III; (R.W. Woodcock, K.S. McGrew, & N. Mather, 2000).

Stimulus materials

All stimuli were displayed on a white background at a viewing distance of 60 cm on a

17 inch monitor using E-Prime version 1.1 (Psychology Software Tools Inc., Pittsburg,

PA). Twenty color face photographs of adult faces (10 with neutral facial expressions

and 10 with angry facial expressions) were obtained from the NimStim set,

www.macbrain.org; faces did not overlap across tasks. The Emotional Identification

24

Task consisted of six colored photographs of adults with angry and neutral facial

expressions (three of each gender). The Attentional Blink Task consisted of three

classes of stimuli: 1) Two color photographs of dogs, from the website

www.dogbreedinfo.com, were used as the first target stimuli (T1). 2) Twenty eight

colored photographs of scrambled faces were used for the distractor stimuli. Faces of

seven females and seven males with neutral expressions were scrambled with the face

outline preserved and the interior portion scrambled using the scramble feature from

the website http://www.faceresearch.org ). 3) Fourteen colored photographs of faces

with angry and neutral expressions (seven of each gender) were used for the second

target stimuli (T2).

Procedure

First, participants completed the State-Trait Anxiety Inventory questionnaire

[STAI Form Y for adults, and STAI-CH for children; (Spielberger, Edwards,

Montuori, & Lushene, 1983)]. Then, participants completed the Emotional

Identification Task followed by 20 practice trials and 188 test trials of the Attentional

Blink (AB) task.

Emotional Identification Task

The goal of the task was to assess whether participants recognized the

emotional valence of faces accurately. The task consisted of 6 trials. Participants

began the task by pressing the space bar in response to the words “Get Ready!” on the

screen. Either an angry face or neutral face was presented centrally for 2000 ms,

25

following which the participant had to press the number “1” key for “angry” or the

number “2” key for “no emotion” in answer to the question “Is this person:”.

Participants could take as long as they needed to record their response. Recognition

accuracy of 70% or higher was required to move on to complete the AB task. All

subjects achieved this criterion at first attempt.

Attentional Blink task

The AB task consisted of 188 trials divided into four blocks of 47 trials each.

Participants pressed the space bar in response to the words “Get Ready!” on the screen

to start each block; they could take a self-paced break in between blocks. Each trial

began with the presentation of a fixation cross for 750 ms followed by a rapid serial

visual presentation (RSVP) stream of 15 stimuli presented for 100 ms each (see Figure

1). Participants were instructed to look for a dog and informed that sometimes a face

would appear and to pay attention because the pictures came on the screen very fast.

Each trial ended with the a question on the screen “Did you see a dog?”. Participants

pressed “1“ for “yes” or “2” for “no” on the number pad of the keyboard. Following

their response, a second question “Did you see a face?” appeared on the screen. Once

again, participants indicated their response by using the number pad. For each

question, participants had 5000 ms to respond.

The AB task consisted of eight types of trials, two types of catch trials, and six

types of AB trials varying in the facial expression of the T2 stimulus, at each of three

lags. Specifically, catch trials included: 1) 30 T1-only trials, in which only the T1, a

26

dog, appeared in the RSVP stream but no T2 appeared; 2) 32 T2-only trials, in which a

T2 (either an angry or neutral face) appeared in the RSVP stream without a preceding

T1. These trials were included in order to ‘catch’ children with a bias to respond with

“yes” to both questions at the end of the RSVP stream.

On the remaining AB trials, a T2 was either a face with a neutral or angry

expression and followed the T1 with three lags: 1) 21 trials with a neutral T2

appearing 200 ms after the T1, (termed Neutral-Lag 2); 4) 21 trials with an angry T2

appearing 200 ms after the T1, (termed Angry-Lag 2); 5) 21 trials with a neutral T2

appearing 400 ms after the T1 (termed Neutral-Lag 4); 6) 21 trials with an angry T2

appearing 400 ms after the T1, (termed Angry-Lag 4); 7) 21 trials with a neutral T2

appearing 800 ms after the T1 (termed Neutral-Lag 8); 8) 21 trials with an angry T2

appearing 800 ms after the T1 (termed Angry-Lag 8). Further, within the RSVP

stream, the serial position of T1 was varied among stimuli 4, 5, and 6 in the trial

sequence. The position of T1 was varied at these points to minimize missing the T1

because it occurred too early in the sequence that individuals would not have enough

time to adapt to the RSVP presentation and to minimize working memory demands of

remembering they saw the T1 by the end of the trial. Thus, T2 followed each of these

positions at the three lags described above such that it was either the second (Lag 2),

fourth (Lag 4), or eighth (Lag 8) stimulus after the T1. All the trials were

pseudorandomized into blocks so that no more than two successive trials were of the

same type within each block. Blocks were created to allow breaks after 47 trials, and

27

were balanced to include the same number of each trial types. Two versions of the task

were created to counterbalance block presentation order.

28

Figure 1. Rapid serial visual presentation (RSVP) stream for the Attentional Blink

Task. Participants monitored two target stimuli (T1 & T2) embedded among 13

distractor stimuli, each presented for 100ms with no interstimulus interval. The first

target stimulus (T1) was a dog face and the second target stimulus (T2) was a human

face (either neutral or angry). The distractors were scrambled faces. a) T1-only catch

trials in which only the dog face was presented, b) T2-only catch trials in which only

an angry or neutral face was presented, c) Double target trials in both T1, the dog face,

and T2, the human face, were presented. The two targets were separated by either 2, 4

or 8 distractors, (200ms, 400ms, or 800ms, respectively). At the end of the RSVP

stream, participants indicated by key press whether they had seen saw a dog and a face

29

Results

Participants who performed worse than chance (50%) on the T2 catch trial were

excluded from analysis. This criterion resulted in the exclusion of seven children and

two adults from the total cohort of participants. A response was scored as correct if the

participant correctly identified first the T1 and then the T2, as for T1 incorrect trials the

source of T2 errors is unknown (Marvin M. Chun & Potter, 1995). For each

participant, percentage of accurate T2 trials was computed for each of the six types of

AB trials. Percent accuracy was analyzed in a mixed analysis of variance with Group

(Children, Adults) as the between-subjects factor and emotion (T2 - angry, neutral) and

lag (2, 4, 8) as within-subjects factors. Measures of effect size (partial eta-squared for

analyses of variance [ANOVA], Cohen’s d for means, r2 for correlations) are presented

for the attentional blink effects. Figure 2 summarizes the results by trial type and

groups.

30

Figure 2. Mean percentage correct scores as a function of group (adult vs. child),

emotional face of T2 (angry vs. neutral), and lag (2, 4, 8).

We found three main effects. Overall, T2 accuracy was higher in adults than in

children (main effect of group), F(1,62) = 21.77, p< .001, η2=.26 and both groups were

more accurate when the T2 was a face with angry than neutral expression (main effect

of emotion), F(1,62) = 39.84, p < .001, η2=.10. Further, T2 accuracy differed by lag

(main effect of lag), F(2,61) = 25.99, p< .001, η2=.17. Post-hoc tests showed that T2

detection at lag 2 was lower than at lag 4, t(63) = 4.51, p < .001, d= 0.80, and lag 8,

31

t(63), = 6.75, p < .001, d= 1.76. T2 detection at lag 4 was also lower than lag 8, t(63),

= 4.48, p < .001, d= 0.82. These findings indicate that our paradigm produced the

expected attentional blink phenomenon, decreased accuracy at the shortest lag relative

to longer lags.

In addition to these main effects, several key interactions were observed. First,

a significant lag X group interaction was obtained, F(2,61) = 5.39, p < .01, η2=.03,

suggesting that the magnitude of the overall attentional blink (neutral and emotional)

differed by age. Paired t-tests revealed that both groups showed improvement in

overall T2 detection with each increasing lag: Adults - lag 2 versus lag 4, t(32), = 3.45,

p < .005, d= 1.53; lag 4 versus lag 8, t(32), = 3.22, p < .001, d= 0.68, and lag 2 versus

lag 8, t(32), = 4.70, p < .005, d= 2.18; Children - lag 2 versus lag 4, t(30), = 3.06, p <

.01, d= 0.78; lag 4 versus lag 8, t(30), = 4.02, p < .001, d= 1.13, and lag 2 versus lag 8,

t(30), = 5.44, p < .001, d= 2.11. These results demonstrate that both adults and

children had an attentional blink effect, poor T2 accuracy at shorter lags that improved

as the lag increased. However, adults’ accuracy was better at each lag. Independent

samples t-test revealed that adults had higher T2 detection at lag 2, t(62), = 4.52, p <

.001, d= 1.15, lag 4, t(62)= 4.18, p < .001, d= 1.02 , and lag 8, t(62)= 3.84, p < .001, d=

.97. When a difference score was calculated to assess the magnitude of the blink, (i.e.

lag 8 performance minus lag 2), there was a larger magnitude of blink in children than

in adults, t(62), = 2.63, p < .05, d= 0.67, such that children had a more profound blink,

(lower T2 accuracy) than adults.

32

Second, there was also a significant lag X emotion interaction, F(2,61) = 10.97,

p<.001, η2=.05, suggesting that the magnitude of the attentional blink differed by the

nature of the T2. Collapsing across groups, paired t-tests showed a higher T2 accuracy

for angry trials relative to neutral trials at lags 2 and 4 but not lag 8: lag 2, t(63), =

6.37, p < .001, d= 1.52, lag 4, t(63)= 3.25, p < .005, d= 1.02 , and lag 8, t(63)= 1.72, p

= .09, d= .23. Thus, better T2 detection for emotional faces was limited to earlier lags.

Among the angry trials, T2 detection improved from lag 2 to lag 8, t(63)= 3.05, p <

.005, d= 0.93 and also from lag 4 and lag 8, t(63)= 2.74, p < .01, d= 0.59, but not from

lag 2 to lag 4, t(63)= 1.47, p = .15, d= 0.36. Among the neutral trials, T2 detection

improved with increasing lag length: lag 2 and lag 4, t(63), = 5.18, p < .001, d= 1.06;

lag 4 and lag 8, t(63), = 4.29, p < .001, d= 0.89, and lag 2 and lag 8, t(63), = 7.45, p <

.001, d= 2.14. This finding was further substantiated when a difference score was

calculated to assess the magnitude of blink (lag 8 minus lag 2), a significantly larger

blink was found on neutral trials than on angry trials, t(63), = 4.71, p < .001, d= 1.56.

These findings indicate that we observed the expected effect of emotional stimuli on

the attentional blink phenomenon, a shorter blink for angry than neutral faces.

Third, a significant Emotion by Group interaction was found, F(1, 62) = 5.67,

p< .05, η2=.01. Independent sample t-tests revealed better T2 detection (when

collapsing across all lags) in adults than children for angry trials, t(62)= 4.18, p < .001,

d= 1.06 and neutral trials, t(62)= 4.61, p < .001, d= 1.17. Paired t-tests revealed a

significantly higher T2 accuracy for angry than neutral trials in both adults, t(32)=

33

3.61, p < .001, d= 1.47, and children, t(30)= 5.09, p < .001, d= 1.26. When a

difference score was calculated to assess the influence of emotion , (i.e. angry face

trials’ performance minus neutral face trials), children had a larger difference score

than adults, t(62), = 2.38, p < .05, d= 0.60, indicating that accuracy improved much

more with the emotional face in children. Figure 3 summarizes graphically that adults

had better detection of T2 for both angry and neutral faces than children, however

children had a greater improvement in accuracy when an emotional face was presented

as T2. Most importantly, the Group X Lag X Emotion interaction was not significant,

F(2,61) = .632, p =.54, η2=.033 indicating that the effect of the facial expression of the

T2 stimulus did not influence the magnitude of the attentional blink differently in the

two age groups.

Figure 3. Means percentage correct scores for emotional face of T2 (angry vs neutral)

and group (adult vs. child).

34

In order to assess individual differences in state and trait anxiety, both the adult

and child STAI, raw scores were converted to Z-scores. There was a difference in trait

anxiety between adults (raw: mean = 37.4, SD = 12.2; Z-score: mean = 0.27, SD =

1.23) and children (raw: mean = 31.6, SD = 5.14; Z-score: mean = -0.31, SD = .52),

t(60)= 2.37, p < 0.05, d= 0.96) such that adults were more anxious than children. In

lieu of the difference, we then assessed individual differences in the size of the

attentional blink, (indexed by subtracting T2 accuracy for neutral at lag 8 from T2

accuracy for neutral at lag 2), separately in each group. A negative correlation between

the size of the attentional blink and trait anxiety level was shown in children, such that

more anxious children had a smaller attentional blink, (r= -0.41, p < .05, r2= 0.16); this

relationship was not significant in adults (r= 0.21, p = 0.25, r2= 0.04). Lastly to assess

whether trait anxiety was associated with the emotional attentional blink (indexed by

subtracting T2 accuracy for neutral from T2 accuracy for angry faces at Lag 2). No

significant correlations were found in either group with trait anxiety. Furthermore,

state anxiety was also higher in adults (raw: mean = 33.0, SD = 8.68; Z-score: mean=

0.28, SD= 1.18) than children (raw: mean = 28.2, SD= 3.57; Z-score: mean= -0.38,

SD= .49), t(55)= 2.55, p < .001, d= 0.70, but was not associated with either the size of

the attentional blink or its modulation by emotion.

