Mixed Feelings: Emotional Empathy and Social Motivation in Autism Spectrum Disorder
Emily Trimmer
Bachelor of Psychology (Hons) ANU, Australia Master of Psychology (Clinical) Western Sydney University, Australia
A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy
School of Psychology, Faculty of Science
September 2017
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ACKNOWLEDGEMENTS
First, I would like to express my special appreciation and thanks to my supervisor, Professor
Skye McDonald, for her continuing encouragement, advice and support over the past 6 years.
You have been a tremendous mentor. I would also like to thank my co-supervisor, Dr Jacqui
Rushby, for all her help and expertise on the complicated and technical issues involved in my
studies. I have learnt so much from you throughout my candidature. Second, I would like to
thank all the people involved with Skye’s lab over the past 6 years. It has been a pleasure to
work with you all and you have made my time more enjoyable and less stressful!
To my family: Mum, Jeff, Dad, Kate, Fi, Alex, Iro & Josh - thank you so much for all your
supportive encouragement, champagne and bloody Mary's. To my parents for financial
assistance to attend and present at international conferences, for emotional support and to
making me stop complaining and getting things done.
To my closest friends: Alana, Francesca, Gems, Laurenn, Sophie and Sally. For putting up
with my mood swings and making me laugh and enjoy life despite the huge mountain in front
of me.
To the Stanburys: my British surrogate family who have encouraged, supported and believed
in me to achieve everything I have.
And a special thank you to my mum: my inspiration, my life mentor. The person who has
both supported me and made me believe that I could achieve anything if I tried hard enough.
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Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterised by social communication impairment and restricted, repetitive interests and behaviours. Whilst many theories have been proposed as to the nature of these social difficulties, from impaired theory of mind to the extreme male brain theory, the concept of empathy remains a widely debated construct in the Autism literature. Empathy includes both a cognitive and an affective component, which have been shown to activate separate but interrelated brain regions.
Whilst cognitive empathy encompasses processes such as theory of mind, mentalising and self-other awareness, emotional or affective empathy involves processes such as emotional mimicry, emotion contagion, emotion perception and emotion regulation. Social motivation is also an influencing factor affecting one’s empathic experience. A comprehensive model encompassing these constructs and their neurobiological underpinning is proposed. Each of these constructs can be measured using a range of measure from recordings of physiological responses to behavioural measures to subjective self-report trait-based questionnaires. By using a comprehensive array of measures, the relationships between objective responses and subjective experiences can be explored.
Whilst there is a general consensus in the literature that individuals with ASD have difficulty with cognitive empathy, much less is known about emotional empathy processing in these individuals. Most research has employed subjective self-report measures, which can often be misinterpreted or under/over-reported. More objective measures, such as psychophysiological recordings of arousal offer a more objective response. Furthermore, combining physiological responses with self-report ratings allows us to explore the relationship between these two types of responses to emotionally charged stimuli. vii
Study 1 examined both the autonomic emotional responses as well as self-report
responses to emotional stimuli in individuals with ASD compared with controls. Twenty-five individuals with ASD were compared with twenty-five matched controls on their physiological (arousal and facial expression) and psychological (self-report) responses to emotionally distressing video scenes. These responses were also then compared with self- report cognitive and emotional trait empathy. Results indicate that whilst individuals with
ASD appear to respond similarly to controls physiologically, their interpretation of this response is dampened emotionally. Furthermore, this dampening of self-report emotional response is associated with a general reduction in trait empathy.
Emotional empathy involves both bottom-up automatic sensory and emotional responses, such as sensorimotor and emotional contagion as well as top-down subjective appraisal of emotion in both the self and others. It is unclear as to which components of emotional empathy are impaired in individuals with ASD. Therefore, Study 2 explored these multilevel factors of emotional empathy using both physiological and self-reported responses using a previously established empathy for pain paradigm. Results indicated that individuals with ASD demonstrated typical sensorimotor response to a painful stimulus being applied to another person, however, emotion contagion (as measured by arousal levels) was dampened.
Furthermore, subjective ratings of intensity and unpleasantness of the pain was also dampened in the ASD group. These findings suggest that both bottom-up emotion contagion and top-down subjective interpretation of others’ pain may be impaired in individuals with
ASD.
Social motivation has a significant impact on one’s ability to understand and respond empathically. The need to affiliate and interact socially is a normal human process, however, it has been shown to be reduced in children with ASD. Many people with ASD desire social interaction and relationships, but have difficulty interacting, which can lead to social viii
exclusion and ostracism. Whilst most people at some point in their lives experience some
form of ostracism, it is experienced far more often in individuals with ASD. Little is known
about how this social exclusion is interpreted, experienced or managed. Study 3 aimed to
explore the psychological (mood and social needs) as well as the physiological (arousal)
effects of ostracism using a well-established paradigm, Cyberball. Results demonstrated no differences between groups on social needs, however, mood was rated as more negatively by the ASD group overall. Arousal was increased in when excluded compared with when excluded for the ASD group, but not for controls. Overall, individuals with ASD experienced heightened physiological arousal but this response was not expressed as emotionally significant to these individuals, highlighting the potential role of alexithymia traits.
Another potential factor affecting social motivation and one’s ability to empathise is anxiety. Anxiety disorders are the most common comorbid diagnoses in individuals with
ASD. Indeed there is significant overlap between many of the characteristics of social anxiety and ASD, including social isolation and avoidance and heightened arousal. It is therefore plausible that anxiety is a contributing factor to the ability to empathize with others, a construct which has been found to be impaired in those with ASD. In Study 4, twenty-five adults with ASD and twenty-five matched controls were compared on theory of mind performance and completed questionnaires measuring trait empathy and trait and social anxiety. The relationship between these measures was examined. Individuals with ASD performed worse than controls on the Faux Pas reasoning item of the task only. Further, those with ASD scored significantly higher on both trait and social anxiety, and lower on all measures of trait empathy. Trait anxiety was significantly related to both perspective taking and general trait empathy, and accounted for variance above and beyond autistic traits in both relationships. Trait anxiety is therefore a vital concept to consider and manage when assessing and treating empathy impairments in individuals with ASD. ix
Together the four studies reported in this thesis indicate that adults with ASD demonstrate both typical and blunted bottom-up automatic emotional empathy responses
(emotion contagion) to interactive real world video stimuli, suggesting heterogeneity within this population. However, these individuals demonstrate significant impairment on top-down subjective appraisal of emotional responses in both the self and other. Whilst social motivation is typical in this group, factors such as anxiety and impaired emotion regulation are associated with these empathic difficulties. Implications for clinical intervention and research are discussed.
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Table of Contents
Acknowledgments ...... v
Abstract ...... vi
Publications Relating to this Thesis ...... xv
List of Tables ...... xvi
List of Figures ...... xviii
List of Abbreviations ...... xx
CHAPTER 1 ...... 1
General Introduction ...... 1
Autism Spectrum Disorder ...... 1
Extreme Male Brain Theory ...... 2
Theory of Mind Impairment (Mind Blindness) ...... 2
Social Motivation Theory ...... 3
Empathy ...... 4
Cognitive Empathy ...... 5
Affective/Emotional Empathy ...... 6
Neurobiological Underpinnings of Empathy ...... 8
Cognitive Empathy ...... 8
Affective Empathy ...... 9
Anatomical distinction between Cognitive and Affective Empathy ...... 10
Measures of Empathy ...... 10
Subjective Measures ...... 11
Objective Measures ...... 12 xi
Models of Empathy ...... 14
Empathy in ASD ...... 18
Cognitive Empathy ...... 18
Affective Empathy ...... 19
Metacognition/Self-Awareness in Empathy ...... 22
Social Motivation in ASD ...... 26
Ostracism ...... 27
Anxiety ...... 28
General Aims of Thesis ...... 29
CHAPTER 2 ...... 32
General Methods ...... 32
Participants ...... 32
Materials and Measures ...... 33
CHAPTER 3 ...... 37
Study 1. Emotional Empathy in Adults with ASD ...... 37
Method ...... 41
Participants ...... 41
Materials and Measures ...... 41
Procedure ...... 42
Data Reduction and Analyses ...... 43
Results ...... 44
Demographics ...... 44
Self-reported Trait Empathy ...... 45
Subjective Ratings of Mood and Arousal ...... 45
Skin Conductance ...... 47 xii
Corrugator EMG ...... 49
Correlations ...... 50
Discussion ...... 53
CHAPTER 4 ...... 58
Study 2. Empathy for Pain in ASD ...... 58
Method ...... 61
Participants ...... 61
Procedure ...... 62
Materials and Measures ...... 62
Data Reduction and Analyses ...... 63
Results ...... 65
Physiological Measures ...... 66
Self-Reported Pain Ratings ...... 69
Relationships between Arousal Levels, Trait Empathy and self-reported pain
ratings ...... 71
Discussion ...... 72
CHAPTER 5 ...... 79
Study 3. The effects of Ostracism on Adults with ASD ...... 79
Method ...... 83
Participants ...... 83
Procedure ...... 83
Manipulation of Ostracism – Cyberball ...... 84
Materials and Measures ...... 84
Data Reduction and Analyses ...... 85
Results ...... 86 xiii
Manipulation of Ostracism ...... 86
Self-reported Needs and Mood ...... 87
Skin Conductance Level ...... 90
Relationship between self-reported mood, arousal to exclusion and trait
empathy ...... 93
Discussion ...... 93
CHAPTER 6 ...... 100
Study 4. Anxiety and Empathy in ASD ...... 100
Method ...... 103
Participants ...... 103
Materials and Measures ...... 103
Data Analyses ...... 106
Results ...... 107
Demographics ...... 107
Faux Pas ...... 108
Anxiety ...... 108
Empathy ...... 109
Correlations ...... 109
Effect of Anxiety on Empathy ...... 111
Discussion ...... 113
CHAPTER 7 ...... 120
General Discussion ...... 120
What does this mean for Emotional Empathy in ASD? ...... 123
What does this mean for Social Motivation in ASD? ...... 130
Clinical Implications for Understanding and Treating ASD ...... 135 xiv
Limitations and Future Directions ...... 140
REFERENCES ...... 142
xv
Publications Relating to this Thesis
CHAPTER 3
Trimmer, E. M., McDonald, S. & Rushby, J. A. (2016). Not knowing what I feel: Emotional
Empathy in Autism Spectrum Disorder. Autism, 1-8; DOI: 10.1177/1362361316648520
CHAPTER 4
Trimmer, E. M., McDonald, S., De Blasio, F. M. & Rushby, J. A. (in review). The multifaceted experience of empathy for pain in Autism Spectrum Disorder. International
Journal of Psychophysiology.
CHAPTER 5
Trimmer, E.M., McDonald, S., Kelly, M. & Rushby, J. A. (2017). The psychological and physiological effects of ostracism in Autism Spectrum Disorders. Journal of Autism and
Developmental Disorders, 2326-2335.
CHAPTER 6
Trimmer, E. M., McDonald, S., Mathersul, D. & Rushby, J. A. (in review). Does anxiety play a role in empathy deficits in adults with ASD? Autism Research.
xvi
List of Tables
Table 3.1 Participant demographic details and performance on self-report
questionnaires ...... 44
Table 3.2 Relationship between self-report and physiological response and
trait empathy across the combined group (ASD and Control) ...... 50
Table 3.3 Relationship between self-report and physiological response and
trait empathy – ASD Group ...... 51
Table 3.4 Relationship between self-report and physiological response and
trait empathy – Control Group ...... 52
Table 4.1 Demographic and Questionnaire Data ...... 65
Table 4.2 Correlations between skin conductance, self-report responses and
IRI subscales ...... 72
Table 5.1 Participant demographic details and performance on self-report
questionnaires ...... 86
Table 5.2 Relationship between self-report mood, arousal and trait empathy 92
Table 6.1 Participant Demographics for the group with ASD and the
control group as well as Empathy and Anxiety scores ...... 107
Table 6.2 Correlations between measures of theory of mind (Faux Pas),
empathy (cognitive and emotional) and autistic traits in the
combined group ...... 110
Table 6.3 Unstandardised and standardised regression coefficients for the
variables entered into the model (Cognitive Empathy) ...... 112
Table 6.4 Unstandardised and standardised regression coefficients for the xvii
variables entered into the model (Emotional Empathy) ...... 112
Table 6.5 Unstandardised and standardised regression coefficients for the
variables entered into the model (Explanation of Faux Pas) ...... 113
xviii
List of Figures
Figure 1.1 Diagram outlining overlap between Theory of Mind and
Empathy ...... 6
Figure 1.2 Proposed Model of Empathy ...... 17
Figure 3.1 Mean self-reported mood rating to neutral and emotional clips ... 45
Figure 3.2 Mean self-reported arousal ratings to neutral and emotional 46
clips ......
Figure 3.3 Mean skin conductance level to neutral and emotional video
clips ...... 47
Figure 3.4 Comparison of ASD and control SCL for each emotional clip .... 48
Figure 3.5 Comparison of ASD and control SCL for each neutral clip ...... 48
Figure 3.6 Mean corrugator response to neutral and emotional video clips .. 49
Figure 4.1 Difference in EMG hand response for pain relative to Control
(Touch, Apple, Static) stimuli for Control and ASD Participant
Groups ...... 66
Figure 4.2 Difference in skin conductance level to pain stimuli relative to
Control (Touch, Apple, Static) stimuli ...... 67
Figure 4.3 Difference in heart rate to pain stimuli relative to Control
(Touch, Apple, Static) stimuli 68
......
Figure 4.4 Difference in Corrugator EMG response to pain stimuli relative
to Control (Touch, Apple, Static) stimuli ...... 69
xix
Figure 4.5 Difference in pain intensity ratings to pain stimuli relative to
Control (Touch, Apple, Static) stimuli ...... 70
Figure 4.6 Difference in pain unpleasantness ratings to pain stimuli relative
to Control (Touch, Apple, Static) stimuli ...... 71
Figure 5.1 Needs Ratings after Inclusion ...... 87
Figure 5.2 Needs Ratings after Exclusion ...... 88
Figure 5.3 Mood Ratings after Inclusion 89
......
Figure 5.4 Mood Ratings after Exclusion ...... 89
Figure 5.5 Skin Conductance Level Totals during game ...... 91
Figure 5.6 Skin Conductance Levels Over Time throughout the Inclusion
Condition 91
......
Figure 5.7 Skin Conductance Levels Over Time throughout the Exclusion
Condition ...... 92
xx
List of Abbreviations
ACC Anterior Cingulate Cortex
AE Affective Empathy
ANOVA Analysis of Variance
AQ Autism Quotient
ASD Autism Spectrum Disorder
ADHD Attention Deficit Hyperactivity Disorder
ADI-R Autism Diagnostic Interview – Revised
BVAQ Bermond-Vorst Alexithymia Questionnaire
CE Cognitive Empathy
CR Corrugator supercilii (muscle) dACC Dorsal Anterior Cingulate Cortex
DLPFC Dorsolateral Prefrontal Cortex
EC Empathic Concern (subscale of the IRI)
EEG Electroencephalography
EMG Electromyography
EQ Empathy Quotient
ERP Event-Related Potential
FDI First Dorsal Interosseous (muscle) xxi
fMRI Functional Magnetic Resonance Imaging
FSIQ Full Scale IQ
IAPS International Affective Picture System
IRI Interpersonal Reactivity Index
MANOVA Multivariate Analysis of Variance
MEP Motor-Evoked Potential
MET Multifaceted Empathy Test
MPFC Medial Prefrontal Cortex
MRI Magnetic Resonance Imaging
PT Perspective Taking (subscale of the IRI)
SI Primary Sensory Cortex
SII Secondary Sensory Cortex
SAD Social Anxiety Disorder
SAM Self-Assessment Manikin
SCL Skin Conductance Level
SD Standard Deviation
SPAI Social Phobia and Anxiety Inventory
STAI State-Trait Anxiety Index
STS Superior Temporal Sulcus
TAS-20 Toronto Alexithymia Scale – 20 items xxii
TMS Transcranial Magnetic Stimulation
ToM Theory of Mind
UNSW HREC University of NSW Human Research Ethics Committee
VMPFC Ventromedial Prefrontal Cortex
WASI Wechsler Abbreviated Intelligence Index
1
CHAPTER 1
GENERAL INTRODUCTION
Autism Spectrum Disorder
Autism Spectrum Disorders (ASDs) are pervasive developmental disorders characterized by impairments in social communication, social interaction and restricted or repetitive interests or behaviours (American Psychiatric Association, 2013). Autism is a complex and highly variable condition that has a wide range of symptoms and presentations from those at the ‘high-functioning’ end of the scale to those that require lifelong care. The term Autism Spectrum Disorder therefore implies that there is significant heterogeneity within this population. Autism was first identified and described by Leo Kanner in 1943, who described in detail the social and emotional impairments in 11 children with autism. Since then, research exploring clinical presentations, neurobiological underpinnings, genetic markers and both diagnostic and treatment measures has exploded. Epidemiological literature reports rates of approximately 62 in 10,000 globally (Elsabbagh et al., 2012). Whilst the cause of ASD is unknown, a number of theories have been put forward, some without scientific foundation. Most scientific researchers today agree that the disorder is caused by a complex interaction between genetic, biological and environmental factors, which is different for each individual (Hollander, Kolevzon, & Coyle, 2011). Autism is also rarely diagnosed alone and is, more often than not, diagnosed along with comorbid ADHD, anxiety, oppositional defiant disorder, depression, language impairment and/or intellectual disability in children. Together, these difficulties cause significant impairment in social communication and interaction and can be profoundly stigmatizing leading to social isolation, low mood and 2
anxiety (White, Ollendick, & Bray, 2011b). A number of interrelated theories have been posited to explain these social communication difficulties seen in ASD.
Extreme Male Brain Theory
The extreme male brain theory of autism was first proposed by Hans Asperger who
wrote “the autistic personality is an extreme variant of male intelligence” (Asperger, 1944,
translated by Uta Frith). Baron-Cohen (2004) has since refined the definition by suggesting
the female brain as being characterized by the individual’s greater ability to empathise than
systemize, whereas the male brain is defined as the opposite, that is, greater ability to
systemize than empathise. Thus, the autistic brain is one of very low empathizing ability and
very high systemizing ability. Evidence of this theory comes from gender differences on
standardized measures of empathizing and systemizing as well as correlations with measures
of autistic traits (Baron-Cohen, 2011). In the general population, females tend to score higher
on measures of empathizing and males tend to score higher on measures of systemizing as
well as higher on measures of autistic traits (Goldenfeld, Baron-Cohen, & Wheelwright,
2005).
Theory of Mind Impairment (Mind Blindness)
Theory of mind (ToM) refers to the ability to evaluate the behaviour of other people
on the basis of their goals, emotions, intentions and beliefs (Baron-Cohen, Leslie, & Frith,
1985). The ToM theory of autism proposes that this cognitive mechanism is absent or impaired in individuals with ASD so that they are unable to process mental-state information
(Tager-Flusberg, 2007). ToM research has differentiated a range of tasks with varying levels of mentalising demands. Mentalising refers to making inferences about other people’s mental 3
states. At a basic level, ToM tasks focus on making an inference about another person’s
belief, such as in the original Sally-Ann false-belief task developed by Baron-Cohen et al.
(Baron-Cohen et al., 1985) or about one person’s belief about another person’s belief (second
order ToM). However, as most adolescents and adults with ASD are able to pass both first
and second-order false belief tasks, more difficult and challenging tasks have been developed
tapping in to the ability to make inferences about others’ intentions and motivations. Tasks
such as the Strange Stories Test, developed by Happé (Happe, 1994) and the faux-pas test, developed by Baron-Cohen et al. (Baron-Cohen, O'Riordan, Stone, Jones, & Plaisted, 1999) require an individual to make judgments about another character’s emotions and intentions. A large volume of research has demonstrated that adults with ASD find this task much more difficult than adults without ASD and many are unable to provide accurate judgments.
Social Motivation Theory
Social affiliation, or lack thereof, has been one of the principal traits or personality
characteristics associated with ASD (Stavropoulos & Carver, 2013). The desire or need to
socialize with others is fundamental to the human species as our survival has been dependent
upon group affiliation and cooperation (Carpenter, 2011). Need for affiliation has core
underpinnings in neural reward and reinforcement circuits (Carpenter, 2011). Three core
processes have been suggested to support and reinforce the social affiliation process
(Neuhaus, Beauchaine, & Bernier, 2010): first, an appetitive phase in which biologically-
important social behaviours, including facial expressions, vocalisations and gestures, are
sought after and pursued; second, a consummatory phase in which reward processes
positively reinforce social behaviour; third, a phase in which affiliative memories facilitate
the establishment of long-lasting social bonds (Neuhaus et al., 2010). Dawson and Bernier 4
(2007) propose that ASD is characterized by reduced social motivation early in development, which is reflected in reduced social orientation, less time spent looking at or interacting with others, and less attention to social stimuli. Due to this lack of social motivation, reward responses do not occur and associated brain regions do not function appropriately to social stimuli.
Overall, the poor social cognition exhibited by people with ASD appears to be the most salient feature of the disorder and one that will be the subject of this thesis. In particular, one area of impairment that pulls these theories together is empathy. By exploring empathy in detail, more light can be shed on some of the mechanisms causing difficulties in ASD.
Empathy
"Empathy depends not only on one's ability to identify someone else's emotions but also on one's capacity to put oneself in the other person's place and to experience an appropriate emotional response" (Morris, 1996, p. 442). It requires a significant degree of social and emotional understanding, which is lacking in ASD. It is important to note, however, that empathy deficits are not unique to ASD. A number of psychological, neurodevelopmental and neurological disorders have been shown to be associated with impaired empathy such as schizophrenia (Bora, Yucel, & Pantelis, 2009; Shamay-Tsoory,
Shur, Harari, & Levkovitz, 2007), bipolar disorder (Derntl, Seidel, Schneider, & Habel,
2012), anorexia nervosa (Beadle, Paradiso, Salerno, & McCormick, 2013; Calderoni et al.,
2013), psychopathy (J. Blair, Sellars, Strickland, Clark, & et al., 1996; Marshall, Hudson,
Jones, & Fernandez, 1995) and traumatic brain injury (Bibby & McDonald, 2005; de Sousa,
McDonald, & Rushby, 2012). 5
The term empathy has been used diversely throughout the scientific literature, and has
a number of definitions depending on the focus of the research. Broadly speaking, empathy
refers to the reactions of one individual to the observed experiences of another. However, this
limited definition is not particularly useful when examining the concept of empathy in
psychological research. Other definitions include “the ability to see the world, including
one’s own behavior, from another person’s point of view” (Hollin, 1994, p. 1240), “the result
of psychological inferences about other persons’ mental and emotional states allowing for
socially appropriate emotional responses” (Schulte-Rüther et al., 2011, p. 2), and “natural tendency to share and understand the emotions and feelings of others in relation to oneself”
(Decety & Meyer, 2008, p. 1053). It is clear from these definitions that empathy involves both a cognitive and an emotional component. Indeed, Walter (2012) describes three levels of empathy: mentalization (i.e. cognitive processing) emotional contagion and emotional empathy. Emotional contagion is often considered as an aspect of emotional empathy. Recent research has focused on the distinction between cognitive and affective empathy and the implications this has for distinguishing between various psychopathologies such as autism, conduct disorder and psychopathy (Jones, Happé, Gilbert, Burnett, & Viding, 2010).
Cognitive empathy can be defined as “the process of understanding another person’s
perspective” (Rogers, Dziobek, Hassenstab, Wolf, & Convit, 2007, p. 709). Theory of mind
is often used interchangeably with empathy but not all ToM equates to empathy. Cognitive
ToM refers to making inferences about another’s beliefs, and does not involve any empathic
understanding. In contrast, affective ToM refers to making inferences about another’s
intentions or feelings, and can be viewed as being synonymous with cognitive empathy
(Shamay-Tsoory, Aharon-Peretz, & Perry, 2009). 6
Affective or emotional empathy refers to the ability to emotionally resonate with others’ feelings while understanding that they are distinct from one’s own (Baron-Cohen &
Wheelwright, 2004). These interrelated concepts are presented visually in Figure 1.1.
Theory of Mind Empathy
Cognitive Affective Cognitive Affective
Inferences about Inferences about intention/emotion Resonance with feelings beliefs (mentalising)
Figure 1.1. Representation of overlap between Theory of Mind and Empathy
Emotional empathy has often been used synonymously with the term emotion contagion, i.e. “the tendency to mimic the verbal, physiological and/or behavioural aspects of another person’s emotional experience/expression, and thus to experience/express the same emotions oneself” (Hsee, Hatfield, Carlson, & Chemtob, 1990, p. 328). Emotional contagion may be thought of as empathy at its most automatic and rudimentary level. Facial mimicry has been shown to be related to emotional contagion and emotional understanding
(Niedenthal, 2007). For example, using facial electromyography (fEMG), Lundqvist and 7
Dimberg (1995) demonstrated that when participants observed happy facial expressions, they showed increased muscular activity in the zygomaticus major (cheek muscle) and when they observed angry facial expressions, they displayed greater activation in the corrugator supercilii (brow muscle). This facial mimicry does not appear to be simply a motor mimicry response. Moody et al. (2007) demonstrated the influence of the experience of an emotion
(fear) on the facial mimicry response, so that when fear was induced in participants, they responded with a fearful rather than angry expression when viewing angry faces.
Furthermore, mimicry of facial expression has been shown to be strongly related to subjective
self-reported responses to emotional context as well as more general measures of trait
empathy (Balconi, Bortolotti, & Crivelli, 2013).
Emotional empathy also involves conscious emotion perception and recognition.
Whether conscious emotion recognition is achieved through feedback from facial mimicry is
unclear, with evidence both for (Ekman, Levenson, & Friesen, 1990) and against (Hess &
Blairy, 2001) this proposition. Ekman et al. (1990) demonstrated that voluntary facial
activity produced significant levels of subjective experience of the associated emotion.
However, despite demonstrating mimicry of basic emotion expressions (happiness, sadness,
disgust, anger), Hess and Blairy (2001) could not confirm any relation between mimicry and
emotional contagion or between mimicry and emotion recognition, using meditational
analyses.
Emotional empathy also requires a high level of appraisal. This higher level of
emotional empathy is thought to include shared neural representations, self-awareness and
emotion regulation (Balconi et al., 2013). When viewing a social situation, observers must be
able to separate feelings belonging to themselves from feelings belonging to others and be
able to regulate their emotional response. Self-other awareness relies on both cognitive and affective processing and may also be understood as metacognition. The importance of being 8 able to separate one’s own feelings from the feelings of another, without confusion, is important for empathic responding. For example, when one is able to separate oneself from feelings of distress or anxiety in the observed person, and regulate one’s own emotions appropriately, one is more likely to be able to respond with sympathy and helping behaviour.
Conversely, when the observed person’s feelings of distress are confused with one’s own self-focused emotional reaction, empathic responding is less likely (Decety & Meyer, 2008).
These different components of emotional empathy relate to increasing levels of complexity ranging from the automatic bottom-up process that involves the vicarious sharing of the bodily states of others seen in mimicry or emotional contagion (Shamay-Tsoory, 2014) to the more reflective and subjective experience of sympathy (Eisenberg & Eggum, 2009).
Neurobiological underpinnings of Empathy
Evidence of the distinction between affective and cognitive empathy comes from research on neurophysiology and the neural foundations of empathy. Clinical lesion studies have provided evidence for anatomical differentiation between cognitive and affective empathy processing (Shamay-Tsoory & Aharon-Peretz, 2007; Shamay-Tsoory et al., 2009;
Shamay-Tsoory, Tomer, Berger, Goldsher, & Aharon-Peretz, 2005).
Cognitive empathy
The neural systems engaged during the representation of the mental states of others include the medial prefrontal cortex (MPFC), specifically, anterior paracingulate cortex, as well as temporal-parietal junction and the temporal poles (Blair, 2008; Goel & Dolan, 2003).
It has been argued that the anterior paracingulate cortex is involved in mentalising, or ToM, 9
whereas the superior temporal sulcus and the temporal poles are believed to be involved in
processes which aid mentalising (Blair, 2008). Furthermore, the temporo-parietal junction, an
area of posterior superior temporal sulcus, has been reported to be specific to reasoning about
the content of others’ mental states, that is, inference making (Shamay-Tsoory, 2009).
As detailed in Figure 1.1, cognitive empathy is thought to reflect affective ToM only, not cognitive ToM. In the ToM literature these two facets of ToM have often been labelled
“hot” and “cold” cognition or reasoning, respectively to differentiate inferences about others’ affective states (emotions, preferences, beneficent or hostile intentions) from those of epistemic states (beliefs, knowledge, focus of attention). Neuroanatomical distinctions between the two kinds of ToM have also been made. Goel and Dolan (2003) reported that a reciprocal prefrontal activation pattern can be seen in response to emotional saliency. They found that “cold” reasoning (cognitive ToM) activated dorso-lateral prefrontal cortex
(DLPFC) and suppressed ventro-medial prefrontal cortex (VMPFC), however the reverse occurred in response to “hot” cognition (affective ToM) where VMPFC was activated and
DLPFC suppressed. Lesion studies have consistently demonstrated that deficits in cognitive empathy and ToM processing are specific to the VMPFC (Shamay-Tsoory et al., 2005;
Umeda, Mimura, & Kato, 2010), for example, patients with VMPFC damage were significantly impaired at cognitive empathy tasks such as faux pas, while presenting with intact emotion recognition and self-reported emotional empathy.
Affective empathy
Much of the neuroimaging literature involving affective empathy comes from
research examining empathy for pain. Singer et al. (2004) compared brain activity of healthy
participants when receiving pain as well as when viewing a signal indicating a partner was 10
receiving pain. Both the anterior insula and anterior cingulate cortex were activated in both
conditions. Similarly, Jackson et al. (2005) demonstrated that perceiving and assessing
painful situations in others was associated with significant bilateral changes in regions known
to play a role in pain processing, such as the anterior cingulate cortex, the anterior insula, the
cerebellum and the thalamus. This research suggests, once again, that simulation of another’s
experience, in this case at the neural level, is part of the empathic process.
Anatomical distinction between Cognitive and Affective empathy
Using resting-state functional magnetic resonance imaging (fMRI), Cox et al. (2012)
demonstrated that healthy adults with greater affective empathy showed stronger functional
connectivity among social-emotional regions (ventral anterior insula, orbitofrontal cortex,
amygdala). Conversely, individuals with greater cognitive empathy, relative to affective
empathy, showed stronger connectivity among brain regions implicated in interoception
(awareness of own physiological state), autonomic monitoring and ToM (brainstem, superior
temporal sulcus, ventral anterior insula). The ventral anterior insula is involved in both emotional and cognitive empathy suggesting at least some overlap between the two processes. As these two empathic processes are unlikely to occur separately, this study demonstrates the interaction between both cognitive and affective trait empathy in typical adults. Indeed, Gu et al. (2013) have demonstrated the importance of the anterior insula cortex for integration of cognitive and emotional empathy processing. Similarly, in a real- time functional MRI study, Raz et al. (2014) demonstrated distinct patterns of activation in regions associated with affective empathy (anterior cingulate cortex, anterior insula) and regions associated with ToM (MPFC, superior temporal sulcus and temporo-parietal 11
junction) whilst participants watched empathy-evoking videos. The study also demonstrated connectivity between these two networks whilst engaging in empathic processing (watching emotional films).
Measures of Empathy
There are many measures of empathy in the literature. Some focus on empathy as a single construct, whereas others distinguish between the cognitive and affective components.
Further, some measures of empathy rely on subjective self-report responses, whereas others
rely on more objective responses such as task performance on empathy measures as well as
physiological and neural responses, including skin conductance, heart rate, EEG and imaging
techniques such as fMRI.
Subjective Measures
One of the most commonly used and well-established measures of empathy is the
Interpersonal Reactivity Index (IRI; Davis, 1980). The IRI is a multidimensional self-report
measure of empathy with four seven-item subscales: “perspective-taking”, “fantasy”,
“empathic concern” and “personal distress”. Two scales, in particular, Perspective Taking and Empathic Concern are considered valid measures of cognitive and emotional empathy, respectively (Davis, 1983).
More recently, Baron-Cohen et al. (Baron-Cohen & Wheelwright, 2004) developed
the Empathy Quotient (EQ), which has been used widely in the literature on empathy, autism
spectrum disorders and other psychopathologies. The EQ is a self-report 60-item measure of 12
empathy. Research has shown that individuals with high functioning autism score
significantly lower on the EQ than do controls. Furthermore, females tend to score slightly
but significantly higher than males on the EQ (Baron-Cohen & Wheelwright, 2004).
One problem with self-report measures of empathy is that they vary with respect to what they measure. In a recent review of self-report empathy questionnaires, Baldner et al.
(2014) performed exploratory factor analysis on six different empathy questionnaires
including the IRI and found only low to moderate inter-scale correlations as well as many items that did not load onto any factor of empathy. These results highlight questions about whether various measures of self-report empathy were actually measuring the same things.