35

Figure 4. Correlation between STAI Trait Anxiety and the size of the attentional blink

in children.

Discussion

We examined developmental differences in early stages of attention processing and

its modulation by emotional stimuli. We found a developmental difference in the

magnitude of the attentional blink for faces such that it was larger in children than in

adults regardless of the nature of T2. Among children, those with higher trait anxiety

showed a smaller magnitude of the attentional blink. Emotional stimuli reduced the

36

attentional blink, as expected, but this effect did not differ developmentally. However,

overall detection of emotional stimuli was higher for emotional than neutral stimuli, to

a larger extent in children relative to adults. Together, these findings are the first to

reveal developmental differences in early stages of attention processing and its

modulation by emotion.

The introduction of socio-emotional information in our paradigm is central to

understanding our findings. Attentional blink paradigms that have been used in

children to date have used non-social stimuli: words, (Anderson & Phelps, 2001),

chinese characters, (Ray Li et al., 2005), letters, (Marois & Ivanoff, 2005), numbers,

(Colzato, Slagter, Spapé, & Hommel, 2008), shapes, (Garrad-Cole et al., 2011). We do

see a larger more profound blink with children than adults as the previous

developmental study that used non-social stimuli, shapes, (Garrad-Cole et al., 2011).

We do not however see a developmental difference between neutral and emotional

faces for the magnitude of the attentional blink, despite seeing the attentional blink

phenomenon (main effect of lag) and a difference among adults and children in the

nature of the emotional face, (significant Emotion x Group interaction). Therefore it is

important to look at our study more closely to understand the influence of emotion

across development in early stages of attention processing. The introduction of social

information, as is the case of with neutral faces, reveals a developmental difference in

early stages of attention processing seen in children having a larger attentional blink

than adults, (a significant Group X Lag interaction is observed when a repeated

37

measures ANOVA is analyzed with the neutral face trials alone). However when

emotional information is introduced, as in the case of the angry faces, we see no

difference in the size of the attentional blink between children and adults, (there is also

no longer a Group x Lag interaction present when a repeated measures ANOVA is run

with only the angry face trials). It maybe that neutral faces are salient and the

introduction of an emotion is not as salient to produce a strong effect or difference in

early stages of attention processing, when comparing neutral and angry faces together.

In order to better assess the influence of emotion a non-social stimulus like shapes,

(that have been previously used), could be additionally incorporated to the task to

better distinguish between the influence of social and emotional information. Another

alternative to assess the influence of emotion in the attentional blink would be to use

the emotional information as the first target. This “reverse” version of the attentional

blink has been used more recently to assess how long an emotional stimulus engages

early attentional processes (de Jong & Martens, 2007). In conclusion the specifics of

our paradigm may in fact explain our lack of a specific developmental difference in the

emotional modulation of early stages of attention processing.

The developmental difference found in the attentional blink can be explained by

recent research showing developmental differences in event-related potential (ERPs)

indicators of early face processing. Developmental ERP studies have shown the

presence of two components that are indicative of early face processing, the N170 and

the P1 (M. J. Taylor et al., 2004). The N170, in young children between 4 and 7 years

38

of age, is found to be bifid, (two peaks) and between 10 and 13 years of age, (our

sample included children ranging from 8 to 14 years of age), the bifid pattern merges

to one, looking more like the single peak adult N170. The bifid N170 in childhood is

reflective of a slower inefficient process that is distributed across different areas, and

as the brain matures, the processing of faces is localized to one region. The P1, the

positive component preceding the N170, is an indicator of earlier stage processing than

the N170. The P1 latency has been shown to decrease with age among a group of

children ranging from 4 to 13 and adults. The larger latency in young children (4 years

to 10 years old) reflects a slower processing speed. In adulthood, myelination,

facilitates neurotransmission that results in faster and more efficient processing (Luna

& Sweeney, 2004; Rubia et al., 2000; Toga, Thompson, & Sowell, 2006). The

presentation of a larger blink by children maybe a result of a larger distributed set of

regions trying to encode each face and thus require more time to not only encode but

communicate to each other in order to successfully identify T2, indicating an immature

face processing network.

Our study continues to show converging evidence for the preferential treatment for

emotion in early attention processing in both adults and children. Emotion modulated

the attentional blink at early lags but its effect is reduced at later lags. The modulation

of emotion was an expected finding since it has been previously shown with other

emotional attentional blink studies (Jefferies, Smilek, Eich, & Enns, 2008; Maratos et

al., 2008; Srivastava & Srinivasan, 2010; Stein et al., 2010). One key difference

39

between our paradigm and previous studies’ is the high accuracy present on the lag 2

trials in the adult group, approximately 87% for the neutral faces and 95% for angry

faces, other studies using faces have ranged from 30%-50% for neutral faces and 65%-

88% for angry faces (de Jong & Martens, 2007; Maratos et al., 2008). In the later lags,

adults perform at near ceiling, over 90%. The higher accuracy could be a result of an

adaptation to our paradigm, the initial target, T1, is a dog face, that is salient enough to

facilitate faster processing of T1 allowing attentional resources to become readily

available for a subsequent target to be processed. The use of a dog face was done to

make the task child-friendly. Both adults and children improved with the presentation

of an angry face as the T2 at any time point, this finding is consistent with other

literature showing a benefit from angry or threatening faces in performance on various

tasks in children (Hadwin et al., 2003; Heim-Dreger, Kohlmann, Eschenbeck, &

Burkhardt, 2006; Perez-Edgar et al., 2010) and adults (Mogg, Bradley, de Bono, &

Painter, 1997; Schub, Meinecke, Abele, & Gendolla, 2006; Schutter et al., 2001).

Lastly, our study provides correlational evidence for a vigilant-avoidant bias

among more anxious individuals for ambiguous emotion (Amir, Foa, & Coles, 1998;

MacLeod & Cohen, 1993; Stopa, 2000). In our study, the neutral faces appeared to be

more salient to highly anxious children, than angry faces in our sample, as indicated by

a smaller attentional blink with neutral faces. Emerging literature suggests that a

critical discriminate among anxious individuals is in how they process ambiguous

information. Anxious individuals appear to perceive ambiguity as threatening and

40

therefore avoid any ambiguous information and it is evident from childhood, (A. P.

Field & Lester, 2010). Neutral faces are considered to provide ambiguous information

in previous child studies, (for review(Leppanen & Nelson, 2009). In our study of early

attentional processing, we see that the neutral faces are very salient to highly anxious

children, since their attentional blink is reduced. It maybe the case that in early stages

of attention processing children are vigilant towards ambiguous stimuli and when

assessed in later stages of attention, they actually display the avoidant bias traditionally

seen in emotion studies (Perez-Edgar et al., 2010). Important to note is that adults did

not show the heightened response to neutral faces. One explanation is that adults

understood that these were neutral faces and thus did not perceive any ambiguity.

Most of the adult studies assessing the role of anxiety in early attentional processing

have selected those adults who score higher on measures of anxiety. In our study we

did not select the more anxious individuals, none of our subjects met the criteria for an

anxiety disorder. In conclusion, further research should be done to confirm this early

vigilant, later avoidant pattern of behavior in more anxious children and investigate if

this pattern might be present in adults as well.

Further studies should continue to investigate early automatic attentional processes.

It is important to differentiate whether the differences we see with emotion are due to

overall salience difference or truly a preference for emotion. The reason we must

distinguish this is because the size of the attentional blink varies depending on the

relationship between the targets and the distractors in the trials, the greater difference

41

between the targets and distractors, the smaller the blink, and increased sensitivity

(Anderson, 2005; Marvin M. Chun & Potter, 1995). Lastly, within the realm of

individual differences, presence of a short allele for the serotonin transporter

corresponds with an increased reactivity to threatening stimuli in an individual on an

emotional go-no-go task (Hariri, 2002; Hariri et al., 2000). Therefore genetic

differences in the serotonin transporter may help explain individual differences in early

automatic attention processing. In conclusion, our study is one of the first pieces of

evidence for developmental differences in the early automatic attention processing and

the increased saliency of both social and emotional information in early attention

processing.

42

CHAPTER III: EMOTIONAL MODULATION OF THE ATTENTIONAL FUNCTION IN

CHILDHOOD ATTENTION DEFICIT/HYPERACTIVITY DISORDER

Abstract

The attentional blink (AB) phenomenon was used to assess the modulation of

emotion in early visual attention in children with Attention Deficit/Hyperactivity

Disorder (ADHD). The AB effect is the temporary loss of perceptual awareness that

follows successful target identification in a rapid serial visual presentation. Emotional

stimuli can either attenuate or abolish the AB effect, which suggests that there is a

preferential access to early visual attention processing for emotional information. In

the present study, we examined the AB effect with neutral and angry faces in 7-14 year

old children with and without an ADHD diagnosis. Children with ADHD exhibited an

AB effect for neutral faces and it was reduced for angry faces to the same extent as

control children. Children with ADHD who were more inattentive benefitted the most

from the presentation of an angry face in early lags. Control children who were more

anxious had a smaller AB effect with neutral faces. Thus, early visual attentional

processing and its modulation by emotion are intact in ADHD, however individual

differences in anxiety and inattention symptoms influence it.

43

Introduction

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common

neuropsychiatric disorders in childhood, affecting 3-7% of school-aged children and

continues to affect between 30% and 60% of adults (Wender, Wolf, & Wasserstein,

2001). ADHD is characterized by symptoms of inattention, hyperactivity and

impulsivity, which are believed to be indicative of a core cognitive deficit in executive

functioning (Barkley, 1997; Cubillo, Halari, Smith, Taylor, & Rubia, 2012). More

recently, emotional problems exhibited by children with ADHD have gained attention

and are thought to contribute to ADHD pathophysiology (Johansen, Aase, Meyer, &

Sagvolden, 2002; Nigg & Casey, 2005; Sonuga-Barke, 2005). Individuals with

ADHD have difficulties in establishing and maintaining social interactions and

relationships (Biederman et al., 1996; Friedman et al., 2003), and the same medications

that aid executive dysfunction in ADHD also reduce emotional dysfunction (Reimherr

et al., 2005; Williams et al., 2008). However, emotional dysfunction in ADHD is not

well characterized currently.

There are differences in the perception of emotional stimuli by individuals with

ADHD. When a positive word was read in a dichotic listening task, children with

ADHD did not respond faster when the word was presented in the right ear, indicating

reduced reactivity to positive stimuli (Becker, Doane, & Wexler, 1993). Adults with

ADHD showed a reduced startle response to pleasant picture scenes, suggesting that

positively valenced stimuli are not salient in ADHD (Conzelmann et al., 2009).

44

Adolescent ADHD boys had more signs of emotional impairment in that they had

higher anxiety and depression levels and difficulty correctly identifying negative (e.g.,

angry and fearful) emotional faces than controls (Williams et al., 2008) . These

children also exhibited an atypical ERP response: reduced occipital lobe activity during

early perceptual analysis of the emotional expression (within 120ms), followed by an

exaggerated amount of activity associated with structural encoding (120-220ms) and

lastly a slowed and reduced amount of temporal brain activity indicative of context

processing (300-400ms). Upon receiving methylphenidate treatment, the boys’ ability

to identify the negative emotions improved and a more normalized ERP response to

emotional faces was observed. Further, adults with ADHD who self reported a

heightened negative response to the emotional face which subsequently interfered with

their ability to successfully categorize an emotional face (Rapport, Friedman, Tzelepis,

Van Voorhis, & Friedman, 2002). Together, these studies suggest that negative

emotional stimuli produce a greater response than positive emotional stimuli in

ADHD.

The attentional blink paradigm is a tool that allows the assessment of early visual

attentional processing with minimal executive control demands. The paradigm

requires a person to search for a specific target within a rapid serial visual processing

(RSVP) stream of irrelevant stimuli (~100ms). Upon successful detection of that

target, a second subsequent target will not be perceived; this effect is referred to as an

attentional blink, AB, (D. E. Broadbent & Broadbent, 1987). Detection of the second

45

subsequent target improves when there is more time in between the presentation of the

first and second target (e.g. 600ms) than less time (e.g. 200ms).

Theoretical models posit that the AB effect indexes information processing

limitations in a two-stage process. Detection of a second subsequent target decreases

because attentional resources are depleted when the first target is being processed

(Shapiro et al., 1997). All items in the RSVP stream are processed at stage 1, in order

to identify target features. Target selection occurs at Stage 2 and requires short-term

memory. Decreased T2 detection occurs because Stage 1 has priority and therefore

Stage 2 processing cannot begin until Stage 1’s is complete. A bottleneck effect is said

to occur at Stage 2 because it cannot process additional items from Stage 1 until it is

done processing (Arnell, Helion, Hurdelbrink, & Pasieka, 2004; Chun & Potter, 1995;

Jolicoeur, 1998; Martens & Wyble, 2010). It is believed that more efficient

consolidation of the first target should reduce the wait at the bottleneck, resulting in a

smaller AB (Arnell et al., 2006). Newer models that incorporate neuroimaging

evidence believe that the bottleneck is caused by an attentional filter under executive

control in frontal regions (Maratos et al., 2008; Martens & Wyble, 2010).

Emotional information has been shown to influence the AB. When emotional

information is introduced as the second subsequent target in the AB paradigm, the

blink is reduced such that the second emotional stimuli can be detected at earlier time

points, before 500ms (Anderson & Phelps, 2001). The reduced AB has also been

confirmed with socio-emotional stimuli like schematic faces (Maratos et al., 2008) and

46

photographs of faces (Stein et al., 2010). Reduction of the AB for socio-emotional

information suggests a prioritization in early attentional processing that eliminates the

bottleneck effect.

Studies of the AB in ADHD show mixed results. There is evidence in both

children and adults with ADHD that the AB is intact with non-social stimuli like letters

(Carr, Henderson, & Nigg, 2006; Mason, Humphreys, & Kent, 2005). Other studies

report an atypical AB with non-social stimuli. In adults with ADHD, a prolonged

blink has been reported with letters in which there is below chance detection of T2 and

an increased amount of lags before recovering from the blink (Hollingsworth,

McAuliffe, & Knowlton, 2001). Increased gaze instability has been proposed to

explain the prolonged blink with letters in adults with ADHD (Armstrong & Munoz,

2003). Children with ADHD have also shown a prolonged blink with numbers and

Chinese characters (Li et al., 2004). Increased symptoms of impulsivity have been

related to a prolonged AB in control children with numbers and Chinese characters (Li,

Chen, Lin, & Yang, 2005) and children with ADHD (Chhabildas et al., 2001; Gomez,

2003; Oades et al., 2002). In contrast, adolescents with the inattentive subtype of

ADHD did not show the expected AB effect with letters such that they had increased

detection at early lags but as the lag interval increased, detection of T2 did not increase

suggesting attentional filtering problems (Carr et al., 2006). Therefore differences in

the AB effect are related to symptoms of ADHD. To date there is no study

investigating the influence of socio-emotional information on the AB in ADHD.

47

Anxiety is a common symptom that is comorbid with ADHD and is associated with

differences in the emotional modulation of the AB. Individuals with increased anxiety

levels have consistently shown more sensitivity to emotional information and thus

exhibited a smaller AB for emotional stimuli (Amir, Taylor, Bomyea, & Badour, 2009;

de Jong & Martens, 2007; Fox et al., 2005; Srivastava & Srinivasan, 2010). It is also

important to investigate the influence of anxiety in ADHD because up to 25% of the

child ADHD population display one or more anxiety conditions (Brown, 2009).

Deficits in the self-regulation of emotion are evident in a substantial number of

children with ADHD and these deficits play an important role in determining

functional impairment and the risk of developing comorbid anxiety disorders

(Anastopoulos et al., 2010).

In the present study, we investigated the influence of socio-emotional information

on the AB in children with ADHD and age and IQ matched controls. It is

hypothesized that both controls and children with ADHD would have a reduced AB

with emotional faces. Children with ADHD would be more sensitive to the emotional

face and thus exhibit a smaller AB effect than controls, since they are more susceptible

to attend to negatively valenced faces (Williams et al., 2008) and are more vulnerable

to salient information with the AB as seen with Mason et al. (2005). In addition, as

reported by Li et al. (2005), symptoms of impulsivity would be related to a larger AB

with neutral faces. Based on findings from Carr, et. al. (2010), symptoms of

inattention would be related to an increased detection of emotional information at early

48

lags and therefore, a smaller emotional AB. Lastly symptoms of anxiety would be

related to increased detection of emotional faces at the early lags and a smaller

emotional AB as well as previously found with De Martino et. al. (2008) and Fox et al.

(2005).

Methods

Participants

Thirty-one children with a diagnosis of ADHD and 35 control children, aged 7-

14 years were recruited from the Washington DC metropolitan area through

advertisements. The final sample included 31 control children (15 female; mean age =

11.5, SD = 1.9 years; mean IQ = 110, SD = 13.9, Range= 91-138) and 27 children with

ADHD (12 female; mean age = 11.0 , SD = 1.8 years; mean IQ = 114, SD =14.9,

Range= 87-140) following exclusion of 7 children who did not meet performance

criteria (see below in results). Children with ADHD consisted of ten inattentive and

seventeen combined subtypes. All participants were paid $10 for participation.

Written informed consent from parents of children, and assent from children were

obtained according to Georgetown University Institutional Review Board guidelines.

Participants with neurological and psychiatric conditions were excluded by

parent-report and the Behavior Assessment System for Children (BASC; (Brown,

2009) including conduct disorder. Additional exclusion criteria for children was: 1)

Full scale IQ below 85 (estimated using Vocabulary and Block Design subtests of the

49

Wechsler Abbreviated Scale of Intelligence, (WASI; (Weschler, n.d.). 2) Score below

50 on the Word Attack and Letter Word Identification subtests of the Woodcock-

Johnson reading test to screen for reading disability (WJ III; (R.W. Woodcock et al.,

2000). All participants with ADHD provided documentation from the diagnosing

clinician (either a physician or a psychologist) which was reviewed by a clinical

psychologist (PL) to confirm DSM-IV criteria were met (i.e., presence of 6 out of 9

symptoms of inattention and 6 out of 9 symptoms of hyperactivity/impulsivity in more

than one setting, present before age 7 years and causing impairment). Further, a parent

for each child completed the ADHD Rating Scale (DuPaul, 1998) to characterize

ADHD symptoms of hyperactivity and inattention.

Age (p = 0.30) and Full Scale IQ (p = 0.43) did not differ between ADHD

(Mean age = 11.0 years, SD = 1.76; Mean IQ = 114.11, SD = 14.94) and control

children (Mean age = 11.5 years, SD = 1.91; Mean IQ = 110.45, SD = 13.86). Relative

to controls, ADHD children had higher symptoms of hyperactivity (ADHD: Mean =

12.5, SD= 5.26; Controls: Mean = 4.05, SD = 3.70; t(40)= 6.04, p < .01) and

inattention (ADHD: Mean = 16.2, SD= 5.39; Controls: Mean = 5.62, SD = 3.92; t(45)=

7.73, p < .001). At the time of testing, six ADHD children were stimulant medication

naïve and the remaining withheld medication for over 24 hours.

Stimulus materials

All stimuli were displayed on a white background at a viewing distance of 60

cm on a 17 inch monitor using E-Prime version 1.1 (Psychology Software Tools Inc.,

50

Pittsburg, PA). Twenty color face photographs of adult faces (10 with neutral facial

expressions and 10 with angry facial expressions) were obtained from the NimStim set,

www.macbrain.org.

All face stimuli were drawn from the NimStim set and did not overlap across

tasks. The Emotional Identification Task consisted of six colored photographs of

adults with angry and neutral facial expressions (three of each gender). The AB Task

consisted of three classes of stimuli: 1) Two color photographs of dogs, from the

website www.dogbreedinfo.com, were used as the first target stimuli (T1). 2) Twenty

eight colored photographs of scrambled faces were used for the distractor stimuli.

Faces of seven females and seven males with neutral expressions were scrambled with

the face outline preserved and the interior portion scrambled using the scramble feature

from the website http://www.faceresearch.org). 3) Fourteen colored photographs of

faces with angry and neutral expressions (seven of each gender) were used for the

second target stimuli (T2).

Procedure

First, participants completed the State-Trait Anxiety Inventory questionnaire

[STAI-CH for children; (Spielberger et al., 1983)] to assess both state and trait anxiety.

Then, participants completed the Emotional Identification Task followed by 20

practice trials and 188 test trials of the AB task.

Emotional Identification Task

51

The goal of the task was to assess whether participants recognized the

emotional valence of faces accurately. The task consisted of 6 trials. Participants

began the task by pressing the space bar in response to the words “Get Ready!” on the

screen. Either an angry face or neutral face was presented centrally for 2000 ms,

following which the participant had to press the number “1” key for “angry” or the

number “2” key for “no emotion” in answer to the question “Is this person:”.

Participants could take as long as they needed to record their response. Upon

successful recognition of at least 70% of the emotional faces, they went on to complete

the AB task.

Attentional Blink task

The AB task consisted of 188 trials divided into four blocks of 47 trials each.

Participants pressed the space bar in response to the words “Get Ready!” on the screen

to start each block; they could take a self-paced break in between blocks. Each trial

began with the presentation of a fixation cross for 750 ms followed by a rapid serial

visual presentation (RSVP) stream of 15 stimuli presented for 100 ms each (see Figure

1). Participants were instructed to look for a dog and informed that sometimes a face

would appear and to pay attention because the pictures came on the screen very fast.

Each trial ended with the a question on the screen “Did you see a dog?”. Participants

pressed “1“ for “yes” or “2” for “no” on the number pad of the keyboard. Following

their response, a second question “Did you see a face?” appeared on the screen. Once

52

again, participants indicated their response by using the number pad. For each

question, participants had 5000 ms to respond.

The AB task consisted of eight types of trials, two types of catch trials, and six

types of AB trials varying in the facial expression of the T2 stimulus, at each of three

lags. Specifically, catch trials included: 1) 30- T1-only trials, in which only the T1, a

dog, appeared in the RSVP stream but no T2 appeared; 2) 32- T2-only trials, in which

a T2 (either an angry or neutral face) appeared in the RSVP stream without a T1

preceding it. These trials were included in order to ‘catch’ any response biases to

answering “yes” to both questions at the end of the RSVP stream.

On the remaining AB trials, a T2 was either a face with a neutral or angry

expression and followed the T1 with three lags: 1) 21 trials with a neutral T2

appearing 200 ms after the T1, (termed Neutral-Lag 2); 4) 21 trials with an angry T2

appearing 200 ms after the T1, (termed Angry-Lag 2); 5) 21 trials with a neutral T2

appearing 400 ms after the T1 (termed Neutral-Lag 4); 6) 21 trials with an angry T2

appearing 400 ms after the T1, (termed Angry-Lag 4); 7) 21 trials with a neutral T2

appearing 800 ms after the T1 (termed Neutral-Lag 8); 8) 21 trials with an angry T2

appearing 800 ms after the T1 (termed Angry-Lag 8). Further, within the RSVP

stream, the serial position of T1 was varied among stimuli 4, 5, and 6 in the trial

sequence. Thus, T2 followed each of these positions at the three lags described above

such that it was either the second (Lag 2), fourth (Lag 4), or eighth (Lag 8) stimulus

after the T1. All the trials were pseudorandomized into blocks so that no more than

53

two successive trials were of the same type within each block. Blocks were created to

allow breaks after 47 trials, and were balanced to include the same number of each trial

types. Two versions of the task were created to counterbalance block presentation

order.

Figure 5. Rapid serial visual stream presentation (RSVP) stream for the AB Task.

Participants monitored two target stimuli (T1 & T2) embedded among 13 distractor

stimuli, each presented for 100ms with no interstimulus interval. The first target

stimulus (T1) was a dog face and the second target stimulus (T2) was a human face

(either neutral or angry). The distractors were scrambled faces. a) T1-only catch trials

in which only the dog face was presented, b) T2-only catch trials in which only an

angry or neutral face was presented, c) Double target trials in both T1, the dog face,

and T2, the human face, were presented. The two targets were separated by either 2, 4

54

or 8 distractors, (200ms, 400ms, or 800ms, respectively). At the end of the RSVP

stream, participants indicated by key press whether they had seen saw a dog and a face.