For example, the Perspective Taking subscale of the IRI did not factor on to the cognitive subscales of two other measures, indicating they are measuring different constructs completely. Furthermore, subscales such as the Fantasy and Personal Distress subscales of the IRI are believed to measure concepts related to empathy, but not empathy itself (Baron-
Cohen & Wheelwright, 2004) and have been recommended not to be used in studies examining empathy (Baldner & McGinley, 2014).
Objective Measures
Behavioural measures of empathy are more objective as they measure performance
reflecting empathic ability. These have also been used to demonstrate a dissociation between
cognitive and emotional empathy, especially in research investigating ASD and conduct
disorder or psychopathy. Three performance measures of cognitive empathy were developed
specifically in order to examine empathy difficulties in ASD. These are the Faux Pas task
(Simon Baron-Cohen et al., 1999), the Strange Stories test (Happe, 1994) and the
Multifaceted Empathy Test (MET) (Dziobek et al., 2008) which assesses both cognitive and 13
emotional empathy. Both the Faux Pas task and the Strange Stories test involve listening to a
story and making a judgment about whether someone said something awkward or why
something was done (justification), both of which require some degree of mentalising and
inference making. The MET is slightly different in that participants are required to make
judgments about what a person in an image is thinking/feeling (cognitive empathy) and then
rate how that picture made the participant feel (emotional empathy).
Physiological measures provide indications of emotional responsivity and do not rely
on any form of self-report, in which other processes such as working memory or verbal
ability may interfere. Replication of another person’s emotion is often measured using
electromyography (EMG) responses to either facial expression, with electrodes attached to
the muscles of the face, or to other muscles in the body, such as in response to pain stimuli.
Measures such as skin conductance and heart rate provide indices of autonomic
arousal. For example, Fernandez et al. (2012) demonstrated that participants showed
increased skin conductance and heart rate responses when watching films evoking feelings of
fear or anger and that these responses correlated with self-report reactions. Similarly, Balconi
et al. (2014) found positive associations between self-report measures and psychophysiological behaviour, primarily for emotions rated as more arousing and negative in valence.
Lastly, anatomical and functional imaging techniques have provided a wealth of information on brain regions and connectivity during empathic understanding and responding. In a review of over 200 papers using fMRI, Van Overwalle (2009) explored the brain regions associated with social cognition, of which empathy is an important component.
Specifically, regions involved in both mentalising and emotion were involved when participants were predicting a future emotional response (Hooker, Verosky, Germine, Knight, 14
& D'Esposito, 2008). Functional MRI studies show that regions such as the anterior insula
and cingulate cortex activate when individuals observe targets who are embarrassed or
socially excluded as well as more general emotions such as fear, anger or disgust (McCall &
Singer, 2013). Further, greater neural activity in emotion-related regions when predicting an emotional response has been found to be associated with more self-report empathy, similar to studies examining autonomic responses (Hooker et al., 2008).
While such objective measures of empathy have clear advantages over subjective measures, the nature of the paradigms (e.g. lying still in a scanner) limits the kinds of stimuli that can be used. Thus many of the stimuli used in imaging and physiological studies exploring empathy rely on static scenes such as the International Affective Picture System
(IAPS) or static images of facial expressions (Ekman & Friesen, 1976). The extent to which these can be considered representative of empathy in realistic everyday situations must be questioned. An alternative is the use of audio-visual stimuli which are dynamic and more
“real world” requiring participants to engage multiple processes (visual and auditory). In the following thesis, audiovisual and/or dynamic materials are used in preference to static stimuli where possible.
Models of Empathy
There have been a number of models of empathy proposed by different theorists.
These vary as they arise from various psychological disciplines, and are used to explain various psychopathologies and phenomena. One model put forward by Marshall et al. (1995) to explain the empathy process in sex-offenders, proposes that empathy can be viewed as a 4- stage process model. These four stages consist of (1) emotion perception, (2) theory of mind,
(3) emotion replication and (4) decision-making. While stages (1) to (3) refer explicitly to 15 constructs already described as part of empathy stage (4), decision making, could be conceptualised as a more generic cognitive process that is not specific to empathy itself.
Overall, this model introduces several constructs that have been researched in the clinical field with clear relevance and there is some evidence for their independence. The disadvantage of this model is that it does not address the issue that people need to distinguish between self and other when interpreting others’ thoughts, feelings and behaviour.
Decety et al. (2007) have proposed a model of empathy from a neuropsychological perspective. In this model the neural connections and pathways are the focus of the model.
Their model combines both representational aspects, such as memories, as well as processes, such as computational procedures that are neutrally localized. They suggest that one of the main components of empathy is based on an unconscious mental simulation of the emotional states of others (Decety et al., 2007). The four major cognitive processes involved in the
Decety et al. (2007) model consist of mental flexibility, emotion sharing, self-awareness and emotion regulation. Each process has unique patterns of cortico-cortical connections, which determine its function, and differences in neural activity during the experience of empathy are produced by distributed subsystems of brain regions. Furthermore, the model involves massive parallel processing and bidirectional links between areas. As with Marshall’s model, the model described by Decety et al includes processes that are clearly specific to empathy
(such as emotion replication) and others, such as self-awareness and emotion regulation that may be more generic. One difficulty with this model is that it implies hierarchy of processing from affective to cognitive, however there is evidence in the literature for a dissociation between these two types of processing (Shamay-Tsoory et al., 2009).
Perry and Shamay-Tsoory (2013) proposed a model that incorporates neural mechanisms involved in the cognitive processes involved in empathy. The authors suggested that the experience of empathy occurs when both the cognitive and affective networks are 16
activated. Theory of mind is the process underlying cognitive empathy, whereas a more
automatic, emotion simulation process underlies affective empathy. The neural mechanisms
identified as being involved in the ToM network include the medial prefrontal cortex, specifically the ventral medial PFC, the superior temporal sulcus and the temporo-parietal
junction. The affective/simulation network involves the anterior cingulate cortex, the
amygdala and the anterior insula.
One attribute to empathy that is not catered for in any of these models, is the notion of
social motivation (as discussed in Section 1.1), a major theoretical construct that could
potentially explain lack of empathy due to a failure to orientate to others. This essentially
represents an attentional or goal oriented process that is a precursor to empathic processes.
The following model is an adaption based upon the strengths of each of the prior models and
including social motivation. It includes both cognitive and emotional empathic processes, as
well as processes that are necessary for empathy but which maybe more generic and the
neurobiological underpinnings. Figure 1.2 displays this proposed model of empathy
combining the three models discussed. 17
Cognitive Empathy Affective Empathy
Faux Pas Empathic Concern (IRI) Common Measures Personal Distress (IRI) Strange Stories Psychophysiological measures Perspective Taking (IRI) Fantasy (IRI)
Social Motivation
Self-Awareness
Theoretical components Metacognition
Mental Flexibility Self-regulation
Affective Theory of Mind Emotion Perception
Mentalising Emotion Replication/Contagion
Ventromedial Prefrontal Orbitofrontal Cortex Cortex Anterior Cingulate Cortex Neural substrates Temporo-parietal Junction Amygdala Superior Temporal Sulcus Insula
Figure 1.2. Proposed Model of Empathy
18
Empathy in ASD
Empathy deficits or impairments have long been considered one of the hallmarks of
ASD. However, with more recent investigation, empathy deficits in ASD have been shown to follow a distinct pattern. That is, while cognitive empathy has been shown to be consistently impaired in individuals with ASD, the degree to which these individuals are impaired at affective empathy remains unclear.
Cognitive Empathy
Theory of Mind and Perspective Taking: Decades of research support the notion that cognitive empathy, mentalising and perspective taking are impaired in both children and adults with ASD (Golan, Baron-Cohen, & Hill, 2006; Jolliffe & Baron-Cohen, 1999).
Objective measures of theory of mind such as the Faux Pas test and Strange Stories test have demonstrated consistent impairments in individuals with ASD (Spek, Scholte, & Van
Berckelaer-Onnes, 2010; Zalla, Sav, Stopin, Ahade, & Leboyer, 2009). In a large meta- analysis on children with ASD, children with Mental Retardation and matched typically developing control children, theory of mind was found to be impaired in both children with
ASD and children with developmental delay, however, theory of mind was impaired more severely in children with ASD (Yirmiya, Erel, Shaked, & Daphna, 1998). This impairment in theory of mind holds even when verbal confounds are removed, such as in the “Triangles” tasks, whereby participants must infer intentions, behaviours or actions of triangles in social and non-social conditions (Abell, Happe, & Frith, 2000).
Neurophysiological evidence of reduced or impaired cognitive empathy has been demonstrated using functional imagining techniques. Mason et al. (2008) found that, unlike 19
neurotypical adults, individuals with ASD did not differentiate between three types of
inferences (intentions, emotional states and physical causality) and showed lower functional
connectivity within brain regions of the theory of mind network. Specifically, adults with
high-functioning ASD have been shown to have significantly less amygdala activation during
a mentalising task (Baron-Cohen et al., 1999) as well as reduced activity in the left medial frontal cortex (Happe et al., 1996) and orbitofrontal cortex (Baron-Cohen et al., 1994). Blair
(2008) has further evidenced a dissociation between autism and psychopathy by demonstrating that aspects of social cognition in which the amygdala plays a role (such as affect-related judgments based on facial stimuli) are impaired in autism, but remain intact in psychopathy.
Mental Flexibility: One area in which individuals with ASD have been shown to be impaired is mental flexibility, indicating problems in the ability to shift to a different thought or action according to changes in a situation. This deficit is more generic within the autism spectrum and thus affects not only social behaviour, but can also be seen in the perseverative and stereotyped behaviour that characterizes ASD (Hill, 2004).
Affective Empathy
Far less research has focused on emotional empathy in ASD with the majority
examining emotion perception or the physiological correlates of either emotional contagion
or mimicry.
Emotion perception: The literature on emotion perception abilities in autism is also somewhat divided. Lacroix et al. (Lacroix, Guidetti, Rogé, & Reilly, 2009) found that children with autism spectrum disorder did not perform significantly worse than controls on a measure of emotion perception, whereby participants were required to identify and recognize 20 emotional facial expressions. The facial expressions used were quite basic and exaggerated, which may have led to ceiling effects. Kessels et al. (Kessels, Spee, & Hendriks, 2010) addressed this concern in their study by using dynamic facial emotional expressions with a group of young adolescents with ASD. They found that, overall, the ASD group performed equivalent to the controls on the emotion perception task, with the exception of a slightly worse performance of the ASD group on the emotions fear and disgust (Kessels et al., 2010).
Using the same measure, the Emotion Recognition Test, Law Smith et al. (Law Smith,
Montagne, Perrett, Gill, & Gallagher, 2010) found results consistent with the findings of
Kessels et al. (Kessels et al., 2010). They demonstrated that adolescents with ASD were only slightly less accurate at recognizing emotional expressions of anger, disgust and surprise at low intensities as compared with controls (Law Smith et al., 2010). Overall this suggests that young adults with ASD have little to no problem recognising emotional expressions. Thus, if problems with affective empathy occur, these are in some other aspect of the process.
Emotion replication/contagion: Investigations of emotion replication in people with
ASD have used stimuli ranging from facial and body expressions, through to affective scenes.
Facial mimicry responses to static photos of emotional expressions in children and adults with ASD have been reportedly delayed and reduced, although not absent when responses were automatic, i.e. stimuli were viewed passively or a backwards masking paradigm was used (Mathersul, McDonald, & Rushby, 2013b; Oberman, Winkielman, & Ramachandran,
2009). However, when responses were voluntary or prompted, no differences between control children and children with ASD were found (McIntosh, Reichmann-Decker, Winkielman, &
Wilbarger, 2006). Conversely, in a study examining contagion to video recordings of spontaneous facial and body expressions while a person ate a lemon (Hagenmuller, Rossler,
Wittwer, & Haker, 2014) individuals with ASD responded with increased salivation as did 21 the control group, however, this response was slightly reduced indicating a reduced, but not absent contagion response. Unfortunately, facial responses were not examined in this study.
Autonomic responses (skin conductance and heart rate responses) are more variable in
ASD. Mathersul et al. (Mathersul, McDonald, & Rushby, 2013a) reported comparable arousal and heart rate in adults with ASD and controls in response to implicit emotional stimuli, despite abnormal mimicry. Similarly, Louwerse et al. (2013) showed that both adolescents with ASD and matched controls responded with comparable levels of arousal
(both self-report and autonomic) to images with social or affective content.
Overall, these studies provide equivocal evidence as to whether automatic mimicry and/or autonomic responses to emotional stimuli are impaired in ASD, although they suggest that normal responses do occur when processing is deliberate and conscious. Indeed, one study in young children with ASD found that these children had reduced unconscious emotional reactivity as measured by pupillary responses, but normal responsivity to consciously presented emotion (Nuske et al., 2014). One of the shortcomings of many of these studies is the use of static images as facial expressions or emotion-inducing images such as the International Affective Pictures System (IAPS). Static images do not reflect real- life situations and therefore cannot easily be generalized to day-to-day functioning. Another limitation of these studies is that, apart from Louwerse et al (2013), physiological responses are not directly compared with subjective understanding and interpretation of these responses.
Whilst individuals with ASD may have some level of automatic response to emotional stimuli (emotional contagion), their understanding of this response (metacognition) is unknown.
Another avenue for examining mimicry and emotional contagion is to study responses to observing pain in others. Studying empathy for pain provides a different, potentially 22
potent, means to examine automatic responses including mimicry and autonomic changes as
well as subjective evaluation. Several studies have examined pain perception in people with
ASD with mixed results. For example, both Fan et al. (2014) and Chen et al. (2016) have
demonstrated typical mimicry or sensorimotor response in individuals with ASD. Further,
emotional responding as measured by both self-report and EEG was heightened. In contrast,
Minio-Paluello et al. (2009) found that, when observing pain affecting another person, individuals with ASD did not demonstrate any neurophysiological response in their corticospinal system, as measured by EMG potentials following transcranial magnetic stimulation (TMS), indicating an absence of sensorimotor response in these individuals.
Collectively, these studies suggest that evidence for mimicry and contagion for pain is mixed in ASD while the subjective emotional response to pain appears typical or even heightened in this group. Most of these empathy-for-pain studies focused on mimicry and sensorimotor responses and did not consider autonomic emotional responding. As detailed above,
Mathersul et al (2013) found group differences for mimicry but not autonomic responses in people with ASD. This suggests that these kinds of responses are not necessarily collinear.
The use of autonomic measures such as skin conductance and cardiac recordings is an additional index of emotional response to pain stimuli. This can then be compared with self- report emotional response in order to gain more insight into emotional contagion and meta- cognitive empathic responses.
Metacognition/Self-awareness/Self-regulation in empathy
Metacognition: Other research has focused more particularly on the meta-cognitive,
higher-level appraisal of emotional empathy including making subjective empathic judgments about the observed other and one’s self (Lockwood, Bird, Bridge, & Viding, 2013) 23
(see green box in model). In several studies, children with ASD have demonstrated relatively good emotional empathy in contrast to cognitive. For example, in a study using the multifaceted empathy test (Dziobek et al., 2008), in which participants must infer mental states of characters (cognitive empathy) in a series of images as well as rate their own emotional reactions in response to the images (emotional empathy), the ASD group had difficulties with the cognitive empathy component, but not the emotional empathy
component. When boys with ASD, boys with conduct disorder and matched controls were
compared on a task measuring both cognitive and emotional empathy, Schwenck et al. (2012)
found that while boys with ASD demonstrated significant cognitive empathy difficulties
compared with those with conduct disorder, they reported significantly more emotional
affection than those with conduct disorder. Further, children with ASD, when compared to control children, made comparable observed responses to the emotional states of the stimulus, based on both parent observations and an unfamiliar adult observation (Scheeren, Koot,
Mundy, Mous, & Begeer, 2013). However, not all research has produced this pattern. In a study by Shülte-Ruther et al. (2011) participants with ASD were asked to indicate the emotional expression of the person in the image (other-task) and the emotion that the image provoked in them (self-task). While response times and correct responses in the other-task did not differ between the ASD group and controls, individuals with ASD reported fewer episodes of emotional contagion during the self-task.
Self-awareness: Subjective self-report on trait questionnaires of empathy also tap meta-cognitive awareness because these instruments measure not only empathy but, in the case of emotional empathy, self-awareness of one’s own emotional responses. In two studies
(Rogers et al., 2007; Rueda, Fernandez-Berrocal, & Baron-Cohen, 2015) a dissociation between cognitive and emotional empathy using the self-report IRI (Davis, 1983) was found. 24
In both cases individuals with ASD scored lower than controls on cognitive empathy
subscales (perspective taking, fantasy), however, no significant differences were found on the
emotional empathy subscales (Empathic Concern, Personal Distress) suggesting that both
emotional responsivity and self-awareness were intact. Interestingly, Rogers et al. (2007) and
Dizobek et al. (2008) found that individuals with autism tended to score higher than controls on the personal distress subscale of the IRI. Rogers et al. (2007) suggested this could be due to greater emotional empathy in these individuals, however individuals with autism also tend to experience higher levels of anxiety, and increased personal distress scores may simply be a reflection of this. The variability in this measure and other facets of empathy research in people with ASD raises the question as to whether there are other factors that contribute to variable empathy performance in people with ASD.
One consideration is that emotional responses to another’s plight may be intact in people with ASD but there is variability in the extent to which they are able to self-report on their own emotions and thoughts. This dovetails into research on self-awareness and alexithymia in ASD. Alexithymia, that is, an inability to describe or recognize one’s own emotions (Moriguchi et al., 2006), is a condition or trait-like characteristic that is often linked with autism. Individuals with ASD often score higher than controls on measures of alexithymia, such as the Toronto Alexithymia Screen (TAS-20) or the Bermond Vorst
Alexithymia Questionnaire (BVAQ) (Berthoz & Hill, 2005) with reported rates above 50% compared with approximately 10% in the general population (Bernhardt et al., 2014; Hill
Berthoz & Frith, 2004).
Furthermore, there seems to be a link between poor self-awareness (alexithymia) and low empathy even in those without ASD. Moriguchi et al. (Moriguchi et al., 2006) examined neuronal activation in individuals with high and low alexithymia during a non-verbal
(animated triangles) ToM task. Neural activity in the MPFC was not only decreased in those 25 with high alexithymia but related to perspective-taking scores in the IRI. In general, the high alexithymia group self-reported low perspective-taking ability and empathic concern and high personal distress relative to those low in alexithymia (Moriguchi et al., 2006). Interestingly, these IRI response profiles are consistent with those found by Rogers et al. (Rogers et al.,
2007) with individuals with ASD. In addition, Moriguchi et al. (Moriguchi et al., 2007) found that individuals with high alexithymia have lowered self-other discrimination abilities and they are inclined to simulate the actions of others. They also tend to overlap the action of others onto themselves, suggesting alexithymia is not only associated with decreased awareness of emotion but also decreased ability to dissociate between self and other’s emotions. This raises the interesting question as to whether people with ASD have difficulty with two facets of emotional empathy: reporting on their own empathic responses to others and separating their own emotional responses from those perceived in observed others.
Self-awareness of emotions can be inferred by comparing autonomic and mimicry responses with self-report interpretation of these responses. If one has normal or typical autonomic responses to emotional stimuli but reduced subjective reporting of emotional distress, this suggests reduced self-awareness. Indeed, while some individuals with ASD appear to report increased or typical levels of distress following exposure to emotional stimuli, others have reported reduced emotional contagion and emotional responding
(Yirmiya, Sigman, Kasari, & Mundy, 1992). Further, Mathersul et al. (2013c) found that while some individuals with ASD did demonstrate reduced autonomic arousal, it was specifically those who demonstrated normal arousal levels who self-reported low emotional empathy which suggests a dissociation between emotional responsivity and self-awareness in
ASD.
26
Self-regulation: Emotion regulation can be defined as the dynamic ordering and
adjusting of emotional behaviour (Southam-Gerow & Kendall, 2002) and is frequently found
to be impaired in individuals with ASD (Mazefsky et al., 2013). Samson et al. (2012) found
that adults with ASD reported using less cognitive reappraisal, that is, the process of taking
another mental perspective on a stimulus or situation in order to reinterpret its meaning, than
controls. Given the ability to perspective take is impaired in these individuals, this may lead
to difficulties using cognitive reappraisal strategies to regulate emotions. Samson et al. also
found that those with ASD also used more suppression as a way to regulate emotions,
suggesting a less adaptive emotion regulation profile. Samson et al. (2015) suggested that individuals with ASD have more difficulty generating cognitive reappraisal strategies due to a decreased ability to describe and identify emotions (alexithymia) and reduced self- awareness of emotions. Emotion dysregulation has been linked with increased social anxiety in the ASD population (Swain, Scarpa, White, & Laugeson, 2015). Similarly, Rieffe et al.
(2011) found that maladaptive emotional coping strategies, such as catastrophising and self-
blame, contributed positively to internalizing symptoms, such as worry and rumination in
children with ASD. Relating this back to empathy, individuals who can regulate their
emotions are more likely to experience empathy and regulation of emotions is an important
factor in modulating one’s own vicarious emotional response so that it is not experienced as
aversive or distressing (Decety & Moriguchi, 2007).
Social Motivation in ASD
A final component of the model is that of social motivation. Early developmental research has demonstrated that young children with ASD tend to look less at the eye region of 27 pictures of faces (Falck-Ytter, Bölte, & Gredebäck, 2013; Stauder, Bosch, & Nuij, 2011) as well as showing a preference for faces with eyes closed compared with eyes open (Kylliainen et al., 2012). Together, these studies indicate that children with ASD may lack normative approach-related motivational response to eye contact. However, many children, adolescents and adults with ASD often have a desire to form meaningful relationships but lack the skills necessary to do so, resulting in increased feelings of loneliness and peer victimisation
(Bauminger & Kasari, 2000). Indeed, Mathersul et al. (2013d) found that adults with ASD spent more time appraising images of static neutral faces than controls, as measured by a failure to habituate to facial stimuli, indicating possible increased allocation of significance to facial stimuli. Interestingly, this same group (Mathersul et al., 2013a) also showed that adults with ASD engaged with social affective (positive and negative) images initially, but failed to sustain their attention over time. These conflicting results raise questions as to the nature of social motivation in individuals with ASD. Another way of examining this concept is by examining what happens when people with ASD are ostracised.
Ostracism
The need for affiliation or to belong has long been explored in the literature and it is common knowledge that human beings fear rejection and exclusion and therefore conform, comply or in some way alter their behaviour in order to be included or accepted (Bartz &
Hollander, 2006). Indeed, it is believed that humans possess an internal monitoring system that is attuned to reductions in social belonging (Leary & Baumeister, 2000). The primary function of this monitoring system is to recognize rejection cues in the environment in order to activate the autonomic nervous system, that is, the “fight or flight” response. Social rejection cues, such as being excluded from a conversation, have been shown to increase 28
salivary cortisol levels within 10-30 minutes (Engert et al., 2011). Williams’ model proposes
that ostracism threatens four fundamental needs: belonging, self-esteem, control and meaningful existence (Williams, Cheung, & Choi, 2000). When these needs are threatened, individuals will react with psychological discomfort including a range of feelings such as negative mood, anxiety, social pain and physiological distress (Lustenberger & Jagacinski,
2010; Williams & Sommer, 1997). To cope with these feelings, individuals will attempt to recover their threatened needs. Ostracism is, therefore, a useful means to look at social motivation and whether it is impaired in people with ASD.
Anxiety
Another critical factor affecting social motivation is the concept of anxiety. Despite
having a desire for social interaction or affiliation, anxiety can be a significant disadvantage to
forming and maintaining relationships (La Greca & Lopez, 1998). Anxiety is one of the most
common co-diagnosed disorders in individuals with ASD. Social anxiety is particularly
important when conceptualising ASD as there is significant overlap in diagnostic criteria
between the two conditions (Kuusikko et al., 2008). Social Anxiety Disorder is characterized by
discomfort around social interaction and concern about being embarrassed and judged by others
(American Psychiatric Association, 2013). While people with social anxiety desire social
contacts and want to participate in social situations, their anxiety can become unbearable when
they make these attempts. Therefore, anxiety can often lead to isolation, which can lead to
either absence or dampening of development of social skills (American Psychiatric Association,
2013). A predominant feature of social anxiety is the focus on the self, rather than others (Clark
& Wells, 1995). Socially anxious individuals are less likely to approach others and keep further
distance when interacting with others (Rinck et al., 2010). The social difficulties of social 29
withdrawal, preference for being alone and not speaking in social situations that are
characteristic of social anxiety are also characteristic of ASD. Furthermore, these avoidant
characteristics associated with both disorders have been shown to impact negatively on the
individual’s ability to develop social and emotional processing skills (Bellini, 2004). It is,
therefore, of interest to determine whether social anxiety in people with ASD plays a role in
their difficulties with empathy.
General Aims of Thesis
Using a model of empathy derived from research on neurobiological underpinnings and cognitive and emotional processes, the predominant aim of this thesis was to explore the psychological and physiological processes involved in emotional empathy and the role of
social motivation in empathy in adults with ASD. The literature is fairly consistent with
regards to cognitive empathy impairment in ASD, with research indicating difficulties or
abnormalities on behavioural, self-report and neurological measures. However, there is considerable debate on whether emotional empathy is heightened, typical or reduced in ASD.
Much of this debate comes from variability in the specific process of emotional empathy targeted in the research. Emotional empathy involves various levels of empathic responding, from the more automatic, bottom-up responses such as “emotional contagion” to the high- level subjective emotional responses such as sympathy or compassion made possible by meta-cognition and self-awareness.
Much of the inconsistency arises from differences in measurement of emotional empathy: self-report, behavioural performance or psychophysiological. Whilst various studies have focused on one or two of these components, no research to date, has examined all three components and the relationship between them. Furthermore, the role of social motivation in 30
empathic processing and responding has been largely absent in research on empathy in ASD.
Specifically, the roles of social exclusion and anxiety have yet to be examined in terms of
their involvement in empathic understanding and responding in people with ASD. Therefore,
this thesis aimed to investigate emotional empathic responses, in terms of automatic mimicry,
autonomic responding and subjective interpretation via self-report, in individuals with ASD.
Further, the role of social motivation in empathic understanding was explored by assessing
the effects of social exclusion and the role of anxiety in adults with ASD.
Study 1 aimed to investigate the psychophysiological empathic responses to
emotionally distressing video stimuli. The use of video stimuli provides more “real life”
scenes than static pictures and is therefore more generalizable to everyday situations.
Physiological responses were compared with self-report ratings of mood and arousal as well as self-report trait empathy in order to understand the relationship between autonomic and meta-cognitive, subjective experience of emotional empathy in individuals with ASD.
Study 2 also explored this relationship between autonomic physiological responses to a specific type of empathy response: empathy for pain. This study aimed to examine bottom-
up sensorimotor responses to pain stimuli in individuals with ASD compared with matched
controls. Whilst previous research has mostly focused on automatic mimicry of pain
response, autonomic emotional responding has largely been ignored. Therefore, along with
EMG as a measure of sensorimotor responding to painful stimulation to a muscle of the hand,
this study examined the physiological emotional responses to these painful stimuli by using
measures of skin conductance, heart rate and facial muscle responses. Further, the
relationship between these bottom-up sensorimotor empathic responding and top-down
subjective pain evaluations was explored as well as associations with trait level empathy. 31
Conflicting evidence of whether social motivation is impaired or typical in ASD
highlights the need to assess this concept using a well-established experimental paradigm. As the degree to which one is socially motivated is likely to affect empathic responding, social motivation is an important component of the model of empathy. Therefore, in order to explore the role of social motivation and need for affiliation in relation to empathic ability in adults with ASD, Study 3 examined emotional responses, both psychological and physiological, in individuals with ASD to induced feelings of ostracism via the Cyberball task. The study aimed to identify whether adults with ASD are able to identify ostracism when it occurs and their emotional response to feelings of social rejection.
Social motivation can be strongly affected by anxiety, especially social anxiety. Very little research has focused on whether emotional traits such as anxiety play a role in empathy responding or ability. Study 4 explored the relationships between anxiety, both trait and more specifically, social anxiety, and self-report ratings of the two components of empathy: emotional and cognitive. Further, this study aimed to determine whether anxiety contributes independently to difficulties with empathy, especially in individuals with ASD.
32
CHAPTER 2
GENERAL METHODS
This chapter provides general methodological details that are common to all studies. Specific elements from each experiment are described more fully in the specific chapters.
Participants
Twenty-seven high-functioning individuals with ASD (mean age 28 years, age range 16-61, 4 females) and thirty-two non-clinical control individuals (mean age 27 years, age range 18-50,
5 females) were recruited for all studies presented in this thesis. ASD participants were recruited from support groups, university counselling and disability services, clinicians as well as Aspect (Autism Spectrum Australia). Control participants were recruited from an online university advertising service (Sona). Participants were screened for serious mental illness diagnoses, with the exception of anxiety and depression, as well as any neurological disorders, physical brain disorders or other developmental disorders (besides ASD). All participants were reimbursed for their time. Participants gave written informed consent in accordance with the University of New South Wales Human Research Ethics Committee
(UNSW HREC).
All participants in the ASD group had been diagnosed by qualified health professionals, experienced in diagnosis of ASD (clinical psychologists, paediatricians, neuropsychologists and psychiatrists), but independent of the research project. In most cases formal diagnostic reports were made available to the researchers. For two participants, where formal diagnosis 33
was unable to be confirmed via reports, the Autism Diagnostic Interview – Revised (ADI-R)
was completed.
Sample Size
It was determined that a total sample size of 52 participants would be required to identify
between subject effect at an effect size of 0.80 with 80% statistical power with an alpha level
of .05 (5% error rate).
Materials and Measures
All participants completed the following battery of questionnaires once they had completed
all four experiments. A summary of these measures is listed below. Other measures used for specific studies are outlined in the related chapters.
Wechsler Abbreviated Scale of Intelligence
The Wechsler Abbreviate Scale of Intelligence (WASI) provides a brief, reliable measure of
cognitive ability (Wechsler, 1999). All participants were administered the two-subtest format
(Vocabulary and Matrix Reasoning) in order to obtain a measure of full-scale IQ (FSIQ) and to ensure they had normal intelligence. The two-subtest version of the WASI was used to reduce testing time and avoid fatigue effects. In adults, the WASI two-subtest FSIQ correlates .87 with the Wechsler Adult Intelligence Scale (WAIS) full scale IQ (FSIQ).
34
Interpersonal Reactivity Index (IRI) (Davis, 1983)
The IRI is a 28-item self-report questionnaire consisting of four 7-item subscales, each measuring a specific aspect of empathy. Participants respond on a 5-point Likert scale (0 =
does not describe me at all, 4 = describes me very well). Only two of the four scales were
analysed, namely the Perspective Taking (PT; tendency to adopt another person’s point of
view in everyday life) scale, a measure of cognitive empathy and the Empathic Concern (EC;
tendency to experience feelings of warmth, compassion and concern for other people) scale, a
measure of emotional empathy. There has been debate in the literature as to whether the
Fantasy and Personal Distress subscales are true measures of empathy or whether they
measure constructs associated with empathy such as imagination and anxiety, respectively
(Baron-Cohen & Wheelwright, 2004). The IRI has good internal consistency with alpha
coefficients ranging from 0.68 to 0.79 and has been shown to correlate with other measures
of empathy demonstrating good construct validity (Davis, 1983).
Empathy Quotient (Baron-Cohen & Wheelwright, 2004)
Participants completed the 40-item version of the Empathy Quotient (EQ). The Empathy
Quotient is a self-report questionnaire with a forced choice response. Participants must answer on a 4-point Likert scale (“strongly agree”, “slightly agree”, slightly disagree” and “strongly disagree”). An empathic response is given a score of “1” or “2” depending on the strength of response, other responses are scored “0”. Scores are summed providing a total score between 0 and 80. The EQ has very high internal consistency with alpha of 0.92 (Baron-Cohen &
Wheelwright, 2004). It correlates highly with other measures of empathy such as the IRI, indicating strong construct validity. Whilst the EQ is usually used as a measure of general trait empathy and not split into subscales, Lawrence et al. (2004) performed factor analysis on the 35
EQ resulting in three distinct subscales: Cognitive Empathy (EQ-CE), Emotional Reactivity
(EQ-ER) and Social Skills (EQ-SS).
Autism Quotient (Baron-Cohen, Wheelwright, Skinner, Martin, & Chubley, 2001)
The AQ is a 50-item self-report questionnaire designed to measure autistic traits in individuals with average or high-average IQ. Participants respond on a four-point Likert scale: “strongly disagree”, “slightly disagree”, “slightly agree” and “strongly agree”.
Responses are coded as zero or one with one point scored if the participant selects the autistic trait response. The autistic trait is indicated by a response of “strongly agree” or “slightly agree” in half the items and “strongly disagree” and “slightly disagree” in the other half. The
Autism Quotient was used as a diagnostic check with all participants in the ASD group scoring 32 or above. This score has been identified as being indicative of having high levels of autistic traits.