Results

Participants who performed worse than chance (50%) on the T2 catch trial were

excluded from analysis. This criterion resulted in the exclusion of seven children and

two children with ADHD from the total cohort of participants. A response was scored

as correct if the participant correctly identified first the T1 and then the T2, as for T1

incorrect trials the source of T2 errors is unknown (Marvin M. Chun & Potter, 1995).

For each participant, percentage of accurate T2 trials was computed for each of the six

types of AB trials. Percent accuracy was analyzed in a mixed analysis of variance with

Group (Children, Adults) as the between-subjects factor and emotion (T2 - angry,

neutral) and lag (2, 4, 8) as within-subjects factors. Measures of effect size (partial eta-

squared for analyses of variance [ANOVA], Cohen’s d for means) are presented for the

AB effects. Figure 2 summarizes the results by trial type and groups.

55

Figure 6. Mean percentage correct scores as a function of group (Control vs. ADHD),

emotional face of T2 (angry vs. neutral), and lag (2, 4, 8).

We found two main effects. Both groups were more accurate when the T2 was

a face with angry than neutral expression (main effect of emotion), F(1,56) = 36.77, p

< .001, η2=.04. Further, T2 accuracy differed by lag (main effect of lag), F(2,56) =

21.42, p< .001, η2=.08. Post-hoc tests showed that T2 detection at lag 2 was lower

than at lag 4, t(58) = 3.90, p < .001, d= 1.71, and lag 8, t(58), = 6.66, p < .001, d= 1.93.

T2 detection at lag 4 was also lower than lag 8, t(58), = 3.89, p < .001, d= 0.98. These

56

differences by lag indicate that our adapted paradigm produced the expected AB

phenomenon, decreased accuracy at the shortest lag relative to longer lags.

In addition to these main effects, one key interaction was observed, a

significant Lag X Emotion interaction, F(2,56) = 3.51, p<.001, η2=.01, indicating that

the magnitude of the AB differed by the nature of the T2. Collapsing across groups,

paired t-tests showed a higher T2 accuracy for angry trials relative to neutral trials at

each lag: lag 2, t(58), = 4.89, p < .001, d= 1.40, lag 4, t(58)= 2.97, p < .005, d= 0.72 ,

and lag 8, t(58)= 2.82, p < .01, d= 0.79. Among the angry trials, there was an

improvement in T2 detection from lag 2 to lag 8, t(58)= 3.97, p < .001, d= 1.31 , and

another from lag 4 and lag 8, t(58)= 3.54, p < .001, d= 1.13. Lag 2 and lag 4 did not

show an improvement for the angry faces, t(58)= 1.59, p = .12, d= 0.46. Among the

neutral trials, all the lags were different from each other: lag 2 and lag 4, t(58), = 4.10,

p < .001, d= 1.10; lag 4 and lag 8, t(58), = 6.06, p < .001, d= 0.81, and lag 2 and lag 8,

t(58), = 2.81, p < .01, d= 2.01, showing improved T2 detection with increasing lag

length. This finding was further substantiated when a difference score was calculated

to assess the magnitude of the AB, (i.e. lag 8 performance minus lag 2) which was

significantly larger on neutral trials than on angry trials, t(58), = 2.49, p < .05, d= 0.94.

This finding indicates that we observed the expected effect of emotional stimuli on the

AB phenomenon, a shorter blink for angry than neutral faces.

57

Most importantly, the Group X Lag X Emotion interaction was not significant,

F(2,56) = 0.72, p =.49, η2=.001 indicating that the effect of the facial expression of the

T2 stimulus did not influence the magnitude of the AB differently in both groups.

We examined whether the magnitude of the AB was associated with anxiety. In

order to assess the magnitude of the AB, the neutral face performance (T2 accuracy)

from lag 2 was subtracted from lag 8. In order to assess the influence of emotion in

early attentional processing, neutral face performance (T2 accuracy) for lag 2 was

subtracted from angry face performance at lag 2.

Trait anxiety, as measured with the STAI-C, was higher in children with

ADHD (mean = 37.6, SD = 5.69) relative to control children (mean = 31.6, SD = 5.14,

t(41)= 3.99 p < .001, d= 1.11); no child met the criteria for the presence of a clinical

anxiety disorder. A negative correlation between the magnitude of the AB and trait

anxiety level was shown across control and ADHD children combined, such that more

anxious children had a smaller AB (r= -0.24, p < .05). Examining the groups

separately, trait anxiety correlated significantly with the magnitude of the AB in

control children, (r= - .41, p <.05), but not ADHD children, (r= - .04, p =.43).

Therefore, the correlation in the full group was driven by the control children. Results

are shown in Figure 3. There were no correlations between trait anxiety and the

influence of emotion on the AB across both groups, (r= -.13, p =.18), or within each

group, (ADHD: r= .05, p=.40; Controls: r = -.25, p=.09).

58

Figure 7. Correlation between the AB Size with Neutral Faces and Trait Anxiety based

on STAI scores

No significant correlations were found with state anxiety and the magnitude of

the AB across both groups (r= .16, p =.13) or within ADHD (r= .30, p=.09) or

Controls: r = .06, p=.38. No significant correlations were found between state anxiety

and the influence of emotion on the AB across both groups (r= .11, p =.21) or within

ADHD (r= .25, p=.13) or Controls (r = .01, p=.48).

Lastly, we investigated whether the magnitude of the AB or its modulation with

emotion was associated with symptoms of hyperactivity/impulsivity and inattention

59

measured with the ADHD Rating Scale. A positive correlation was found between

symptoms of inattention and the size of the emotional AB (r= 0.38, p < .05) in children

with ADHD, such that the more inattentive children benefitted more from an angry

face at lag 2 relative to a neutral face. Results are shown in Figure 4. This finding was

exclusive to the ADHD group since it did not appear in control children (r= - .25, p

=.12) and across both groups (r= .02, p =.43). The magnitude of the AB did not

correlate with inattention symptoms, (r= - .14, p =.17). Further, symptoms of

hyperactivity/impulsivity did not show association with either the magnitude of the AB

(r= - .11, p =.23) or the influence of emotion on the AB (r= - .02, p =.43) across both

groups. Neither group alone showed a relationship with hyperactive/impulsivity

symptoms for the magnitude of the AB, (ADHD: r= .09, p =.35,; Controls: r= - .18, p

=.19), and influence of emotion on the AB (ADHD: r= .35, p =.05; Controls: r= - .09, p

=.35).

60

Figure 8. Correlation between the Influence of Emotion on Early Attentional

Processing and Inattention symptoms as measured by the ADHD Rating Scale.

Discussion

We assessed early attentional processing and its modulation by emotional

stimuli in children with ADHD and age and IQ matched controls using the AB

paradigm. The magnitude of the AB was similar in both groups with neutral faces.

Both groups also showed a similar reduction of the AB with angry faces. Thus, early

61

attentional processing was preserved for socio-emotional stimuli in ADHD. The

magnitude of the AB varied by individual differences in anxiety such that children who

were more anxious had a smaller AB with neutral faces. The influence of emotion on

the AB effect varied by individual differences in symptoms of inattention among

children with ADHD such that more inattentive children showed a larger effect of

angry faces on the AB. Together, these findings further the understanding of early

attentional processing in ADHD and typical development.

The present study extends our knowledge of early attention processing in

ADHD in four ways. First, our study is the first to examine the AB effect for socio-

emotional stimuli in ADHD. Prior to this study, the AB had been investigated with

non-social stimuli such as letters (Carr et al., 2006), letters with asterisks, (Mason et

al., 2005), and numbers among Chinese characters (Li et al., 2004), with mixed results.

Our study demonstrates that the AB effect with socio-emotional information is similar

in children with ADHD and controls.

Second, we demonstrate preserved AB effect for both neutral and emotional

faces in both controls and ADHD. One probable explanation for the absence of

differences between groups with our study is our task design. A bottleneck effect

normally occurs at Stage 2 because it cannot process additional items from Stage 1

until it is done processing (Arnell, Helion, Hurdelbrink, & Pasieka, 2004; Chun &

Potter, 1995; Jolicoeur, 1998; Martens & Wyble, 2010). When the first target can be

more efficiently processed because its uniqueness amongst distractors, a smaller AB is

62

seen because there is no bottleneck effect (Arnell et al., 2006). Thus, the more unique

a target is among the distractors, like in our study that required a dog and human face

to be detected among scrambled faces, the easier and thus faster processing that occurs

at both Stage 1 and 2, which results in increased T2 detection at earlier lags (Chun,

1997; Chun & Potter, 1995). We see how the increased contrast between targets and

non-targets improved performance, since our control children had better T2 detection,

above 70% for neutral faces and above 85% for angry faces at lag 2, than previous

non-emotional AB studies that have performance for lag 2 between 50% and 60%,

(Carr, Henderson, & Nigg, 2010; Mason et al., 2005). In conclusion, out task design

may have facilitated early attentional processing of both neutral and emotional stimuli

in both controls and ADHD.

An alternative explanation for our lack of group differences in the emotional

AB arises from the networks responsible for processing emotional information.

Preferential emotion processing is believed to be dependent on the amygdala, since

patients with amygdala lesions do not show a reduction in the AB when emotional

words are presented as the second target (Anderson & Phelps, 2001). The amygdala is

part of the mesolimbic network involved in emotional processing (Sonuga-Barke,

Bitsakou, & Thompson, 2010) and has been highlighted recently in ADHD research.

Increased amygdala activation has been found during making judgments regarding

nose width and perceived hostility of a face. The altered amygdala functioning may

relate to later stages of emotional attention processing since the task required an

63

evaluative judgement (Brotman et al., 2010). In our study, we only required detection

of a face without any evaluation of valence. We can only infer from our study that

behaviorally, amygdala functioning is intact in children and ADHD during early

attentional processing.

Third, our study shows a reduced AB with neutral faces in more anxious

control children. This finding is different from previous studies revealing individuals

with higher anxiety levels have a smaller AB with emotional faces (de Jong &

Martens, 2007; B. De Martino et al., 2008; Fox et al., 2005). We propose that the

neutral faces were perceived as ambiguous, and thus, those who were more anxious

perceived them more negatively and were more vigilant towards these faces.

Emerging literature suggests that anxious individuals process ambiguous information

differently (Becker & Detweiler-Bedell, 2009; Heim-Dreger, Kohlmann, Eschenbeck,

& Burkhardt, 2006; MacLeod & Cohen, 1993; Stopa, 2000). Anxious children appear

to perceive ambiguity as threatening and therefore avoid any ambiguous information

(A. P. Field & Lester, 2010). Faces with neutral expressions are perceived as

ambiguous in children (for review(Leppanen & Nelson, 2009). It maybe the case that

in early stages of attention processing children are more vigilant towards ambiguous

stimuli and when assessed in later stages of attention, they actually display the

avoidant bias traditionally seen in emotion studies (Perez-Edgar et al., 2010). Children

with ADHD despite reporting higher levels of trait anxiety did not show the same

phenomenon as controls. Controls’ anxiety scores seem more evenly distributed across

64

the full range while children with ADHD’s range is smaller if the outliers are dropped,

as seen in Figure 3. Therefore in ADHD, the absence of correlation between anxiety

and the magnitude of the attentional blink is the result of a reduced range of scores.

Fourth, children with ADHD who are more inattentive had a larger effect of

emotion on the AB (i.e., higher accuracy for detecting angry relative to neutral faces

immediately after detection of T1). The inattentive subtype is believed to have a

defective attentional filter (gate) and therefore, poor interference suppression

(Diamond, 2005). In our study, children with ADHD with more inattentive symptoms

benefitted the most from an emotional T2. Emotional stimuli are more salient and

therefore, may have been more difficult to suppress for those with more severe

inattention. This is inline with a previous study indicating that inattention in ADHD is

related more to early-stage information control (attention-gating) instead of response

inhibition (Carr et al., 2006). There are neuroimaging studies suggesting that the

deficit in attention filtering seen in the inattentive subtype is related to a dysfunction in

fronto-parietal cortical attention networks, while the combined and

hyperactive/impulsive subtypes possess late stage control deficits subserved by frontal-

subcortical circuits related to executive control (Adams, Derefinko, Milich, &

Fillmore, 2008; Diamond, 2005; Milich, Balentine, & Lynam, 2006).

It is important to note the limitations of our study. First, ADHD is a

heterogenous disorder comprised of different subtypes and it maybe important to study

more closely subtype differences especially in lieu of our finding that inattentive

65

symptoms relate to the emotional AB effect and the previous inattentive subtype study

that only found a difference in the AB effect among the inattentive subtype (Carr et al.,

2006). Second, our task was manipulated to enhance child engagement and

subsequently performance. It is possible that by making our task too child-friendly, we

lost the ability to detect more subtle differences between controls and ADHD. Lastly,

we only investigate negative and neutral faces, it maybe the case that positive emotions

may should a greater AB since there is evidence that positive emotions are not as

salient as negative emotions in ADHD (Becker et al., 1993; Conzelmann et al., 2009).

In summary, early visual attention processes were sensitive to socio-emotional

information in children with ADHD. Furthermore, both anxiety and inattention

influence early visual attention processes, anxiety reduces the overall magnitude of the

blink while in ADHD inattention symptoms were related to greater sensitivity to angry

faces.

66

CHAPTER IV: GENERAL DISCUSSION

This dissertation presented results that extend the understanding of the role of

socio-emotional information in early visual attention processing in typical development

and in a developmental disorder, Attention Deficit/Hyperactivity Disorder (ADHD).

Chapter 2 and 3 reported the results from studies, which are the first to use the

attentional blink (AB) paradigm to examine early visual attention to human faces with

neutral and negative emotional expression. We found two novel findings: First,

independent of emotional expression, typically developing children showed a larger

attentional blink than adults, indicating reduced attentional resources in early visual

processing. However, the extent to which early visual attention is influenced by

emotionally salient information did not differ between typically developing children

and adults as there were no developmental differences in the attentional blink effect for

angry faces. This finding suggests that processing of emotional stimuli as salient is

mature in late childhood. Second, early visual attention to neutral faces and to

emotionally salient information was intact in ADHD as children with ADHD showed a

similar magnitude of the attentional blink for neutral and angry faces as age and IQ

matched control children. Therefore, attentional deficits observed in ADHD do not

appear to stem from early visual attention, at least, for socio-emotional information.

Anxiety was found to enhance early attention processing in typically

developing children. Prior adult work had shown a reduced AB effect among anxious

individuals with emotional words, faces and scenes (Jefferies et al., 2008; Maratos et

67

al., 2008; Tibboel, Van Bockstaele, & De Houwer, 2011). Our study did not replicate

the reduced AB effect with faces in anxious adults but did find a reduced AB effect

using neutral faces in more anxious children. Neutral faces are high priority

information as there is evidence that they are perceived as ambiguous and therefore

negative for anxious individuals (MacLeod & Cohen, 1993; Leppanen & Nelson,

2009). In this dissertation we suggest that vigilant bias is present among anxious

children for neutral (ambiguous) faces and it would disappear as the brain matures into

adulthood. The perception of the neutral faces as negative among the anxious children

reduced the bottlenecking in attentional processing because they were considered to be

highly important information to process.

In children with ADHD, symptoms of inattention were related to early

attention processing of emotional information. Inattentive subtype of ADHD had been

characterized as showing a lack of the AB effect because of an inefficient attentional

filter (Carr, Henderson, & Nigg, 2010). We show that in the presence of high priority

information like faces, the AB effect is not only intact but in the early lags (early stages

of attentional processing) angry faces reduce it. Our study suggests that the attentional

filter is aided possibly by a subcortical structure like the amygdala to normalize the AB

effect and reduces the bottlenecking effect at the early lags when a high priority

stimulus like an emotional face is presented. Collectively from both studies, individual

differences in anxiety and inattention can influence early attentional processing in the

presence of socio-emotional information.

68

Neural substrates of the Attentional Blink effect

Attentional processing has been shown to be mediated via a dorsal and ventral

pathway. The AB task has furthered our understanding of early attention processing to

include more specific neural substrates. A distributed network across the occipital,

parietal and frontal areas is implicated in the non-emotional AB effect (Martens &

Wyble, 2010). Early visual areas like V1 are activated by both T1 and T2 (Hein,

Alink, Kleinschmidt, & Müller, 2009). When T2 is not reported, there is reduced

activity in the fronto-parietal cortex, specifically the anterior cingulate, lateral

prefrontal, and parietal regions (Kranczioch, Debener, Schwarzbach, Goebel, & Engel,

2005; Marois & Ivanoff, 2005; Marois, Yi, & Chun, 2004). The lateral frontal and

parietal regions are believed to be the neural substrates for the AB bottleneck (Marois

& Ivanoff, 2005) and under top-down control. Our findings from Chapter II suggest

selection for high priority information in early attention processing is critical for

humans therefore a mechanism is established early in childhood in order to detect

threatening or important information in the environment.

The emotional AB recruits other neural substrates than the nonemotional AB.

When faces are used in the emotional AB, the fusiform face area (FFA) is more

activated for correct identification of a face (B. De Martino et al., 2008). The

recruitment of the FFA was related more to the presence of a face in T2 than to

whether the face had a positive or negative emotion. Interestingly the ventral striatum

is more active when T2 is a fearful face than with a neutral face T2, consistent with

69

previous research showing ventral striatum activation during aversive or unpleasant

visual stimuli (Paradiso et al., 1999). When attentional resources are limited, rostral

anterior cingulate cortex (rACC) shows increased activation when T2 is an emotional

face (B. De Martino et al., 2008). The rACC activation correlated with successful

identification of T2. Interesting to note is that the rACC has been shown previously to

have reciprocal connections with areas of emotion processing (ventral striatum,

anterior insula, orbitofrontal cortex, and amygdala) that are also part of the ventral

pathway of attention processing (Bush, Luu, & Posner, 2000; B. De Martino et al.,

2008). Therefore, the rACC is an important structure in the selection of high priority

emotional information under limited attentional resources (B. De Martino et al., 2008).

One critical structure in the interaction of attention and emotion is the

amygdala (Phelps, 2006). The amygdala is strategically positioned in the brain to

communicate with various structures and thus has influence on various stages and

types of processing. The amygdala has reciprocal connections with sensory cortical

processing areas, like the visual cortex (Amaral, Behniea, & Kelly, 2003). Because of

its location it is able to receive emotional information input before gaining awareness

of the emotional information (Romanski & LeDoux, 1992; Whalen, 1998). In ADHD

the amygdala is also a key structure in the mesolimbic pathway responsible for

emotional and motivational dysfunctioning (E. J. S. Sonuga-Barke, 2003). Early

attentional processing requires the amygdala (Anderson & Phelps, 2001) to be intact

for the attentional capture of emotion to occur. From our findings, we suspect that the

70

amygdala is working efficiently to detect emotional information in the environment

quickly. Both emotional influences from the amygdala and attentional influences from

fronto-parietal areas appear to act to amplify emotion or task-relevant information in a

stimulus-specific manner, producing similar increases in fMRI and EEG responses

(Lang et al., 1998; Sabatinelli, Lang, Bradley, Costa, & Keil, 2009). Emotion and

attention effects have distinct sources, they may occur in a parallel or competitive

manner and produce additive (or occasionally interactive) effects on sensory responses

(Brosch, Pourtois, Sander, & Vuilleumier, 2011; Keil, Moratti, Sabatinelli, Bradley, &

Lang, 2005; Pourtois, Schettino, & Vuilleumier, 2012; Rossi & Pourtois, 2012; P

Vuilleumier & Schwartz, 2001). Therefore in our emotional AB effect with adults,

children and ADHD we see an enhanced bottom-up signal that facilitates detection of

emotional information in very early stages of attention processing. Therefore, we

continue to confirm behaviorally an important status for emotional information in early

attention processing.

Future Directions

Of central importance to this dissertation was the behavioral investigation of

the early attentional processing of socio-emotional information. A logical next step is

to investigate the neural mechanism of how socio-emotional information is processed

in early attentional stages. By investigating the neural mechanism, we can enhance our

understanding of brain development by shedding light on how early attentional

71

processing may be an immature process in childhood and evidenced as such by a

distributed network of regions. We can also continue to provide evidence for the

emotional dysfunction seen in ADHD. There are several pieces of evidence that the

amygdala is not normal in ADHD, however our behavioral study in Chapter III would

suggest otherwise. For normal behavioral functioning of the amygdala in ADHD,

certain neural compensations may be made such as the development of a

volumetrically smaller amygdala when compared to controls (Frodl & Skokauskas,

2012) or the hyper activation of the amygdala when rating subjective fear of neutral

faces (Brotman et al., 2010)).

To address the notion that salient information has a preferred status of

attentional processing that utilizes a ventral network of attention, a direct investigation

of non-social and social-emotional stimuli in early attentional processing should be

conducted. There is behavioral evidence emotional value is not the sole contributor to

an enhanced early attentional processing, but aspects of arousal and salience can

produce similar results (Anderson, 2005). Therefore, bottom-up processes that utilize

the ventral network of attention may in fact not be coding for emotion but instead a

more global feature like arousal or salience.

Another potential avenue for future research is to investigate the role of

genetics in early attentional processing. Serotonin (5-hydroxytrptamine; 5-HT), is a

monoamine neurotransmitter synthesized in the raphe nucleus and released all through

out the brain (N. M. Barnes & Sharp, 1999). The serotonin transporter protein (5-

72

HTT) is located on the pre-synaptic terminal, and is responsible terminating the action

of 5-HT. A polymorphism in the serotonin transporter long promoter region, 5-

HTTLPR, of the serotonin transporter gene (SLC6A4) influences 5-HTT mRNA

transcription. A short “S” allele is linked with reduced expression of the serotonin

transporter mRNA relative to the long “l” allele (Hu et al., 2006). Given that

polymorphisms in the serotonin transporter gene have been found to contribute to

individual differences in emotional reactivity, it is likely that the individual differences

we see in anxiety may have its origin in the genetic expression of the serotonin

transporter gene. Expression of the S allele is associated with heightened emotional

reactivity (Pérez-Edgar et al., 2010), higher scores of anxiety-related behaviors (Lesch

et al., 1996), and increased amygdala activation to more negative stimuli (Munafò,

Brown, & Hariri, 2008). Therefore the serotonin transporter gene may help

differentiate between those individuals who are more susceptible to socio-emotional

information in earlier lags of the attentional blink. Another gene of interest would be

the catechol-O-methyltransferase (COMT) gene. COMT is the enzyme that degrades

catecholamines like dopamine in the synapse. A single nucleotide polymorphism

(SNP) in the COMT gene, Val158Met, which substitutes a methionine for valine,

results in lower levels of COMT in the synapse, which in turn leads to less dopamine

breakdown. Individuals with two inherited copies of the met allele (met/met) have less

COMT and more dopamine functioning in the synapse compared to those with two

copies of the val allele (val/val) (Chen et al., 2004). Children and adults who are

73

homozygous for the met allele tend to have better performance on attentional tasks

while also possessing reduced control for emotionally salient stimuli (Blasi et al.,

2005; Diamond, Briand, Fossella, & Gehlbach, 2004). Therefore by investigating the

role of the COMT gene, we can determine if dopamine has a role in the gating

mechanism of the attentional filter in ADHD in early attentional processing. Therefore

genetic studies of early attentional processing should be conducted.

CONCLUSION

Emotional information has the ability to capture attention quickly. We know

that emotion can draw our attention through a ventral pathway of attention that utilizes

bottom-up processing. The amygdala appears to be the center for the attentional

capture of emotion because of its location in the brain and its reciprocal connections

with sensory cortices and prefrontal cortices. The AB paradigm has allowed us to

gauge what and how emotion captures attention. Through the use of the AB we can

continue to further understand the mechanism by which emotion and other salient

information captures our attention.

To our knowledge, these are the first studies to utilize an AB with neutral and

angry faces to assess the early visual attention processing across development and in

ADHD. We found that independent of the emotional expression of a face, children

show a larger AB than adults. We also found that children with ADHD have an intact

AB like control children with both angry and neutral faces. We also found that

74

individual differences can influence early attention processing. Based on our findings,

more research should investigate the development of the AB in children and its neural

substrates. Individual differences in gene expressions should be also investigated to

further understand how one’s genetic code can make one more sensitive to emotional

information.

75

REFERENCES

Adolphs, R. (2003). Cognitive neuroscience of human social behaviour. Nature

Reviews. Neuroscience, 4(3), 165–178. doi:10.1038/nrn1056

Amaral, D. G., Behniea, H., & Kelly, J. L. (2003). Topographic organization of

projections from the amygdala to the visual cortex in the macaque monkey.

Neuroscience, 118(4), 1099–1120.

Amir, N., Foa, E. B., & Coles, M. E. (1998). Automatic activation and strategic

avoidance of threat-relevant information in social phobia. Journal of Abnormal

Psychology, 107(2), 285–290. doi:10.1037/0021-843X.107.2.285

Amir, N., Taylor, C. T., Bomyea, J. A., & Badour, C. L. (2009). Temporal allocation

of attention toward threat in individuals with posttraumatic stress symptoms.