Autonomic Measures
Skin conductance level and facial EMG were recorded with an 8/30 Powerlab Data
Acquisition System (ADInstruments, Castle Hill, Australia). SCL was measured from the distal phalanges of digits II and IV of the nondominant hand with two silver-silver chloride
(Ag/AgCl) electrodes, filled with electrode paste of 0.05 M NaCl from. The signal was calibrated before each session to detect activity in the range of 0-40 μS (microSiemens) and digitized at a sampling rate of 100 Hz.
Cardiac responses were measured continuously using a finger photoplethysmograph placed on digit III of the non-dominant hand. 36
EMG activity of the left corrugator supercilii (CR) muscle was measured according to the original guidelines proposed by Fridlund and Cacioppo (1986). Shielded 9 mm diameter gold-plated surface electrodes were placed bipolarly over the CR muscle at an inter-electrode distance of approximately 1.5 cm, and filled with Ten20 conductive paste. An additional ground electrode was placed on the upper portion of the forehead. The data was digitized at a sampling rate of 1000 Hz and amplified by 20,000. The raw EMG was integrated with a time constant set to 100 ms.
37
CHAPTER 3 STUDY 1: Emotional Empathy in adults with ASD1
The literature with respect to emotional empathy and ASD was reviewed in Chapter 1.
For clarity, a summary of those arguments as they pertain to the first study of this thesis are re-iterated here.
Difficulties with cognitive empathy, mentalising and perspective taking have been shown to be one of the hallmarks of ASD (Castelli, Frith, Happé, & Frith, 2002; Jolliffe &
Baron-Cohen, 1999; Zalla et al., 2009). Emotional empathy, on the other hand, remains unclear and inconsistencies may be due to the complexity and multifaceted nature of emotional empathy from bottom up emotion contagion responses to top down subjective and cognitive processing of emotional response and self-awareness, as has been outlined in the model in Figure 1.2. Furthermore, the use of different measures, ranging from autonomic physiological responses to subjective self-report ratings, has added to the inconsistencies in findings.
Objective approaches to the measurement of emotional empathy have used psychophysiological techniques such as skin conductance level (SCL), facial electromyography (EMG) and heart rate which can index emotional responses such as arousal and contagion. Typically stimuli used to evoke these responses are simple, static, visual images of emotional facial expressions or other emotion provoking scenes, such as the
International Affective Picture System (IAPS) (Lang, Bradley, & Cuthbert, 1997). Using such stimuli, it has been found that adults and adolescents with ASD have reduced or atypical facial EMG responses, via the corrugator (frown) and zygomaticus (smile) muscles, as well as reduced skin conductance levels to emotional faces (Mathersul et al., 2013b; McIntosh et
1 Published as Trimmer, E.M., McDonald, S. & Rushby, J.A. (2016) Not knowing what I feel: Emotional Empathy in Autism Spectrum Disorder. Autism, 1-8; DOI: 10.1177/1362361316648520 38 al., 2006; Oberman et al., 2009). Interestingly, these differences were not apparent when highly arousing pleasant (erotica) and unpleasant (mutilated bodies) images of a social nature were used (Mathersul et al., 2013a) suggesting that normal affective responses can be triggered by static visual stimuli in ASD if images are sufficiently salient.
Other research focused upon task performance has also raised questions about whether emotional empathy ability is disrupted in ASD (Lockwood, Millings, Hepper, &
Rowe, 2013). In a study using the Multifaceted Empathy Test (Dziobek et al., 2008), in which participants must infer mental states of characters (cognitive empathy) in a series of images as well as rate their own emotional reactions in response to the images (emotional empathy), the ASD group had difficulties with the cognitive empathy component, but not the emotional empathy component. When boys with ASD, boys with conduct disorder and matched controls were compared on a task measuring both cognitive and emotional empathy,
Schwenck et al. (2012) found that whilst boys with ASD demonstrated significant cognitive empathy difficulties compared with those with conduct disorder, they reported significantly more emotional affection than those with conduct disorder. This pattern was not, however, upheld in research conducted by Schulte-Rüther et al. (2011). Specifically, participants with
ASD were asked to indicate the emotional expression of the person in the image (other-task) and the emotion that the image provoked in them (self-task). Whilst response times and correct responses in the other-task did not differ between the ASD group and controls, individuals with ASD reported fewer episodes of emotional contagion during the self-task.
Based on these studies, which primarily used subjective and self-report ratings, it remains unclear to what degree individuals with ASD have normal or impaired emotional empathy processing.
Audio-visual emotional stimuli are dynamic and more “real world” requiring participants to engage multiple processes (visual and auditory). They may, therefore, be more 39
likely to trigger emotional engagement than static stimuli. In one of the few studies using
video stimuli, Rozga et al. (2013) asked adults with ASD and matched controls to view short videos depicting an actor saying a sentence in one of four emotional tones (fearful, angry, happy and neutral) as well as identify the actor’s facial expression. Whilst no differences were found between groups for behavioural responses (emotions were identified correctly), the authors found undifferentiated patterns of facial EMG responses in the group with ASD to the valence of emotional stimuli, that is, individuals with ASD responded equally with both zygomaticus and corrugator activation regardless of the perceived emotion.
Emotional empathy may also be measured using self-report questionnaires. In a recent study Rueda et al. (2015) found evidence to support a dissociation between cognitive and emotional empathy using the self-report IRI (Davis, 1983). They found that individuals with
ASD scored lower than controls on cognitive empathy subscales (perspective taking, fantasy), however, no significant differences were found on the emotional empathy subscales
(empathic concern, personal distress). This is not a universal finding, however. Reduced empathic concern and increased personal distress has also been reported (Dziobek et al.,
2008). Discrepancies in responses to self-report empathy measures (specifically emotional empathy) may reflect heterogeneity within this clinical population.
In sum, people with ASD display abnormal arousal and facial responses to static and audiovisual facial expressions but are possibly less impaired when viewing highly salient
images. This may suggest reduced emotional empathy but very few studies combine measurement of physiological responses with measurement of the subjective experience of the participant (Bons et al., 2013). Emotional empathy involves not only a physiological
reaction, but an understanding of that reaction (Bird & Viding, 2014). As far as the author is
aware, no research to date has examined the relationship between these two concepts: self- 40
report empathic response and physiological response or how these two concepts relate to
broader trait level empathy in ASD.
The Present Study
The following study aimed to explore emotional empathy in ASD by examining the relationship between psychophysiological (arousal and frown response) and psychological
(self-report) responses to short video clips depicting individuals in distressing situations. It
also examined the relationship between these performance-based measures and self-report
measures of trait empathy. Prior research yields mixed evidence regarding a deficit in
emotional empathy in ASD. On the presumption that adults with ASD do have problems with
emotional empathy it was hypothesised that, in comparison to a matched control group, the
ASD group would 1) report smaller changes in mood and arousal in response to emotional
video stimuli compared to controls; 2) demonstrate lower levels of physiological responding
compared to controls and 3) report lower emotional as well as cognitive trait empathy
compared to controls.
In Chapter 1, the possibility was raised that people with ASD may have normal
automatic emotional responses to emotional events, but lack self-awareness of them. In order
to test the hypothesis that people with ASD have intact self-awareness of empathic responses,
it was hypothesised that there would be a positive relationship between self-reported
emotional responses and physiological responses to the video clips. Further, it was
hypothesised that higher levels of self-reported trait empathy would be positively associated
with physiological responses to the emotional clips. Finally it was hypothesised that greater
impairment in empathy would be associated higher levels of ASD traits.
41
Method Participants
Data from twenty-five individuals from the total sample with ASD (mean age 28 years, age range 16-61, 4 females) and twenty-five non-clinical individuals from the control sample
(mean age 27 years, age range 18-50, 5 females) were used in this study. All missing data from both the ASD group and control group were due to technical complications with the psychophysiological measures.
Procedure
All participants completed this experiment as part of the larger project and had completed one separate experimental task prior to this task. Participants were requested to sit quietly and relax for several minutes until skin conductance levels had plateaued to a resting state. A 3- minute resting baseline period was recorded for all autonomic measures. Participants then viewed ten 30-second video clips depicting either emotionally distressing motor vehicle scenes or neutral information on motor vehicle safety. Each clip was followed by a visual rating scale for mood followed by arousal. Participants were requested to respond to the rating scales by pressing the number on the keyboard. Order was randomized as to whether participants viewed an emotional clip first or a neutral clip first. Another 3-minute resting baseline was recorded at the end of the task.
42
Materials and Measures
Questionnaires
The AQ, EQ and IRI were also included in data analysis and are described in detail in the
General Methods chapter (Chapter 2).
Psychophysiological Measures
Both Skin Conductance and Corrugator EMG data were analysed for this study. Details of these measures are outlined in the General Methods chapter.
Video Stimuli
Ten 30-second video clips (five emotional, five neutral) were presented in an alternating manner with the condition (emotional/neutral) of the first clip being randomized. The emotional clips were taken from various Australian road safety advertisements (3 clips) as well as automobile accident scenes from Australian television programs (2 clips). Neutral clips were taken from television documentaries on car safety and did not contain any traumatic or emotional scenes. Only the last ten seconds of each clip was used in the analysis.
This was to reduce the amount of noise (due to movement) in the data as well as targeting the emotional component of the scenarios (in the emotional clips).
43
Self-Assessment Manikin (SAM) (P. J. Lang, Greenwald, Bradley, & Hamm, 1993)
To assess each participant’s mood and arousal to each film clip, the computerized version of
the SAM was used. This instrument has high reliability for both mood and arousal (r = .94
and r = .93, respectively for 21 pictures) (P. Lang, Bradley, & Cuthbert, 1999). In this study,
participants first rated mood on a 9-point Likert scale from most negative (1), with a graphic
depicting a frowning, unhappy face, to most positive (9), with a graphic depicting a smiling,
happy figure. The same process was then completed for arousal from most calm (1), with
graphic depicting a manikin with eyes closed to most aroused or excited (9), with graphic
depicting a manikin with a large explosion graphic.
Data Reduction and Analyses
The mean integrated corrugator EMG and skin conductance level signals were derived for the
3-minute resting period as well as the last 10 s time-interval of each video clip presented. For both measures, change scores were computed by subtracting mean activity at baseline from that occurring in the last 10 s of each film. Both EMG and skin conductance change scores were logarithmically transformed to normalize the distribution of scores. Data from the five emotional clips and five non-emotional clips were averaged, giving two conditions, emotional versus neutral.
The data for all measures (SCL, CR, self-report ratings) were examined with a series of repeated-measures ANOVAs with group (ASD versus control) as the between-subjects factor and condition (emotional versus neutral clips) as the within-subjects factor.
Pearson’s correlation coefficients were used to examine bivariate associations between autonomic, self-report and questionnaire data. 44
Results Demographics
There were no significant differences between groups on age (t(1, 49) = .50, p = .623) or
gender ratio (χ2 = .092, p = .762). However, a significant difference was found between
groups for IQ with the ASD group having significantly higher IQ (t(1, 49) = 2.21, p = .05).
There were no significant correlations between IQ and any outcome measure
(psychophysiological measures or subjective ratings). Confirming diagnostic group, the ASD group was significantly higher on the AQ (t(1, 49) = 6.26, p < .001). Table 2.1 displays the
demographic and self-report questionnaire details.
Table 3.1. Participant demographic details and performance on self-report
questionnaires
ASD Control T statistic Significance
Mean Age (SD) 29 (13.8) 28 (9.2) .46 .65
Age Range 16-61 18-50
Males:Females 20:5 21:4
Mean IQ (SD) 116 (13.7) 108 (13.9) 2.21 .032
AQ 32.2 (8.54) 15.7 (6.54) 6.26 < .001
EQ 22.7 (10.37) 49.3 (12.99) 7.91 < .001
IRI_PT 13.3 (6.83) 19.9 (4.37) 4.09 < .001
IRI_EC 14.8 (6.21) 21.4 (3.58) 4.63 < .001
45
Self-reported trait empathy
The ASD group rated significantly lower than the control group on both Perspective Taking
(t(1, 49) = 3.93, p < .000) and Empathic Concern (t(1, 49) = 4.46, p <.001) subscales of the
IRI. The ASD group also scored significantly lower on the EQ compared to controls (t(1, 47)
= 7.76, p < .001)
Subjective ratings of mood and arousal (SAM)
Figures 3.1 and 3.2 demonstrate mean mood and arousal ratings respectively for the neutral and emotional videos.
Mood 8 7
6 9) - 5 4 ASD 3 Control
Mood Rating (1 Rating Mood 2 1 0 Emotional Neutral
Figure 3.1. Mean self-reported mood rating to neutral and emotional clips
46
Arousal 8 7
9) 6 - 5 4 ASD 3 Control
Arousal Rating (1 Rating Arousal 2 1 0 Emotional Neutral
Figure 3.2. Mean self-reported arousal ratings to neutral and emotional clips
There was a significant main effect of condition for mood ratings (F(1, 49) = 124.39, p <
.001, Pη2 = .761) with a large effect size, indicating lower mood ratings for the emotional
video clips than the neutral video clips. There was also a significant condition by group
interaction effect with moderate effect size (F(1, 49) = 5.99, p = .019, Pη2 = .133). T-tests
revealed a trend towards significantly lower mood ratings by the control group compared
with the ASD group for the emotional clips only (t(1, 49) = 1.765, p = .085). There were no
main effects of group.
There was a significant main effect of condition for arousal ratings with large effect size (F(1,
49) = 114.62, p < .001, Pη2 = .741), indicating higher arousal ratings for the emotional video clips compared with the neutral video clips. No interaction or group effects were found.
47
Skin conductance
Skin conductance level was averaged across all five video clips in each condition (emotional
and neutral). Average levels for emotional and neutral clips for the two groups are depicted
in Figure 3.3.
Skin Conductance Level 0.64
0.62
0.6
0.58 ASD Control 0.56
0.54 Log SCL change from Baseline
0.52 Neutral Emotional
Figure 3.3. Mean skin conductance level to neutral and emotional video clips
There were no significant main effects or interaction effects for average skin conductance
level, indicating similar levels of arousal and emotional response to both the emotional clips
and neutral clips in the ASD group and the control group.
A paired samples t-test was run with the clips individually. Only two of the emotional clips were significantly different from their paired neutral clips (Clip 1 pair: t(1, 49) = 2.649, p =
.011; Clip 2 pair: t(1, 49) = 1.112, p = .272; Clip 3 pair: t(1, 49) = 2.279, p = .027; Clip 4 pair: t(1, 49) = .917, p = .363; Clip 5 pair: t(1, 49) = .997, p = .324). Figures 3.4 and 3.5 48 depict mean SCL for each of the five clips for both emotional and neutral, respectively, in which it can be seen that SCL to Clips 1 and 3 are significantly higher in the emotional condition than in the neutral condition.
Emotional Clips 0.7 0.68 0.66 0.64 0.62 0.6 Control
Basleine 0.58 ASD 0.56 0.54 0.52 Log Mean SCL difference from from difference SCL Mean Log 0.5 1 2 3 4 5
Figure 3.4. Comparison of ASD and control SCL for each emotional clip
Neutral Clips 0.7 0.68 0.66 0.64 0.62 0.6 Control
Baseline 0.58 ASD 0.56 0.54 0.52 Log Mean SCL difference from from difference SCL Mean Log 0.5 1 2 3 4 5
Figure 3.5. Comparison of ASD and control SCL for each neutral clip
49
Corrugator EMG
As with skin conductance, corrugator responses were averaged across all five video clips in
each condition. Average corrugator responses for emotional and neutral clips for the two groups are depicted in Figure 3.6.
Mean Corrugator Response
0.52 0.5 0.48 0.46 0.44 0.42 0.4 0.38 0.36 Neutral Emotional
ASD Control Log Corrugator Response Minus Baseline
Figure 3.6. Mean corrugator response to neutral and emotional video clips
There were no significant main effects or interaction effects for corrugator response.
However, a trend was found for condition (emotional versus neutral) suggesting greater
corrugator response to emotional clips compared to neutral clips in both groups (F(1, 48) =
3.66, p = .062). 50
Correlations
Correlational analyses are detailed in Table 3.2.
Table 3.2. Relationship between self-report and physiological response and trait
empathy across the combined group (ASD and Control)
IRI_PT IRI_EC EQ AQ S-R S-R SCL COR Mood Arousal IRI_PT - IRI_EC .510** - EQ .527** .634** - AQ -.510** -.422** -.757** - S-R -.010 -.213 -.300 .257 - Mood S-R .082 .219 .157 -.060 -.149 - Arousal SCL -.013 .067 .109 .011 -.352* .304 - COR -.135 -.118 .112 -.185 -.026 -.048 -.127 - * p<.05; ** p<.01 (PT: IRI Perspective Taking; EC: IRI Empathic Concern; EQ: Empathy Quotient; AQ: Autism Quotient; S-R Arousal: Self-report arousal ratings for emotional videos; S-R Mood: Self-report mood rating for emotional videos; SCL: Skin conductance level for emotional videos; COR: Corrugator response to emotional videos.