Journal of Anxiety Disorders, 23(8), 1080–1085. doi:doi: DOI:

10.1016/j.janxdis.2009.07.010

Anastopoulos, A. D., Smith, T. F., Garrett, M. E., Morrissey-Kane, E., Schatz, N. K.,

Sommer, J. L., Kollins, S. H., et al. (2010). Self-Regulation of Emotion,

Functional Impairment, and Comorbidity Among ChildrenWith AD/HD.

Journal of Attention Disorders, 15(7), 583–592.

doi:10.1177/1087054710370567

76

Anderson, A. K. (2005). Affective Influences on the Attentional Dynamics Supporting

Awareness. Journal of Experimental Psychology: General, 134(2), 258–281.

doi:10.1037/0096-3445.134.2.258

Anderson, A. K., & Phelps, E. A. (2001). Lesions of the human amygdala impair

enhanced perception of emotionally salient events. Nature, 411(6835), 305–

309. doi:10.1038/35077083

Armstrong, I. T., & Munoz, D. P. (2003). Attentional blink in adults with attention-

deficit hyperactivity disorder. Experimental Brain Research, 152(2), 243–250.

doi:10.1007/s00221-003-1535-0

Arnell, K. M., Helion, A. M., Hurdelbrink, J. A., & Pasieka, B. (2004). Dissociating

sources of dual-task interference using human electrophysiology. Psychonomic

Bulletin & Review, 11(1), 77–83.

Arnell, K. M., Howe, A. E., Joanisse, M. F., & Klein, R. M. (2006). Relationships

between attentional blink magnitude, RSVP target accuracy, and performance

on other cognitive tasks. Memory & Cognition, 34(7), 1472–1483.

Barkley, R. A. (1997). Behavioral inhibition, sustained attention, and executive

functions: constructing a unifying theory of ADHD. Psychological bulletin,

121(1), 65–94.

Barnes, K. A., Kaplan, L. A., & Vaidya, C. J. (2007). Developmental differences in

cognitive control of socio-affective processing. Developmental

Neuropsychology, 32(3), 787–807. doi:10.1080/87565640701539576

77

Barnes, N. M., & Sharp, T. (1999). A review of central 5-HT receptors and their

function. Neuropharmacology, 38(8), 1083–1152.

Batty, M., & Taylor, M. J. (2006). The development of emotional face processing

during childhood. Developmental Science, 9(2), 207–220. doi:10.1111/j.1467-

7687.2006.00480.x

Becker, D. F., Doane, J. A., & Wexler, B. E. (1993). Effects of emotion on perceptual

asymmetry in adolescent inpatients with attention-deficit hyperactivity

disorder. Journal of the American Academy of Child and Adolescent

Psychiatry, 32(2), 318–321; discussion 322–323. doi:10.1097/00004583-

199303000-00012

Becker, M., & Detweiler-Bedell, B. (2009). Early detection and avoidance of

threatening faces during passive viewing. The Quarterly Journal of

Experimental Psychology, 62(7), 1257–1264. doi:10.1080/17470210902725753

Biederman, J., Faraone, S., Milberger, S., Guite, J., Mick, E., Chen, L., Mennin, D., et

al. (1996). A prospective 4-year follow-up study of attention-deficit

hyperactivity and related disorders. Archives of general psychiatry, 53(5), 437–

446.

Blagrove, E., & Watson, D. G. (2010). Visual Marking and Facial Affect: Can an

Emotional Face Be Ignored? Emotion, 10(2), 147–168. doi:doi: DOI:

10.1037/a0017743

78

Blasi, G., Hariri, A. R., Alce, G., Taurisano, P., Sambataro, F., Das, S., Bertolino, A.,

et al. (2009). Preferential Amygdala Reactivity to the Negative Assessment of

Neutral Faces. Biological Psychiatry, 66(9), 847–853. doi:doi: DOI:

10.1016/j.biopsych.2009.06.017

Blasi, G., Mattay, V. S., Bertolino, A., Elvevåg, B., Callicott, J. H., Das, S., Kolachana,

B. S., et al. (2005). Effect of catechol-O-methyltransferase val158met genotype

on attentional control. The Journal of neuroscience: the official journal of the

Society for Neuroscience, 25(20), 5038–5045. doi:10.1523/JNEUROSCI.0476-

05.2005

Bradley, B. P., Mogg, K., White, J., Groom, C., & de Bono, J. (1999). Attentional bias

for emotional faces in generalized anxiety disorder. The British Journal of

Clinical Psychology / the British Psychological Society, 38 ( Pt 3), 267–278.

Broadbent, D., & Broadbent, M. H. (1990). Human attention: the exclusion of

distracting information as a function of real and apparent separation of relevant

and irrelevant events. Proceedings. Biological sciences / The Royal Society,

242(1303), 11–16. doi:10.1098/rspb.1990.0096

Broadbent, D. E., & Broadbent, M. H. (1987). From detection to identification:

response to multiple targets in rapid serial visual presentation. Perception &

Psychophysics, 42(2), 105–113.

Brosch, T., Pourtois, G., Sander, D., & Vuilleumier, P. (2011). Additive effects of

emotional, endogenous, and exogenous attention: behavioral and

79

electrophysiological evidence. Neuropsychologia, 49(7), 1779–1787.

doi:10.1016/j.neuropsychologia.2011.02.056

Brotman, M. A., Rich, B. A., Guyer, A. E., Lunsford, J. R., Horsey, S. E., Reising, M.

M., Thomas, L. A., et al. (2010). Amygdala activation during emotion

processing of neutral faces in children with severe mood dysregulation versus

ADHD or bipolar disorder. The American Journal of Psychiatry, 167(1), 61–

69. doi:10.1176/appi.ajp.2009.09010043

Brown, T. E. (2009). ADHD comorbidities : handbook for ADHD complications in

children and adults. Washington, D.C: American Psychiatric Publishing.

Bush, Luu, & Posner. (2000). Cognitive and emotional influences in anterior cingulate

cortex. Trends in cognitive sciences, 4(6), 215–222.

Carr, L. A., Henderson, J. M., & Nigg, J. T. (2006). Attentional Versus Motor

Inhibition in Adults With Attention-Deficit/Hyperactivity Disorder.

Neuropsychology, 20(4), 430–441. doi:doi: DOI: 10.1037/0894-4105.20.4.430

Carr, L., Henderson, J., & Nigg, J. T. (2010). Cognitive Control and Attentional

Selection in Adolescents with ADHD Versus ADD. Journal of Clinical Child

& Adolescent Psychology, 39(6), 726–740. doi:10.1080/15374416.2010.517168

Castel, A. D., Lee, S. S., Humphreys, K. L., & Moore, A. N. (2011). Memory capacity,

selective control, and value-directed remembering in children with and without

attention-deficit/hyperactivity disorder (ADHD). Neuropsychology, 25(1), 15–

24. doi:10.1037/a0020298

80

Chen, J., Lipska, B. K., Halim, N., Ma, Q. D., Matsumoto, M., Melhem, S., Kolachana,

B. S., et al. (2004). Functional analysis of genetic variation in catechol-O-

methyltransferase (COMT): effects on mRNA, protein, and enzyme activity in

postmortem human brain. American journal of human genetics, 75(5), 807–

821. doi:10.1086/425589

Chhabildas, N., Pennington, B. F., & Willcutt, E. G. (2001). A comparison of the

neuropsychological profiles of the DSM-IV subtypes of ADHD. Journal of

Abnormal Child Psychology, 29(6), 529–540.

Chun, M M. (1997). Temporal binding errors are redistributed by the attentional blink.

Perception & Psychophysics, 59(8), 1191–1199.

Chun, Marvin M., & Potter, M. C. (1995). A two-stage model for multiple target

detection in rapid serial visual presentation. Journal of Experimental

Psychology: Human Perception and Performance, 21(1), 109–127.

doi:10.1037/0096-1523.21.1.109

Cohen Kadosh, K., & Johnson, M. H. (2007). Developing a cortex specialized for face

perception. Trends in Cognitive Sciences, 11(9), 367–369. doi:doi: DOI:

10.1016/j.tics.2007.06.007

Colzato, L. S., Slagter, H. A., Spapé, M. M. A., & Hommel, B. (2008). Blinks of the

eye predict blinks of the mind. Neuropsychologia, 46, 3179–3183.

doi:10.1016/j.neuropsychologia.2008.07.006

81

Conzelmann, A., Mucha, R. F., Jacob, C. P., Weyers, P., Romanos, J., Gerdes, A. B.

M., Baehne, C. G., et al. (2009). Abnormal affective responsiveness in

attention-deficit/hyperactivity disorder: subtype differences. Biological

Psychiatry, 65(7), 578–585. doi:10.1016/j.biopsych.2008.10.038

Corbetta, M., Patel, G., & Shulman, G. L. (2008). The Reorienting System of the

Human Brain: From Environment to Theory of Mind. Neuron, 58(3), 306–324.

doi:10.1016/j.neuron.2008.04.017

Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven

attention in the brain. Nat Rev Neurosci, 3(3), 201–215. doi:10.1038/nrn755

Cubillo, A., Halari, R., Smith, A., Taylor, E., & Rubia, K. (2012). A review of fronto-

striatal and fronto-cortical brain abnormalities in children and adults with

Attention Deficit Hyperactivity Disorder (ADHD) and new evidence for

dysfunction in adults with ADHD during motivation and attention. Cortex; a

Journal Devoted to the Study of the Nervous System and Behavior, 48(2), 194–

215. doi:10.1016/j.cortex.2011.04.007

Darque, A., Del Zotto, M., Khateb, A., & Pegna, A. J. (2012). Attentional modulation

of early ERP components in response to faces: evidence from the attentional

blink paradigm. Brain topography, 25(2), 167–181. doi:10.1007/s10548-011-

0199-5

de Haan, M., Belsky, J., Reid, V., Volein, A., & Johnson, M. H. (2004). Maternal

personality and infants’ neural and visual responsivity to facial expressions of

82

emotion. Journal of child psychology and psychiatry, and allied disciplines,

45(7), 1209–1218. doi:10.1111/j.1469-7610.2004.00320.x

de Jong, P. J., & Martens, S. (2007). Detection of emotional expressions in rapidly

changing facial displays in high- and low-socially anxious women. Behaviour

Research and Therapy, 45(6), 1285–1294. doi:doi: DOI:

10.1016/j.brat.2006.10.003

De Martino, B., Kalisch, R., Rees, G., & Dolan, R. J. (2008). Enhanced Processing of

Threat Stimuli under Limited Attentional Resources. Cerebral Cortex, 19(1),

127–133. doi:10.1093/cercor/bhn062

De Martino, Benedetto, Strange, B. A., & Dolan, R. J. (2007). Noradrenergic

neuromodulation of human attention for emotional and neutral stimuli.

Psychopharmacology, 197(1), 127–136. doi:10.1007/s00213-007-1015-5

Diamond, A. (2005). Attention-deficit disorder (attention-deficit/ hyperactivity

disorder without hyperactivity): A neurobiologically and behaviorally distinct

disorder from attention-deficit/hyperactivity disorder (with hyperactivity).

Development and Psychopathology, 17(03). doi:10.1017/S0954579405050388

Diamond, A., Briand, L., Fossella, J., & Gehlbach, L. (2004). Genetic and

neurochemical modulation of prefrontal cognitive functions in children. The

American journal of psychiatry, 161(1), 125–132.

83

Dunbar, R. I. M. (2009). The social brain hypothesis and its implications for social

evolution. Annals of Human Biology, 36(5), 562–572.

doi:10.1080/03014460902960289

DuPaul, G. (1998). ADHD rating scale-IV : checklists, norms, and clinical

interpretation. New York: Guilford Press.

Ekman, P., & Friesen, W. V. (1971). Constants across cultures in the face and emotion.

Journal of personality and social psychology, 17(2), 124–129.

Ekman, P., & Oster, H. (1979). Facial Expressions of Emotion. Annual Review of

Psychology, 30(1), 527–554. doi:10.1146/annurev.ps.30.020179.002523

Field, A. P., & Lester, K. J. (2010). Is There Room for “Development” in

Developmental Models of Information Processing Biases to Threat in Children

and Adolescents? Clinical Child and Family Psychology Review, 13(4), 315–

332. doi:10.1007/s10567-010-0078-8

Field, T. M., Woodson, R., Greenberg, R., & Cohen, D. (1982). Discrimination and

imitation of facial expression by neonates. Science (New York, N.Y.),

218(4568), 179–181.

Fox, E., Russo, R., & Georgiou, G. A. (2005). Anxiety modulates the degree of

attentive resources required to process emotional faces. Cognitive, Affective, &

Behavioral Neuroscience, 5(4), 396–404. doi:10.3758/CABN.5.4.396

84

Friedman, S. R., Rapport, L. J., Lumley, M., Tzelepis, A., VanVoorhis, A., Stettner, L.,

& Kakaati, L. (2003). Aspects of social and emotional competence in adult

attention-deficit/hyperactivity disorder. Neuropsychology, 17(1), 50–58.

Frodl, T., & Skokauskas, N. (2012). Meta-analysis of structural MRI studies in

children and adults with attention deficit hyperactivity disorder indicates

treatment effects. Acta Psychiatrica Scandinavica, 125(2), 114–126.

doi:10.1111/j.1600-0447.2011.01786.x

Garrad-Cole, F., Shapiro, K. L., & Thierry, G. (2011). Developmental Aspects of

Temporal and Spatial Visual Attention: Insights from the Attentional Blink and

Visual Search Tasks. Child Neuropsychology, 17, 118–137.

doi:10.1080/09297049.2010.509716

Geurts, H. M., Luman, M., & van Meel, C. S. (2008). What’s in a game: the effect of

social motivation on interference control in boys with ADHD and autism

spectrum disorders. Journal of Child Psychology and Psychiatry, and Allied

Disciplines, 49(8), 848–857. doi:10.1111/j.1469-7610.2008.01916.x

Gomez, R. (2003). Underlying processes in the poor response inhibition of children

with Attention-Deficit/Hyperactivity Disorder. Journal of Attention Disorders,

6(3), 111–122.

Grossmann, T., & Johnson, M. H. (2007). The development of the social brain in

human infancy. European Journal of Neuroscience, 25(4), 909–919.

doi:10.1111/j.1460-9568.2007.05379.x

85

Guevremont, D., & Dumas, M. (1994). Peer relationship problems and disruptive

behavior disorders. Journal of Emotional and Behavioral Disorders, 2(3), 164–

173.

Hadwin, J. A., Donnelly, N., French, C. C., Richards, A., Watts, A., & Daley, D.

(2003). The influence of children’s self-report trait anxiety and depression on

visual search for emotional faces. Journal of Child Psychology and Psychiatry,

44(3), 432–444. doi:10.1111/1469-7610.00133

Hansen, C. H., & Hansen, R. D. (1988). Finding the Face in the Crowd: An Anger

Superiority Effect. Journal of Personality and Social Psychology, 54(6), 917–

924. doi:doi:

Hariri, A. R. (2002). Serotonin Transporter Genetic Variation and the Response of the

Human Amygdala. Science, 297(5580), 400–403. doi:10.1126/science.1071829

Hariri, A. R., Bookheimer, S. Y., & Mazziotta, J. C. (2000). Modulating emotional

responses: effects of a neocortical network on the limbic system. Neuroreport,

11(1), 43–48.

Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. (2000). The distributed human neural

system for face perception. Trends in Cognitive Sciences, 4(6), 223–233.

doi:10.1016/S1364-6613(00)01482-0

Heim-Dreger, U., Kohlmann, C.-W., Eschenbeck, H., & Burkhardt, U. (2006).

Attentional Biases for Threatening Faces in Children: Vigilant and Avoidant

Processes. Emotion, 6(2), 320–325. doi:doi: DOI: 10.1037/1528-3542.6.2.320

86

Hein, G., Alink, A., Kleinschmidt, A., & Müller, N. G. (2009). The attentional blink

modulates activity in the early visual cortex. Journal of cognitive neuroscience,

21(1), 197–206. doi:10.1162/jocn.2008.21026

Hollingsworth, D. E., McAuliffe, S. P., & Knowlton, B. J. (2001). Temporal

Allocation of Visual Attention in Adult Attention Deficit Hyperactivity

Disorder. Journal of Cognitive Neuroscience, 13(3), 298–305.

doi:10.1162/08989290151137359

Hoza, B. (2007). Peer functioning in children with ADHD. Journal of pediatric

psychology, 32(6), 655–663. doi:10.1093/jpepsy/jsm024

Hu, X.-Z., Lipsky, R. H., Zhu, G., Akhtar, L. A., Taubman, J., Greenberg, B. D., Xu,

K., et al. (2006). Serotonin transporter promoter gain-of-function genotypes are

linked to obsessive-compulsive disorder. American journal of human genetics,

78(5), 815–826. doi:10.1086/503850

Jefferies, L. N., Smilek, D., Eich, E., & Enns, J. T. (2008). Emotional Valence and

Arousal Interact in Attentional Control. Psychological Science, 19(3), 290–295.

doi:10.1111/j.1467-9280.2008.02082.x

Johansen, E. B., Aase, H., Meyer, A., & Sagvolden, T. (2002). Attention-

deficit/hyperactivity disorder (ADHD) behaviour explained by dysfunctioning

reinforcement and extinction processes. Behavioural brain research, 130(1-2),

37–45.

87

Johnson, M. H. (2005). Subcortical face processing. Nature Reviews Neuroscience,

6(10), 766–774. doi:10.1038/nrn1766

Johnston, W. (1986). Selective Attention. Annual Review of Psychology, 37(1), 43–75.

doi:10.1146/annurev.psych.37.1.43

Jolicoeur, P. (1998). Modulation of the attentional blink by on-line response selection:

evidence from speeded and unspeeded task1 decisions. Memory & Cognition,

26(5), 1014–1032.

Keil, A., Moratti, S., Sabatinelli, D., Bradley, M. M., & Lang, P. J. (2005). Additive

effects of emotional content and spatial selective attention on electrocortical

facilitation. Cerebral cortex (New York, N.Y.: 1991), 15(8), 1187–1197.

doi:10.1093/cercor/bhi001

Kohls, G., Herpertz-Dahlmann, B., & Konrad, K. (2009). Hyperresponsiveness to

social rewards in children and adolescents with attention-deficit/hyperactivity

disorder (ADHD). Behavioral and Brain Functions, 5(1), 20.

doi:10.1186/1744-9081-5-20

Kohls, G., Peltzer, J., Herpertz-Dahlmann, B., & Konrad, K. (2009). Differential

effects of social and non-social reward on response inhibition in children and

adolescents. Developmental Science, 12(4), 614–625. doi:10.1111/j.1467-

7687.2009.00816.x

Konrad, K., Gauggel, S., Manz, A., & Schöll, M. (2000). Lack of inhibition: a

motivational deficit in children with attention deficit/hyperactivity disorder and

88

children with traumatic brain injury. Child Neuropsychology: A Journal on

Normal and Abnormal Development in Childhood and Adolescence, 6(4), 286–

296.

Kranczioch, C., Debener, S., Schwarzbach, J., Goebel, R., & Engel, A. K. (2005).

Neural correlates of conscious perception in the attentional blink. NeuroImage,

24(3), 704–714. doi:10.1016/j.neuroimage.2004.09.024

Kratochvil, C. J., Faries, D., Vaughan, B., Perwien, A., Busner, J., Saylor, K., Kaplan,

S., et al. (2007). Emotional expression during attention-deficit/hyperactivity

disorders treatment: initial assessment of treatment effects. Journal of child and

adolescent psychopharmacology, 17(1), 51–62. doi:10.1089/cap.2006.0018

Kühle, H. J., Kinkelbur, J., Andes, K., Heidorn, F. M., Zeyer, S., Rautzenberg, P., &

Jansen, F. (2007). Self-regulation of visual attention and facial expression of

emotions in ADHD children. Journal of attention disorders, 10(4), 350–358.

doi:10.1177/1087054706289944

Kuyper, P. (1972). The cocktail party effect. Audiology: official organ of the

International Society of Audiology, 11(5), 277–282.

Lamb, M. E., & Sherrod, L. R. (1981). Infant social cognition : empirical and

theoretical considerations. Hillsdale, N.J.: L. Erlbaum Associates.

Lang, P. J., Bradley, M. M., Fitzsimmons, J. R., Cuthbert, B. N., Scott, J. D., Moulder,

B., & Nangia, V. (1998). Emotional arousal and activation of the visual cortex:

an fMRI analysis. Psychophysiology, 35(2), 199–210.

89

LeDoux, J. E. (2000). Emotion Circuits in the Brain. Annual Review of Neuroscience,

23(1), 155–184. doi:10.1146/annurev.neuro.23.1.155

Leppanen, J. M., & Nelson, C. A. (2009). Tuning the developing brain to social signals

of emotions. Nat Rev Neurosci, 10(1), 37–47. doi:10.1038/nrn2554

Lesch, K. P., Bengel, D., Heils, A., Sabol, S. Z., Greenberg, B. D., Petri, S., Benjamin,

J., et al. (1996). Association of anxiety-related traits with a polymorphism in

the serotonin transporter gene regulatory region. Science (New York, N.Y.),

274(5292), 1527–1531.

Li, R., C.-shan, Chen, S.H., Lin, W.hao, & Yang, Y.-Y. (2005). Attentional blink in

adolescents with varying levels of impulsivity. Journal of Psychiatric

Research, 39(2), 197-205. doi:doi: DOI: 10.1016/j.jpsychires.2004.06.003

Li, C. R., Lin, W., Chang, H., & Hung, Y. (2004). A Psychophysical Measure of

Attention Deficit in Children With Attention-Deficit/Hyperactivity Disorder.

Journal of Abnormal Psychology, 113(2), 228–236. doi:doi:

Luman, M., Oosterlaan, J., & Sergeant, J. A. (2005). The impact of reinforcement

contingencies on AD/HD: a review and theoretical appraisal. Clinical

Psychology Review, 25(2), 183–213. doi:10.1016/j.cpr.2004.11.001

Luna, B., & Sweeney, J. A. (2004). The emergence of collaborative brain function:

FMRI studies of the development of response inhibition. Annals of the New

York Academy of Sciences, 1021, 296–309. doi:10.1196/annals.1308.035

90

MacLeod, C., & Cohen, I. L. (1993). Anxiety and the interpretation of ambiguity: A

text comprehension study. Journal of Abnormal Psychology, 102(2), 238–247.

doi:10.1037/0021-843X.102.2.238

Maedgen, J. W., & Carlson, C. L. (2000). Social functioning and emotional regulation

in the attention deficit hyperactivity disorder subtypes. Journal of clinical child

psychology, 29(1), 30–42. doi:10.1207/S15374424jccp2901_4

Maratos, F., Mogg, K., & Bradley, B. (2008). Identification of angry faces in the

attentional blink. Cognition & Emotion, 22(7), 1340–1352.

doi:10.1080/02699930701774218

Marois, R., & Ivanoff, J. (2005). Capacity limits of information processing in the brain.

Trends in Cognitive Sciences, 9, 296–305. doi:10.1016/j.tics.2005.04.010

Marois, R., Yi, D.-J., & Chun, M. M. (2004). The neural fate of consciously perceived

and missed events in the attentional blink. Neuron, 41(3), 465–472.

Marsh, R., Gerber, A. J., & Peterson, B. S. (2008). Neuroimaging Studies of Normal

Brain Development and Their Relevance for Understanding Childhood

Neuropsychiatric Disorders. Journal of the American Academy of Child &

Adolescent Psychiatry, 47(11), 1233–1251.

doi:10.1097/CHI.0b013e318185e703

Martens, S., & Wyble, B. (2010). The attentional blink: Past, present, and future of a

blind spot in perceptual awareness. Neuroscience & Biobehavioral Reviews,

34(6), 947–957. doi:doi: DOI: 10.1016/j.neubiorev.2009.12.005

91

Mason, D. J., Humphreys, G. W., & Kent, L. (2005). Insights into the control of

attentional set in ADHD using the attentional blink paradigm. Journal of Child

Psychology and Psychiatry, 46(12), 1345–1353. doi:10.1111/j.1469-

7610.2005.01428.x

Mogg, K., Bradley, B. P., de Bono, J., & Painter, M. (1997). Time course of attentional

bias for threat information in non-clinical anxiety. Behaviour Research and

Therapy, 35(4), 297–303.

Montague, D. P., & Walker-Andrews, A. S. (2001). Peekaboo: a new look at infants’

perception of emotion expressions. Developmental psychology, 37(6), 826–838.

Munafò, M. R., Brown, S. M., & Hariri, A. R. (2008). Serotonin transporter (5-

HTTLPR) genotype and amygdala activation: a meta-analysis. Biological

psychiatry, 63(9), 852–857. doi:10.1016/j.biopsych.2007.08.016

Nelson, C. A. (1987). The recognition of facial expressions in the first two years of

life: mechanisms of development. Child development, 58(4), 889–909.

Nigg, J. T., & Casey, B. J. (2005). An integrative theory of attention-deficit/

hyperactivity disorder based on the cognitive and affective neurosciences.