The correlational matrix revealed that self-reported measures of empathy inter-correlated, as expected and also correlated with AQ scores. Self-reported mood and arousal to the video clips did not correlate, nor did the two physiological measures. Correlations within measures of responses to the video clips and between these and empathy measures were examined to test hypotheses. These revealed that those who reported more negative mood following the emotional clips also responded with greater levels of arousal (r = -.352, p = .024). Further, there was a trend towards significance in the relationship between self-reported arousal and 51
skin conductance response (r = .304, p = .053), in that those who reported greater levels of
arousal following the emotional video clips also demonstrated higher levels of physiological
arousal. No relationships were found for self-report measures and the corrugator response. A trend towards a significant negative relationship was found between self-reported mood and the EQ (r = -.300, p = .060), suggesting that those who reported more negative mood to emotional clips reported greater trait empathy. No relation with the IRI emerged. There was also no association with the AQ.
Table 3.3. Relationship between self-report and physiological response and trait empathy – ASD Group
IRI_PT IRI_EC EQ AQ S-R S-R SCL COR Mood Arousal IRI_PT - IRI_EC .364 - EQ .437* .570** - AQ -.390 -.174 -.680** - S-R .190 -.004 -.151 .113 - Mood S-R .251 .427 .588* -.272 .860 - Arousal SCL .106 .001 -.012 .161 -.396 .263 - COR -.281 -.406* -.158 -.149 .183 .064 -.003 - * p<.05; ** p<.01 (PT: IRI Perspective Taking; EC: IRI Empathic Concern; EQ: Empathy Quotient; AQ: Autism Quotient; S-R Arousal: Self-report arousal ratings for emotional videos; S-R Mood: Self-report mood rating for emotional videos; SCL: Skin conductance level for emotional videos; COR: Corrugator response to emotional videos.
52
Table 3.4. Relationship between self-report and physiological response and trait
empathy – Control group
IRI_PT IRI_EC EQ AQ S-R S-R SCL COR Mood Arousal IRI_PT - IRI_EC .232 - EQ .114 .255 - AQ .033 .180 -.393 - S-R .221 -.088 -.142 -.296 - Mood S-R .003 .252 .087 .216 -.346 - Arousal SCL -.197 .206 .276 -.198 -.389 .333 - COR -.326 -.066 -.020 .222 -.198 -.128 -.266 - * p<.05; ** p<.01 (PT: IRI Perspective Taking; EC: IRI Empathic Concern; EQ: Empathy Quotient; AQ: Autism Quotient; S-R Arousal: Self-report arousal ratings for emotional videos; S-R Mood: Self-report mood rating for emotional videos; SCL: Skin conductance level for emotional videos; COR: Corrugator response to emotional videos.
Correlations were also performed within each group separately to determine relationships between self-reported empathy and physiological responses. Within the ASD group, the EQ was positively associated with self-report arousal to the emotional videos (r = .588, p = .010),
indicating those who rated themselves as having greater levels of trait empathy reported
greater levels of arousal following the emotional video scenes. Surprisingly, the IRI-EC was negatively associated with corrugator response (r = -.406, p = .044), indicating that those with ASD who rated themselves as having lower levels of empathic concern responded with greater activation of the corrugator (frown) muscle. No significant relationships were found within the control group.
53
Discussion
This study aimed to examine whether adults with ASD have lower emotional empathy
than a matched control group as indexed on self-report and in their physiological responses to
video clips depicting emotionally distressing scenes. In addition, we examined the relationship between physiological responses and self-reported responses and the relation between empathic responses to the videos and self-reported trait empathy.
The ASD group reported reduced or flattened responses to the clips. That is, relative to the control group, they reported slightly less negative mood to the emotional clips than controls. Interestingly, the ASD group did not differ from the control group in their ratings of perceived arousal following the emotional clips, suggesting that individuals with ASD perceive their physiological responses to emotional stimuli to be as intense as do control participants.
Furthermore, despite differences in self-rated mood, the participants with ASD responded with similar levels of both arousal and facial affect to controls. These findings indicate that individuals with ASD were experiencing normal physiological arousal responses to emotionally salient stimuli. Our finding of normal arousal to emotional videos is consistent with results found by Mathersul et al. (2013a), demonstrating normal arousal response to their emotionally salient stimuli. Also consistent, Rozga et al. (2013) found no difference in intensity of EMG response between those with ASD and controls, although those with ASD did not differentiate their facial responses to positive versus negative emotions. Together, this indicates that individuals with ASD do respond physiologically to emotional stimuli. Whilst they are able to identify this response, i.e. self-rated arousal was normal, their flattened mood ratings suggest they had difficulty interpreting the emotional salience of the physiological response. Individuals with ASD have been shown to have difficulty interpreting, describing 54
and reporting their emotions a condition known as alexithymia (Zech, Luminet, Rime, &
Wagner, 1999) which may account for this area of difficulty. Indeed, a number of studies
have demonstrated a link between alexithymic traits and poor empathy independent of
autistic traits (Aaron, Benson, & Park, 2015; Bird et al., 2010).
The ASD group scored significantly lower on the EQ and on both the cognitive
empathy (Perspective Taking) and emotional empathy (Empathic Concern) subscales of the
IRI. These results are consistent with prior research in the ASD population using these measures (Baron-Cohen & Wheelwright, 2004; Rogers et al., 2007). Although we failed to find any relationships between subscales of the IRI and any self-report responses or physiological responses to the video stimuli in the combined group, there was a trend to suggest that high levels of self-reported empathy on the EQ were associated with more negative mood following the emotional video clips, suggesting greater levels of psychological distress to negative emotional stimuli. Those reporting more negative mood to these clips also demonstrated greater physiological arousal when watching the clips. When the groups were examined separately, the relationship between trait empathy and self- reported arousal was maintained in the ASD group although not controls. No relationship was found between self-report measures and physiological arousal in either group, possibly reflecting reduced power. Interestingly, in the ASD group low empathy on the IRI-EC was associated with heightened brow (corrugator) activity during the emotional videos. This may imply a certain puzzlement at the emotional videos in those who have low levels of empathic understanding.
Taken together, it appears that general trait empathy is associated with greater emotional responding to emotional stimuli, both subjectively and physiologically and that this holds in people with ASD. This suggests that, in this study at least, the people with ASD were able to subjectively appraise their responses to emotional videos accurately, i.e. there 55 was a degree of self-awareness. However, the results also suggest a general dampening of conscious emotional responding in individuals with ASD, in response to audiovisual stimuli which suggests difficulty interpreting the emotional salience of their physiological response.
On a final note, differences in correlations emerged between the EQ and the IRI subscales. One possible reason for these is the nature in which they were developed: the EQ being developed in the field of autism research and the IRI developed in the broader field of social psychology.
Limitations and Conclusions
The ASD group in this study had significantly higher IQ than our control group.
Whilst this may be problematic in theory of mind or cognitive empathy tasks, it is unlikely to have an effect on emotional empathy tasks such as ours. Indeed, Schwenck et al. (2014) found that only cognitive empathy, and not emotional empathy, was influenced by IQ in a study in children. Further, IQ was not correlated with any of the dependent variables in this study.
We failed to find main effects for condition in both the psychophysiological measures, indicating similar levels of arousal and negative facial expression to emotional and neutral clips. The video clips used in this study were based on television advertisements for road traffic safety as well as television programs involving motor vehicle accident scenes. By using these videos as more realistic stimuli, it was hoped that physiological and psychological responses would be more generalizable to real world events and situations. However, these types of videos had not previously been used in empathy research and the range of responses from each of the videos varied. Some of the emotional clips were more distressing or emotionally salient than others. As averages across the five clips were used for comparisons, 56 this may have weakened the effects. Two of the clips were particularly salient and effective in producing skin conductance responses, however, these responses still appeared equal between groups. These two clips, along with one other, primarily evoked the emotion of fear, whereas the remaining two clips primarily evoked sadness, however these responses were not examined specifically. As anxiety or fear produces a greater physiological response than sadness, our results may reflect these differences in physiological responding to differing emotions (Balconi et al., 2014). Future research may want to focus on one specific emotion, or use a greater number of trials in order to determine differences between emotions. Further, it would be helpful to pilot the clips so that the clips could be validated for emotional salience.
Another possible limitation regarding the video clips was the amount of social stimuli in each of the clips. The emotional clips depicted more social information and interactions between people than the neutral clips. However, this does not appear to have unduly affected the results given the comparable arousal and negative facial expression responses between groups. Thus, while ASD individuals have been shown to engage with social information to a lesser degree than controls this was not the case in our study.
Overall, this study suggests that emotional empathy in people with ASD may be abnormal due to a complex interplay between the physiological response typically triggered by socially distressing information and the ability to explicitly understand that emotional response. In this study it appeared that their physiological response was generally unremarkable, but their interpretation of this was muted compared to people without ASD.
Further, this specific failure to interpret feelings in the face of distressing images was associated with a more general lack of trait empathy as reported by the individuals themselves. 57
The stimuli used in this study were quite social in nature depicting two or three people in a social interaction. In all of the scenes, there was verbal and non-verbal information which required processing in order to understand what was happening in the scene, and subsequently respond empathically. Therefore, each of the stimuli required a high level of cognitive processing that, it could be argued, required a level of cognitive empathy as well as affective. This may have influenced the ASD participants’ ability to interpret and understand their own responses. Study 2, therefore, employs a paradigm which requires little cognitive load and processing, the “empathy for pain” paradigm. As participants are exposed to brief emotion-inducing stimuli, with no verbal information or social interaction, emotional empathy response can be measured without interference from social or verbal cognitive processing requirements.
58
CHAPTER 4
STUDY 2: Empathy for Pain in ASD
As discussed in Chapter 1, within emotional empathy, there are varying levels of
emotional resonance, from bottom-up automatic processing, such as emotional contagion or
mimicry, to more top-down subjective experience of emotions. In Study 1 it was found that
physiological responses in adults with ASD were essentially normal but that subjective
cognitive appraisal of their responses to emotional material was muted. This suggests that it
is the top-down processes that may be affected. However, that study used complex material with verbal content that may have also engaged theory of mind and cognitive empathic processes, known to be impaired in ASD.
An alternative way to examine the different processes engaged in emotional empathy is to examine empathy for pain. Empathy for pain has been shown to involve physiological, emotional and cognitive processes. Seeing another person in pain has been found to trigger automatic sensorimotor mimicry responses (Avenanti et al., 2005) and automatic neural responses in the observer (Singer et al., 2004) as well as emotional responses such as fear and alarm for both one’s own personal safety and empathic concern and sympathy for the other person (Goubert, Craig, & Buysse, 2011).
Functional MRI studies have demonstrated that imagining oneself and imagining another person in painful situations are both associated with activation of the pain-related neural network, i.e. a group of neural structures that respond to transient nociceptive stimuli.
These include the anterior insula, anterior cingulate cortex (ACC), brainstem and cerebellum.
There is, however, only partial overlap between the self and other representations (Jackson et 59
al., 2005) with activation of the ACC and anterior insula being common to both (Jackson,
Rainville, & Decety, 2006). It may be that the distinction between self- and other-pain neural representation may be what allows individuals to distinguish between the two (Jackson,
Brunet, Meltzoff, & Decety, 2006). The ACC and insula are associated with the affective dimension of the pain experience (Singer et al., 2004) suggesting that it is this dimension of pain perception that is relevant to empathy. However, sensorimotor studies using transcranial magnetic stimulation (TMS) have shown a consistent reduction of excitability of hand muscles during observation of a painful stimulus being delivered to a model (Avenanti et al.,
2005; Aventanti et al., 2006). These studies suggest that it is not just the affective component of the shared representation of the pain that is triggered, but rather the activation of a “pain resonance system” which maps onto both motor and affective systems within the brain
(Avenanti et al., 2005).
Studies have also demonstrated that pain detection in others and vicarious resonance of pain in an observed other is influenced by the observer’s empathic traits (Vachon-Presseau et al., 2011; Yamada & Decety, 2009). Further, empathy-related activity in the affective component of the pain matrix correlates with scores on trait emotional empathy scores
(Bufalari & Ionta, 2013b).
Research into empathy for pain in adults with ASD has produced mixed findings. At the level of automatic mimicry, Fan et al. (2014) demonstrated typical sensorimotor resonance, as measured by activation of the primary sensory cortex (SI) and secondary sensory cortex (SII), in individuals with ASD when viewing pictures of various people in painful situations. However, using a TMS sensorimotor paradigm, Minio-Paluello et al.
(2009) found that when observing pain affecting another person, individuals with ASD did not demonstrate any sensorimotor response in their corticospinal system. The reason for this is uncertain and it may be due to differences in measurement of sensorimotor responses, with 60
Fan et al. using activation of brain regions and Minio-Palluello et al. measuring muscle response, which is more distal. One advantage of Minio-Paluello’s study is the use of dynamic video stimuli, which is both more engaging and more realistic and generalisable to real life.
In other studies, there is suggestion that top down processes that regulate the affective responses to another’s pain may be abnormal in ASD. Specifically, a significantly greater
(heightened) affective arousal response was reported in adults with ASD compared to control participants, as measured by event-related potentials (ERPs), when actively making a pain judgment. Similarly, using ERPs to measure responses when viewing someone being injured, individuals with ASD showed a heightened response in the early component, indicating heightened affective arousal (Chen et al., 2016). Both studies suggested abnormal and heightened arousal responses to pain. Further suggestion that top down processes may be affected arises from Chen et al.’s (2016) study in which late components of the ERP response to observing pain were diminished. This was taken to indicate reduced cognitive processing.
Unfortunately, neither this study nor the others discussed behavioural or self-report measures to provide independent assessment of the cognitive appraisal of emotional empathy for pain.
In general, very few studies have combined objective physiological measures of empathy to pain with subjective self-report in ASD or other populations.
The Present Study
The purpose of the present study was to employ a previously established empathy for pain paradigm that uses real-life dynamic stimuli (Minio-Paluello et al., 2009) to examine and compare sensorimotor, affective and cognitive empathy for pain in individuals with ASD using both objective physiological measures (skin conductance, heart rate and 61
electromyography) and self-report responses. Based on the literature, along with the findings
of Study 1 it was anticipated that people with ASD would have difficulties with affective
empathy responses, mainly as a result of a disruption to high level processes enabling them to
cognitively appraise and thereby regulate their responses to another’s pain. As such, it was
expected that adults with ASD may experience normal low level mimicry of a pain response
along with normal or even elevated arousal responses to another person’s pain and impaired
cognitive processing of that experience. Four hypotheses were proposed. Relative to control
participants, adults with ASD would: (1) show a normal sensorimotor response to pain
stimuli, as indexed by electromyography (EMG) response in the hand muscle when viewing
painful stimulation in the corresponding muscle of the observed person and (2) show normal
or possibly elevated emotion contagion when viewing painful stimuli, as measured by
increased skin conductance, heart rate responses and frown response (EMG); (3) show
reduced emotional empathy for pain, as measured by self-reported ratings of distress, arousal
and judgments of pain. Further it was hypothesized that (4) low levels of trait emotional
empathy would be associated with abnormal physiological responses and reduced self-report
empathic responses to pain stimuli.
Method
Participants
A smaller sample from the total group of participants was used in the present study. Twenty
high-functioning individuals with ASD (mean age 29 years, age range 16-60, 3 females) and
25 non-clinical control individuals (mean age 28 years, age range 18-50, 5 females) participated in this study.
62
Procedure
All participants completed this task as part of a larger project and had completed two separate
experimental tasks prior to this task. Participants were requested to sit quietly and relax for
several minutes until skin conductance levels had plateaued to a resting state. A 3-minute
resting baseline period was recorded for all autonomic measures before commencing this
experiment. Participants then watched each of the four video clips (pain, touch, apple, static)
and rated five questions (taken from Minio-Paluello et al. 2009) from 1 to 9 on a Likert scale
(1: not at all; 9: very much so). The questions were: 1) How excited or aroused did you feel
watching the video?; 2) How much did the video upset you?; 3) How much were you able to
identify with the model in the video?; 4) How intense do you consider the pain in the video?;
and 5) How unpleasant do you consider the pain in the video? Following subjective ratings,
participants then commenced viewing the same videos in 4 blocks of 18 trials. Participants
were asked to identify with the model in the video and imagine what he/she was feeling. Each
clip was presented for three seconds followed by a six second post stimuli phase (black
screen). Videos were presented in a randomized order within each trial. A 10 second
between-trial break was implemented. Another 3-minute resting baseline was recorded at the
end of the task.