Development and psychopathology, 17(3), 785–806.

doi:10.1017/S0954579405050376

Nixon, E. (2001). The social competence of children with attention deficit

hyperactivity disorder: a review of the literature. Child Psychology and

Psychiatry Review, 6, 172–180.

92

Oades, R. D., Slusarek, M., Velling, S., & Bondy, B. (2002). Serotonin platelet-

transporter measures in childhood attention-deficit/hyperactivity disorder

(ADHD): clinical versus experimental measures of impulsivity. The World

Journal of Biological Psychiatry: The Official Journal of the World Federation

of Societies of Biological Psychiatry, 3(2), 96–100.

Paradiso, S., Johnson, D. L., Andreasen, N. C., O’Leary, D. S., Watkins, G. L., Ponto,

L. L., & Hichwa, R. D. (1999). Cerebral blood flow changes associated with

attribution of emotional valence to pleasant, unpleasant, and neutral visual

stimuli in a PET study of normal subjects. The American journal of psychiatry,

156(10), 1618–1629.

Perez-Edgar, K., Bar-Haim, Y., McDermott, J. M., Chronis-Tuscano, A., Pine, D. S., &

Fox, N. A. (2010). Attention Biases to Threat and Behavioral Inhibition in

Early Childhood Shape Adolescent Social Withdrawal. Emotion, 10(3), 349–

357. doi:doi: DOI: 10.1037/a0018486

Pérez-Edgar, K., Bar-Haim, Y., McDermott, J. M., Gorodetsky, E., Hodgkinson, C. A.,

Goldman, D., Ernst, M., et al. (2010). Variations in the serotonin-transporter

gene are associated with attention bias patterns to positive and negative

emotion faces. Biological psychology, 83(3), 269–271.

doi:10.1016/j.biopsycho.2009.08.009

93

Pessoa, L. (2002). Attentional control of the processing of neutral and emotional

stimuli. Cognitive Brain Research, 15(1), 31–45. doi:10.1016/S0926-

6410(02)00214-8

Phelps, E. A. (2006). Emotion and cognition: insights from studies of the human

amygdala. Annual review of psychology, 57, 27–53.

doi:10.1146/annurev.psych.56.091103.070234

Posner, M. I. (1993). Foundations of cognitive science. Cambridge, Mass.: MIT Press.

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. doi:10.1016/j.biopsycho.2012.02.007

Pratto, F., & John, O. P. (1991). Automatic vigilance: the attention-grabbing power of

negative social information. Journal of personality and social psychology,

61(3), 380–391.

Rapport, L. J., Friedman, S. R., Tzelepis, A., Van Voorhis, A., & Friedman, S. L.

(2002). Experienced emotion and affect recognition in adult attention-deficit

hyperactivity disorder. Neuropsychology, 16(1), 102–110.

Ray Li, C., Chen, S.-H., Lin, W., & Yang, Y.-Y. (2005). Attentional blink in

adolescents with varying levels of impulsivity. Journal of Psychiatric

Research, 39(2), 197–205. doi:doi: DOI: 10.1016/j.jpsychires.2004.06.003

Raymond, J. E., Shapiro, K. L., & Arnell, K. M. (1992). Temporary suppression of

visual processing in an RSVP task: an attentional blink? Journal of

94

Experimental Psychology. Human Perception and Performance, 18(3), 849–

860.

Reimherr, F. W., Marchant, B. K., Strong, R. E., Hedges, D. W., Adler, L., Spencer, T.

J., West, S. A., et al. (2005). Emotional dysregulation in adult ADHD and

response to atomoxetine. Biological psychiatry, 58(2), 125–131.

doi:10.1016/j.biopsych.2005.04.040

Reynolds, C. R., & Kamphaus, R. W. (1992). Behavior Asseement System for

Children, BASC. San Antonio, TX: Pearson Education, Inc.

Romanski, L. M., & LeDoux, J. E. (1992). Equipotentiality of thalamo-amygdala and

thalamo-cortico-amygdala circuits in auditory fear conditioning. The Journal of

neuroscience: the official journal of the Society for Neuroscience, 12(11),

4501–4509.

Rossi, V., & Pourtois, G. (2012). State-dependent attention modulation of human

primary visual cortex: A high density ERP study. NeuroImage, 60(4), 2365–

2378. doi:10.1016/j.neuroimage.2012.02.007

Rubia, K., Overmeyer, S., Taylor, E., Brammer, M., Williams, S. C. R., Simmons, A.,

Andrew, C., et al. (2000). Functional frontalisation with age: mapping

neurodevelopmental trajectories with fMRI. Neuroscience & Biobehavioral

Reviews, 24(1), 13–19. doi:doi: DOI: 10.1016/S0149-7634(99)00055-X

Sabatinelli, D., Lang, P. J., Bradley, M. M., Costa, V. D., & Keil, A. (2009). The

timing of emotional discrimination in human amygdala and ventral visual

95

cortex. The Journal of neuroscience: the official journal of the Society for

Neuroscience, 29(47), 14864–14868. doi:10.1523/JNEUROSCI.3278-09.2009

Schachar, R., Tannock, R., Marriott, M., & Logan, G. (1995). Deficient inhibitory

control in attention deficit hyperactivity disorder. Journal of abnormal child

psychology, 23(4), 411–437.

Schub, A., Meinecke, C., Abele, A. E., & Gendolla, G. H. E. (2006). Detecting

Emotional Faces and Features in a Visual Search Paradigm: Are Faces Special?

Emotion, 6(2), 246-256. doi:doi: DOI: 10.1037/1528-3542.6.2.246

Schutter, D. J., Putman, P., Hermans, E., & van Honk, J. (2001). Parietal

electroencephalogram beta asymmetry and selective attention to angry facial

expressions in healthy human subjects. Neuroscience Letters, 314(1-2), 13-16.

Shapiro, K L, Caldwell, J., & Sorensen, R. E. (1997). Personal names and the

attentional blink: a visual “cocktail party” effect. Journal of Experimental

Psychology. Human Perception and Performance, 23(2), 504-514.

Soliva, J. C., Carmona, S., Fauquet, J., Hoekzema, E., Bulbena, A., Hilferty, J., &

Vilarroya, O. (2009). Neurobiological Substrates of Social Cognition

Impairment in Attention-Deficit Hyperactivity Disorder. Annals of the New

York Academy of Sciences, 1167(1), 212–220. doi:10.1111/j.1749-

6632.2009.04604.x

Somerville, L. H., Fani, N., & McClure-Tone, E. B. (2011). Behavioral and Neural

Representation of Emotional Facial Expressions Across the Lifespan.

96

Developmental Neuropsychology, 36(4), 408–428.

doi:10.1080/87565641.2010.549865

Sonuga-Barke, E. J. S. (2005). Causal models of attention-deficit/hyperactivity

disorder: from common simple deficits to multiple developmental pathways.

Biological psychiatry, 57(11), 1231–1238. doi:10.1016/j.biopsych.2004.09.008

Sonuga-Barke, E. J. S. (2003). The dual pathway model of AD/HD: an elaboration of

neuro-developmental characteristics. Neuroscience and biobehavioral reviews,

27(7), 593–604.

Sonuga-Barke, E. J. S. (2002). Psychological heterogeneity in AD/HD--a dual pathway

model of behaviour and cognition. Behavioural brain research, 130(1-2), 29–

36.

Sonuga-Barke, E., Bitsakou, P., & Thompson, M. (2010). Beyond the dual pathway

model: evidence for the dissociation of timing, inhibitory, and delay-related

impairments in attention-deficit/hyperactivity disorder. Journal of the American

Academy of Child and Adolescent Psychiatry, 49(4), 345–355.

Spielberger, C. D., Edwards, C. D., Montuori, J., & Lushene, R. (1983). State-Trait

Anxiety Inventory for Children. Menlo Park, CA: Mind Garden, Inc.

Srivastava, P., & Srinivasan, N. (2010). Time course of visual attention with emotional

faces. Attention, Perception & Psychophysics, 72(2), 369-377.

doi:10.3758/APP.72.2.369

97

Stein, T., Peelen, M. V., Funk, J., & Seidl, K. N. (2010). The Fearful-Face Advantage

Is Modulated by Task Demands: Evidence From the Attentional Blink.

Emotion, 10(1), 136-140. doi:doi: DOI: 10.1037/a0017814

Stopa, L. (2000). Social phobia and interpretation of social events. Behaviour Research

and Therapy, 38(3), 273-283. doi:10.1016/S0005-7967(99)00043-1

Taylor, J. G., & Fragopanagos, N. F. (2005). The interaction of attention and emotion.

Neural Networks, 18(4), 353–369. doi:doi: 10.1016/j.neunet.2005.03.005

Taylor, M. J., Batty, M., & Itier, R. J. (2004). The faces of development: a review of

early face processing over childhood. Journal of Cognitive Neuroscience,

16(8), 1426–1442. doi:10.1162/0898929042304732

Tibboel, H., Van Bockstaele, B., & De Houwer, J. (2011). Is the emotional modulation

of the attentional blink driven by response bias? Cognition & Emotion, 25(7),

1176–1183. doi:10.1080/02699931.2010.524192

Toga, A. W., Thompson, P. M., & Sowell, E. R. (2006). Mapping brain maturation.

Trends in Neurosciences, 29(3), 148–159. doi:doi: DOI:

10.1016/j.tins.2006.01.007

Visser, T. A. W., Boden, C., & Giaschi, D. E. (2004). Children with dyslexia: evidence

for visual attention deficits in perception of rapid sequences of objects. Vision

Research, 44(21), 2521-2535. doi:doi: 10.1016/j.visres.2004.05.010

Vuilleumier, P. (2005). How brains beware: neural mechanisms of emotional attention.

Trends in Cognitive Sciences, 9(12), 585–594. doi:10.1016/j.tics.2005.10.011

98

Vuilleumier, P., & Pourtois, G. (2007). Distributed and interactive brain mechanisms

during emotion face perception: Evidence from functional neuroimaging.

Neuropsychologia, 45(1), 174–194. doi:doi: DOI:

10.1016/j.neuropsychologia.2006.06.003

Vuilleumier, P, & Schwartz, S. (2001). Beware and be aware: capture of spatial

attention by fear-related stimuli in neglect. Neuroreport, 12(6), 1119–1122.

Weissman, D. H., & Prado, J. (2012). Heightened activity in a key region of the ventral

attention network is linked to reduced activity in a key region of the dorsal

attention network during unexpected shifts of covert visual spatial attention.

NeuroImage, 61(4), 798–804. doi:10.1016/j.neuroimage.2012.03.032

Wender, P. H., Wolf, L. E., & Wasserstein, J. (2001). Adults with ADHD. An

overview. Annals of the New York Academy of Sciences, 931, 1–16.

Weschler, D. (n.d.). Wechsler Intelligence Scale for Children® — Fourth Edition

(WISC®-IV). Retrieved July 6, 2010, from

http://psychcorp.pearsonassessments.com/HAIWEB/Cultures/en-

us/Productdetail.htm?Pid=015-8979-044

Whalen, P. J. (1998). Fear, Vigilance, and Ambiguity: Initial Neuroimaging Studies of

the Human Amygdala. Current Directions in Psychological Science, 7(6), 177–

188. doi:10.1111/1467-8721.ep10836912

Wheeler, J., & Carlson, C. (1994). The social functioning of children with ADD with

hyperactivity and ADD without hyperactivity: A comparison of their peer

99

relations and social deficits. Journal of Emotional and Behavioral Disorders,

2(1), 2–13.

Whiten, A. (1997). Machiavellian intelligence. Cambridge ;;New York: Cambridge

University press.

Williams, L. M., Hermens, D. F., Palmer, D., Kohn, M., Clarke, S., Keage, H., Clark,

C. R., et al. (2008). Misinterpreting emotional expressions in attention-

deficit/hyperactivity disorder: evidence for a neural marker and stimulant

effects. Biological psychiatry, 63(10), 917–926.

doi:10.1016/j.biopsych.2007.11.022

Woodcock, R.W., McGrew, K.S., & Mather, N. (2000). Woodcock-Johnson III.

Rolling Meadows, IL: Riverside Publishing.

Yiend, J. (2010). The effects of emotion on attention: A review of attentional

processing of emotional information. Cognition & Emotion, 24(1), 3–47.

doi:10.1080/02699930903205698

Yuill, N., & Lyon, J. (2007). Selective difficulty in recognising facial expressions of

emotion in boys with ADHD. General performance impairments or specific

problems in social cognition? European child & adolescent psychiatry, 16(6),

398–404. doi:10.1007/s00787-007-0612-5