Materials and Measures
Questionnaires
The AQ, EQ and IRI were also included in data analysis and are described in detail in the
General Methods chapter (Chapter 2).
63
Psychophysiological Measures
Both Skin Conductance and Corrugator EMG data were analysed for this study. Details of
these measures are outlined in the General Methods chapter.
Four participants’ corrugator and hand EMG data were excluded due to recording difficulties,
three from the control group and one from the ASD group.
Visual Pain Stimuli
Four videos were presented to participants on a 19-inch screen. This study used the same four
video conditions as Minio-Paluello et al., but were refilmed for the purpose of this study.
Each video went for 3000 ms instead of 1800 ms. This slightly longer video was used in
order to measure both skin conductance levels and heart rate, both of which require a longer
response time. The four conditions were 1) “pain”, in which a hypodermic needle penetrated
the first dorsal interosseous (FDI) muscle of the right hand; 2) “touch”, in which a cotton
swab touched the FDI region of the right hand; 3) “apple”, in which an apple was penetrated
by a needle and 4) “static”, a still right hand.
Data Reduction and Analyses
The mean integrated corrugator and FDI muscle of the hand and corrugator EMG, skin
conductance level and heart rate signals were derived for the 3-minute resting period as well
as the three-second video clip plus six-second (total of 9 seconds) post stimuli period for each video clip presented. The psychophysiological measures examined in this study have slow onset and recovery times, for example, skin conductance takes 1-3 seconds to rise and up to 9 s to peak and recover. Accordingly, the average physiological response was taken to ensure 64 captured the whole response across each trial. For all measures, change scores were computed by subtracting mean activity at baseline from that occurring in the three second clip plus post stimuli phase. For all psychophysiological measures (skin conductance, heart rate and EMG) an average score was taken over the 9-second stimulus trial.
Analyses examined the difference in scores/data in the Pain condition relative to the Touch,
Apple, and Static conditions (i.e., Pain-Touch, Pain-Apple, Pain-Static difference scores).
Hypotheses 1-3 were assessed using separate Repeated Measures MANOVAs for each dependent variable (physiological measures and subjective ratings). MANOVAs have been shown to be superior to ANOVAs in psychophysiological research due the high correlation amongst psychophysiological measures, which violates the assumption of sphericity in
ANOVA (Vasey & Thayer, 1987). Condition (Touch, Apple, Static) was the within-subjects factor and Group (Control, ASD) was the between-subjects factor. The response to a painful jab to the hand with a hypodermic needle was examined in relation to two types of control stimuli using planned polynomial contrasts. Linear comparisons examined responses relative to other hand images (touching the hand or simply a hand) (Touch vs. Static). Quadratic contrasts examined responses relative to the same hypodermic in an apple, versus hands with no hypodermic (Apple vs. Touch/Static mean). For Hypothesis 4, two-tailed Pearson’s correlation coefficients were used to examine bivariate associations between self-reported trait empathy scores and those physiological and subjective responses to pain that were found to differ significantly between groups2.
2 It should be noted that a correlational analysis on all the variables was conducted in order to check that we were not missing any important associations. No variables other than those that were impaired between groups demonstrated a significant correlation before or after Bonferroni correction 65
Results
A summary of demographic and questionnaire data is presented in Table 4.1. The ASD group
scored lower than controls on both measures of trait empathy (IRI-PT and IRI-EC).
Confirming diagnosis, the ASD scored significantly higher on the AQ (F(1, 42) = 27.46, p <
.001).
Table 4.1. Demographic and Questionnaire Information
ASD Control F statistic Significance (N=20) (N=25) Mean Age (SD) 29.15 28.32 .06 .814
Age Range 16-60 17-50
Males:Females 15:5 19:6
Mean IQ (SD) 116.55 (14.96) 107.48 (15.32) 3.98 .053
AQ 35.7 (11.05) 16.88 (6.15) 27.46 < .001
IRI_PT 12.50 (7.16) 20.32 (3.97) 23.12 < .001
IRI_EC 14.10 (5.40) 20.80 (3.95) 27.46 < .001
66
Physiological Measures
EMG - Hand Muscle
Figure 4.1 illustrates the difference in mean EMG hand muscle response to stimuli involving
pain in the corresponding muscle of the observed person relative to control stimuli (Touch,
Apple, Static). The Control group response was greater for pain than each of the control
stimulus conditions as indicated by positive difference score, while the EMG response of the
ASD group was greater for pain stimuli only in relation to the Apple condition. The small
scale should be noted. Statistically however, there was no effect of Group (F(1, 40) = 0.96, p
2 2 = .332, ηp = .02) or Condition (F(1, 40) ≤ 2.17, p ≥ .149, ηp ≤ .05) and no Group ×
2 Condition interactions (F(1, 40) ≤ 0.42, p ≥ .521, ηp ≤ .01).
0.20 Touch Apple Static
) μV/S
Control ASD Relative EMG pain response ( response pain EMG Relative
-0.10
Figure 4.1. Difference in EMG hand response for pain relative to non-pain (Touch,
Apple, Static) stimuli for Control and ASD participant groups. Error bars indicate standard error. 67
Skin Conductance
Figure 4.2 displays the mean difference in skin conductance level for pain relative to non- pain stimuli. Across the three conditions, Figure 2 indicates that the Control group showed greater pain responses relative to the ASD group, resulting in a main effect of Group with
2 moderate effect size (Control > ASD: F(1, 43) = 5.01, p = .030, ηp = .10). Group and
2 Condition did not interact (F(1, 43) ≤ 0.28, p ≥ .597, ηp < .01). Across the groups, a linear trend in Condition with moderate effect size indicated that the relative pain response was significantly greater for the Touch condition compared with the Static condition (Touch >
2 Static: F(1, 43) = 4.98, p = .031, ηp = .10).
0.50 Touch Apple Static
) μS
Relative SCLPain to stimuli ( Control ASD -0.10
Figure 4.2. Difference in skin conductance level to pain stimuli relative to non-pain
(Touch, Apple, Static) stimuli. Error bars indicate standard error.
68
Heart Rate
Figure 4.3 shows the mean difference in heart rate for pain relative to non-pain stimuli. No
2 main effects were found for Group (F(1, 40) = 0.32, p = .572, ηp = .007). Across the groups, a quadratic trend in Condition with moderate effect size indicated that the relative pain response was significantly greater for the Touch and Static conditions compared with the
2 Apple condition (Touch + Static > Apple: F(1, 43) = 3.73, p = .060, ηp = .080). No Group x
Condition interaction was found. However, as indicated in Figure 4.3, although not significant, the control group showed a greater response to pain stimuli relative to the Apple and Static conditions, but not the Touch condition, whereas the ASD group did not show greater heart rate response to pain stimuli relative to any of the other non-pain condition.
1.90 Touch Apple Static
1.40
0.90
0.40
-0.10 Control ASD Relative HR to Pain Stimuli (bpm) Stimuli to Pain HR Relative -0.60
Figure 4.3. Difference in heart rate to pain stimuli relative to non-pain (Touch, Apple,
Static) stimuli. Error bars indicate standard error
69
EMG Corrugator
Difference in Corrugator EMG response to pain stimuli relative to non-pain (Touch, Apple,
Static) stimuli are depicted in Figure 4.4. The ASD group response was greater for pain than
each of the non-pain stimulus conditions as indicated by positive difference score, while the
EMG response in the corrugator muscle of the control group was greater for pain stimuli only
in relation to the Static condition. Statistically however, there was no effect of Condition
2 2 (F(1, 40) ≤ 0.47, p ≥ .499, ηp ≤ .01) or Group (F(1, 40) = 1.31, p = .260, ηp = .03), and no
2 Group × Condition interactions (F(1, 40) ≤ 0.78, p ≥ .383, ηp ≤ .02).
Touch Apple Static
0.30
0.10 Stimuli (μV/S)
Relative EMG Relative corrugator to Pain Control ASD -0.10
Figure 4.4. Difference in Corrugator EMG response to pain stimuli relative to non-pain
(Touch, Apple, Static) stimuli. Error bars indicate standard error
Self-Reported Pain Ratings
Self-report questions were taken from a smaller sample of both groups, approximately two thirds, due to missing data (ASD: N = 15; Control: N = 16). The smaller groups were comparable to the larger groups and differences between ASD and control groups were 70
matched to the larger group (Age: t(1, 29) = .535, p = .597; IQ: t(1, 29) = 1.747, p = .091;
AQ: t(1, 29) = 4.769, p < .001; IRI_PT: t(1, 29) = 4.356, p < .001; IRI_EC: t(1, 29) = 5.106, p
< .001). Similar to analyses of physiological responses, pain relative to control conditions
were analysed using MANOVAs. No Group main effects were found for Questions 1-3 (how
excited/aroused did you feel?; how upset did you feel?; how well could you identify with the
model?). For the remaining two questions, across the three conditions, the Control group
showed greater pain responses relative to the ASD group, resulting in a main effect of Group
2 for both the intensity of the pain (Control > ASD: F(1, 29) = 6.89, p = .014, ηp = .192) and
2 the unpleasantness of the pain (Control > ASD: F(1, 29) = 5.80, p = .023, ηp =. 167), both
with large effects sizes. Relative change scores for Questions 4 and 5 are depicted in Figures
4.5 and 4.6 respectively.
7.00 Touch Apple Static
6.00
5.00
4.00
3.00
2.00 Relative Intensity of Pain Rating Pain of Intensity Relative
1.00 Control ASD
Figure 4.5. Difference in pain intensity ratings to pain stimuli relative to non-pain
(Touch, Apple, Static) stimuli. Error bars indicate standard error
71
Touch Apple Static
7.00
5.00
3.00 Relative Unpleasantness of Pain of Unpleasantness Relative
1.00 Control ASD
Figure 4.6. Difference in pain unpleasantness ratings to pain stimuli relative to non-pain
(Touch, Apple, Static) stimuli. Error bars indicate standard error
Relationships between Arousal Levels, Trait Empathy and self-reported pain ratings
Bivariate correlations were performed using pain compared to control (average of three
control condition) difference scores for skin conductance, Question 4, Question 5 Pain-Apple
difference scores and trait empathy questionnaires (IRI-PT, IRI-EC) as these measured showed significant group differences. Correlations are displayed in Table 4.2.
72
Table 4.2. Correlations between skin conductance, self-report responses and IRI
subscales
Perspective Taking Empathic Concern
Q4 Pain-Apple .483* .484*
Q5 Pain-Apple .382* .394*
SCL Pain-Control .154 .053
Both the Perspective Taking (PT; r = .483, p = .006) and the Empathic Concern (EC; r =
.484, p = .006) subscales were positively associated with Pain-Apple difference scores for
Question 4 (intensity of pain). Also, both PT (r = .382, p = .034) and EC (r = .394, p = .028)
subscales were positively associated with Pain-Apple difference scores for Question 5
(unpleasantness of pain). Pain-Control difference scores for skin conductance were not
associated with any self-report pain ratings or trait empathy.
Discussion
Empathy for pain involves automatic processes such as sensorimotor responses and
emotional contagion as well as higher-level emotional responses and cognitive appraisal. This study aimed to examine both automatic responses as well as psychological self-reported empathy for pain in adults with ASD using a previously tested empathy for pain paradigm.
Further, the relationship between empathy for pain in an experimental task and trait-level empathy was explored. 73
At the trait level, the ASD sample in this study rated themselves as having lower
levels of both Perspective Taking and Empathic Concern, as measured by the IRI. This
replicates at least one study (Demurie, De Corel, & Roeyers, 2011) but differs from others
that have found similar ratings between groups on the Empathic Concern subscale (Rueda et
al., 2015).
At the most basic and automatic level there was little evidence that a sensorimotor
response in the FDI muscle was evoked by the pain stimuli relative to control stimuli in either
the controls or the ASD group. This contradicted the findings of Minio-Paluello et al. (2009) using the same task (with slight variation of presentation time and order). The divergence between these results and Minio-Paleullo’s suggest that this effect is not reliable. As the two studies used similar sample sizes this is unlikely to account for the discrepancy between studies. Minio-Paluello’s study employed a slightly different measurement of sensorimotor response to the current study in that they used TMS to evoke a motor action potential in the muscle of the hand, whereas the current study employed EMG, without TMS activation, to measure muscle activation. It may be the case that EMG without TMS was not a sensitive enough measure to record a response in the FDI muscle and that investigations of this sensorimotor response do require enhancement of the response, such as by using TMS to examine group differences.
Analyses of autonomic measures revealed significant group differences in skin conductance level between pain relative to control video clips, with the ASD group responding less to pain compared with controls. Even so, both group responded similarly across control conditions. Both groups demonstrated the largest change in arousal in response to the video depicting a painful experience compared to a non-painful experience. The difference score was less when the pain video was compared to observing a hand being touched, suggesting that simply touching a hand caused some small increase in arousal also 74
(thus reducing the pain-control difference). The group differences indicate an apparent flattening or blunting of emotional contagion to pain in individuals with ASD. Group differences in skin conductance were not reflected in the heart rate responses though, suggesting that skin conductance is a more sensitive measure of arousal. Indeed similar studies have demonstrated group differences in skin conductance responses and levels in the absence of heart rate response differences (Mathersul et al., 2013b, 2013d). Despite the absence of group effects, there were differences in heart rate across all participants such that a painful jab to the hand with a hypodermic needle created greater heart rate change compared to viewing hands without pain. Heart rate responses were less differentiated when watching a hypodermic in a hand versus an apple.
Activation of the corrugator muscle, as an index of frown response, revealed no significant differences between groups for pain to non-pain comparisons. However, as can be seen in Figure 4, there was a pattern to suggest that the ASD group produced greater frown activity to pain than no-pain, whereas the control group did not. Given this response is in the opposite direction to the relative blunting of other physiological responses in the ASD group, one could speculate that this reflected enhanced puzzlement, or interest in the pain stimuli rather than an empathic response. However, in the absence of significant effects no conclusions can be drawn. Overall, the results of the psychophysiological measures suggest that the group with ASD were similar to controls generally, but with some dampening of arousal in response to observing pain in another.
The ASD group and the control group differed in terms of their subjective experience when observing pain stimuli compared with non-pain stimuli. Although no differences were found between groups for the first three questions, according to Questions 4 and 5, the ASD group rated the intensity and unpleasantness of pain as lower than controls. While these findings are in line with some previous research indicating reduced sensitivity to pain in 75
individuals with ASD (Vivanti, D'Ambrogio, & Zappella, 2006), other studies have found
higher subjective pain (i.e. reduced thresholds for pain sensitivity) in this same group (Chen
et al., 2016).
One way of interpreting these self-report differences is that those with ASD in the
current study were responding with lower ratings on questions reflecting judgments about
another (intensity and unpleasantness of the pain in the other), whereas other studies that have compared ratings between the ASD group and controls were based around questions involving judgments on self (level of distress and arousal in oneself). Thus, it appears that, on subjective report, the group with ASD shows reduced empathy for another’s pain despite other studies showing them to be sensitive to their own. It might be argued that this reduction in empathy for the other was due to a dampening of mimicry and emotional contagion.
However, despite finding that our group with ASD had reduced arousal (SCL) there was no relationship between this and self-report ratings of pain or trait empathy. Such a relationship has, however, been found by others. For example, Avenanti et al. (2005) found a significant reduction in amplitude of motor-evoked potentials (MEPs) specific to the muscle that the participants observed being pricked by a needle and that this inhibition correlated with the observer’s subjective rating of the of the sensory qualities of the pain as well as measures of state sensory empathy scores.
The reason for the lack of relation between arousal and subjective ratings in this study is not entirely clear. One explanation is that the group with ASD had lowered subjective responses to another’s pain due to the presence of alexithymia, a trait-like condition characterized by reduced ability to experience or describe feelings, occurs at increased levels in the ASD population (Hill, Berthoz, & Frith, 2004). Levels of alexithymia are also variable in this population and a number of studies have demonstrated that it is alexithymic traits and not autistic symptoms that are associated with impaired empathy (Bernhardt et al., 2014; Bird 76
et al., 2010). We cannot know whether alexithymia was influencing responses in this study
as this was not directly measured. However, given the findings that people with ASD in other
studies (e.g. Chen et al, 2016) self-report normal to high levels of pain in self, this
explanation does not seem likely. Further, it has been demonstrated that individuals high in
alexithymia have difficulty separating themselves from others (Moriguchi et al., 2007).
However, this was not evidenced by the current study. Individuals with ASD rated less
intensity and unpleasantness of pain in others, but not in themselves indicating separation of
self and other.
An alternative explanation is that ASD is not a unitary disorder and therefore the
participants in the ASD group may have differed in their arousal responses. Some evidence
for this comes from a study by Mathersul et al. (2013c) who found that within a group of
ASD, some had low resting arousal while others had normal to high resting arousal. Further,
these two groups differed in their levels of self-reported trait empathy. Thus, the relationship between arousal, both base level and responsivity, and empathy may differ within people with ASD. This may also be the case for alexithymic traits, with some individuals with ASD experiencing higher levels of alexithymia than others. Heterogeneity of alexithymic traits within the current sample of participants with ASD as well as the general population of people with ASD may account for the variability in baseline arousal and emotional responses.
Ratings of the intensity of the pain (Question 4) in the pain clips relative to the control clips (mean of Touch, Apple, Static) and the unpleasantness of the pain (Question 5) in the pain clips relative to the control clips were positively associated with the level of self- reported Empathic Concern but not Perspective Taking. This relationship held across the
ASD and control groups. These associations are consistent with previous research, demonstrating that higher levels of empathy for pain are associated with increased trait level empathy, as measured by questionnaires such as the IRI (Bufalari & Ionta, 2013a). The 77
Empathic Concern subscale is purported to be an index of affective empathy in contrast to the
Perspective Taking subscale which is thought to measure cognitive empathy. Thus, consistent
with expectations, it is trait levels of affective empathy rather than cognitive empathy that is
associated with interpreting intensity and unpleasantness of pain in others. While being a
measure of affective empathy, the Empathic Concern subscale taps into top-down cognitive
processing because it requires the ability to self-reflect on one’s empathic abilities, albeit in emotional contexts. The positive relationship between trait empathy and subjective responses to pain in others affirms that people, be they those with or without ASD, are able to introspect
on their subjective experiences to emotional contexts.
Taken together, this study leaves open the question regarding sensorimotor mimicry
to pain in ASD. However, it does suggest that people with ASD had relatively low arousal to
watching another person’s pain and subjectively appraised another’s pain as less intense and
unpleasant than did controls. Further, arousal levels were not found to be associated with
subjective ratings in this sample. Combined, these observations suggest that the group with
ASD had lower engagement, both autonomic (bottom up) and cognitive (top down), to pain
in others, and this was related to low self-reported affective empathy. Whether arousal and
subjective experiences are linked (e.g. the former driving the latter, or vice versa), was not
possible to establish in this study. Although no relation between arousal and subjective
ratings were found, this could reflect heterogeneity within the ASD group combined with a
relatively small sample. Further research is needed to examine this question.
One potential factor that may influence one’s ability to empathise with others is social
motivation. Individuals with ASD have been shown to have reduced social motivation in the
early years of life, with reduced attention to faces (Kikuchi, Senju, Tojo, Osanai, &
Hasegawa, 2009) and poor eye contact (Zwaigenbaum et al., 2005). However, other studies
have demonstrated that adolescents and adults with ASD, especially at the high-functioning 78 end of the spectrum, report the desire for friendships and relationships (Sperry & Mesibov,
2005), indicating some level of social motivation. One way to study social motivation in this population is to examine the effects of ostracism or social exclusion. Can adults with ASD infer ostracism when it occurs and how is this experienced?
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CHAPTER 5
STUDY 3: The effects of ostracism on adults with ASD3
In studies 1 and 2 of this thesis, I have explored different components of emotional empathy, including bottom up physiological responses and top down cognitive appraisal and self-awareness. The results are mixed with respect to the extent that bottom up processes are affected in ASD but both studies suggested that in people with ASD the subjective evaluation of others in emotionally stressful or painful situations was muted. According to Figure 1.2
(Chapter 1) this would suggest that metacognitive processes may be those that are especially
compromised in ASD. Another potential component of emotional empathy, as detailed in
Figure 1.2 is social motivation, i.e. the extent to which we are motivated to attend to social
stimuli and seek social reinforcement. It could be argued, for example, that low emotional
empathy in people with ASD arises from a lack of interest in others, including a lack of
interest and engagement in their plight. In order to address this, the following study,
examined responsiveness to social exclusion, or ostracism, as an index of social motivation.
Ostracism or social exclusion is a natural part of life, from school years to the work place. It is evident in both human and animal interactions and can have lasting negative effects, including depressed mood, loneliness, anxiety, frustration and helplessness (K. D.
Williams et al., 2000). Ostracism has been largely unexplored in individuals with ASD. In one large study examining peer-group indicators of social exclusion among pupils with ASD,
Symes and Humphrey (2010) found that pupils with ASD were more likely to be rejected and less likely to be accepted by their peers than children with other forms of learning disorders
3 Published as Trimmer, E.M., McDonald, S., Kelly, M. & Rushby, J. A. (2017). The psychological and physiological effects of ostracism in Autism Spectrum Disorders. Journal of Autism and Developmental Disorders, 2326-2335.
80
and typically developing children. Further, these children with ASD experienced greater
levels of bullying and reported lower levels of social support from classmates and friends.
What is less well understood however, are the immediate effects of such social exclusion on
individuals with ASD.
An experimental method used for assessing the effects of ostracism is the Cyberball
game, a pretend online ball-toss game, whereby participants are made to believe they are
being excluded from an interactive cyber ball-toss game. Cyberball has been used repeatedly
across a wide range of participant samples, demonstrating its robust nature (Hartgerink, van
Beest, Wicherts, & Williams, 2015). In the original Cyberball experiment, exclusion from the
game produced negative feelings as well as decreased self-esteem and sense of belonging (K.
D. Williams et al., 2000). These results have been replicated in numerous studies (Durlik &
Tsakiris, 2015; Ruggieri, Bendixen, Gabriel, & Alsaker, 2013). In one of the only studies examining the use of Cyberball as a manipulation of ostracism in participants with ASD,
Sebastian et al. (2009) found that adolescents with ASD were as able as matched controls to recognize when they are being excluded from a social situation, and reported similar effects of ostracism on state anxiety and need threats (sense of control, sense of belonging, self-
esteem and sense of meaningful existence). However, when it came to reporting on the mood
items of the Cyberball questionnaire, those with ASD did not report any decreased mood
following exclusion, while mood was negatively affected by ostracism in the control group.
This suggests that those with ASD were either responding emotionally to ostracism in
a different way to control participants, or that they were reporting their response differently.
It is difficult to tease these two interpretations apart with the use of subjective report
measures. As an interesting advance in this field, there have been a number of recent papers
that have examined psychophysiological responsivity during both the Cyberball task and
other experimental manipulations of ostracism. In the Cyberball task, skin conductance and 81
heart rate have been shown to be sensitive to inclusion versus exclusion conditions (Iffland,
Sansen, Catani, & Neuner, 2014a; Kelly, McDonald, & Rushby, 2012; Kouchaki &
Wareham, 2015), and increased levels of salivary cortisol was found in participants who were made to feel excluded from a social task (Blackhart, Eckel, & Tice, 2007). In individuals with
a history of peer victimization, exclusion from the Cyberball game elicited both immediate
and delayed effects on psychological outcomes, including skin conductance response (Iffland,
Sansen, Catani, & Neuner, 2014b).
Kelly et al. (2012) suggest that higher arousal levels are linked to stress associated with social pain, as can be seen in brain regions associated with increases in SCLs, such as the anterior cingulate cortex (ACC), which has shown to be activated when subjects experience social exclusion (Rotge et al., 2014). This is consistent with fMRI studies of
ostracism. Will et al. (2016) examined neural responses to social exclusion in adolescents who had experienced chronic rejection by peers during their childhoods and children who had been socially accepted by their peers. Whilst both groups of adolescents reported similar increases in distress after being excluded in the Cyberball game, the group who experienced chronic peer rejection showed higher activity in brain regions previously linked to the detection of social exclusion, including the dorsal anterior cingulate cortex (dACC). A similar finding was reported in a study examining brain responses of ostracism in children with ASD (McPartland et al., 2011). In this study it was found that whilst children with ASD respond in similar ways to control children on self-report measures, brain activity indexed by event-related potentials revealed a dissociation between reported distress and neural responses in children with ASD, and a difference in the temporal course of brain responses between children with ASD and typical peers (McPartland et al., 2011). These two studies suggest that there are subtle differences in neural activity in those who have experienced 82
social exclusion (both non-clinical adolescents and those with ASD), despite no apparent
differences in self-reported emotional state.
To date, no study has examined the physiological arousal of individuals with ASD
when engaged in the Cyberball task. As the likelihood of experiencing social rejection is
elevated in individuals with ASD, psychophysiological arousal responses to ostracism may
also be elevated, due to the stress associated with the ongoing effects of continuous and
repeated ostracism. However, if social motivation is diminished in ASD, there may either be
lower arousal as a result of ostracism, or else a dissociation between arousal levels as
measured objectively and muted self-reported emotional state, mirroring the dissociations
seen in neural activity and the results of Studies 1 and 2.
The Present Study
The purpose of this study was to examine both self-reported emotional and behavioural responses, and the physiological effects of ostracism in adults with ASD, using the well-established Cyberball paradigm. Based on previous research, it was hypothesized that 1) individuals with ASD would be able to detect ostracism when they were excluded from the game; 2) the ASD group would report similar levels of needs threats (sense of belonging, sense of meaningful existence, self-esteem, sense of control) after being ostracized compared with controls; 3) the ASD group would report less negative mood after being ostracized than controls. 4) As regards arousal, individuals with ASD may display lower levels (reduced social motivation and effects of exclusion), or similar or even heightened arousal (dissociating from self-reported mood) following ostracism compared with controls.
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Method
Participants
Twenty-five individuals (mean age 28 years, age range 16-61, 4 females) from the larger sample with a diagnosis of high-functioning (IQ > 80) Autism Spectrum Disorder (ASD) and twenty-six non-clinical control individuals (mean age 27 years, age range 17-50, 5 females) were recruited for this study.
Procedure
On arrival at the lab, informed signed consent was given and diagnostic information was provided by the participant. Each participant was informed that they would be participating in
“an online game examining the effect of mental visualization on cognitive processing ability” called Cyberball. Participants were informed that they would be playing a ball toss game with three participants from other universities. Participants were also shown and explained how the psychophysiological measures would be used. Participants were then seated in a chair facing a computer screen approximately 60cm away. Skin conductance and heart rate recording devices were then attached. Participants were requested to sit quietly and relax for several minutes until skin conductance levels had plateaued to a resting state. A 3-minute resting baseline period was recorded for all autonomic measures. Instructions were then presented asking the participant to mentally visualize the ball toss game. After each “game” a
5-minute post-task baseline was recorded for all physiological measures. Following this, participants were asked to complete the 24-item Cyberball Questionnaire.
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Manipulation of Ostracism – Cyberball
Cyberball is a pretend virtual ball toss game, which is often used in research to induce
feelings of ostracism. The participants are informed that they are playing a game of ball toss
with three other participants from different universities and that the purpose of the game is to
measure participants’ ability to mentally visualise the game whilst they’re playing. In fact,
the game is programmed for the other “players” to include/exclude the participant from the
game. All participants played two games of Cyberball, one in which they were included
equally amongst all other players (inclusion condition) and one in which they were excluded
from the game after their first two throws (exclusion condition). The order in which
participants played the games was counterbalanced.
Materials and Measures
Cyberball Questionnaire
This is a 24-item questionnaire assessing (1) four fundamental needs (12 questions) and (2) current mood (8 questions) as well as a manipulation check. Responses were rated on a 5- point Likert scale ranging from “not at all” to “very much”. The fundamental needs were:
self-esteem, sense of belonging, sense of meaningful existence and sense of control. Mood
questions consisted of eight questions such as “My mood was… good, bad, happy, sad,
friendly, unfriendly, tense and relaxed”. Four questions examined the effectiveness of the
manipulation of ostracism. Participants were asked how excluded they felt as well as
indicating the percentage of ball tosses they believed they received, assuming 33% of ball
tosses is equal amongst players. 85
Wechsler Abbreviated Scale of Intelligence
The ASD group scored significantly higher than the control group on IQ (t(1, 49) = 2.49, p =
.016).
Data Reduction and Analyses
In line with data analysis methodology used by Kelly et al. (2013) throughout the Cyberball paradigm, the mean skin conductance level signals were derived for the 3-minute resting baseline period as well as intervals of 10 second epochs throughout the period of the game.
The Cyberball game went for approximately 120-150 second for each participant. Epochs were created for the first 120 seconds (12 epochs) so as to avoid any missing data. Change scores were calculated by subtracting mean activity at baseline from the mean during each
10-second epoch. A log transformation was computed for skin conductance change score epochs to normalize the distribution of scores. This was completed for both conditions.
The data for skin conductance level were examined with a 2 x 2 x 12 repeated-measures
ANOVA with group (ASD vs Control) as the between-subjects factor and condition (included vs excluded) and time (Epochs 1-12) as the within-subjects factors.
The self-report questionnaire data were examined using a series of repeated-measures
ANOVAs with group (ASD vs Control) as the between-subjects factor and condition
(included vs excluded) and need (belonging, control, meaningfulness, self-esteem) or mood valence (positive or negative) as the within-subjects factors.
86
Results
A summary of demographic information is presented in Table 5.1.
Table 5.1. Participant demographic details and performance on self-report questionnaires
ASD Control T statistic Significance
Mean Age (SD) 31 (16.4) 28 (8.9) .80 .43
Age Range 16-61 18-50
Males:Females 20:5 22:4
Mean IQ (SD) 118 (13.9) 109 (13.9) 2.48 .017
AQ 33.5 (5.54) 15.7 (6.54) 5.35 < .001
Manipulation of Ostracism
Both participants in the control group and the ASD group were able to accurately perceive the percentage of ball tosses they received in the inclusion condition (actual = 33%; control estimated M = 33.18, SD = 11.55; ASD estimated M = 31.57, SD = 8.74) as well as the exclusion condition (actual = 8%; control estimated M = 10.60, SD = 9.42; ASD estimated M
= 10.26, SD = 5.66). Furthermore, one-sampled t-tests demonstrated no differences for ASD 87 participants in perception of throws to actual throws in the inclusion condition (t(24) = 0.595, p = .558) , whilst there was a trend towards significance in the exclusion condition (t(24) =
1.979, p = .059), indicating the ASD group perceived a slight increase in ball throws than actually occurred. One sampled t-tests for the control group revealed no significant differences for either condition (Inclusion: t(25) = 0.034, p = .973; Exclusion: t(25) = 1.463, p = .156). This indicates that both groups were accurate at perceiving ostracism when it occurred in the game, however, the ASD group may have had a slightly inflated sense of inclusion when they were playing the exclusion condition game.
Self- reported needs and mood
A significant main effect of needs ratings was found for condition with large effect size, with participants’ needs being met to a greater extent when included in the game than when excluded (F(1, 49) = 23.20, p < .001, Pη2 = .321). No main effects or interactions were found for group.
6
5
4
5) - 3
(1 ASD
2 CON
1
Mean Needs Ratings Mean Needs Inclusion Ratings after 0 Self-esteem Sense of Meaningful Control Belonging Existence
Figure 5.1. Needs Ratings after Inclusion 88
6
5
4
5) - 3 ASD (1
2 CON
1
Mean Needs Ratings after Exclusion Mean Needs Exclusion Ratings after 0 Self-esteem Sense of Meaningful Control Belonging Existence
Figure 5.2. Needs Ratings after Exclusion
In terms of mood ratings, no main effects were found for group or condition. However, a
significant interaction was found between valence and group with a large effect size (F(1, 49)
= 10.04, p = .003, Pη2 = .170), with t-tests indicating the ASD group rated less positive
feelings than controls (t(1, 49 = 3.293, p = .002), as well as a trend towards the ASD group
rating more negative feelings than controls (t(1, 49) = 1.982, p = .053, ns). A significant interaction was also discovered between valence and condition with a large effect size (F(1,
49) = 28.91, p < .001, Pη2 = .371) with participants rating their mood as more positive/less negative when they played the inclusion version than when they were excluded.
89
4.5
5) - 4
3.5
3
2.5 ASD 2 Control 1.5
1
0.5 Mean Mood ratings after Inclusion (1 Inclusion after ratings Mood Mean 0 Positive Negative
Figure 5.3. Mood Ratings after Inclusion
4.5
4
3.5
3
2.5 5) - ASD (1 2 Control 1.5
1
Mean Mood Ratings Exclusion after Ratings Mood Mean 0.5
0 Positive Negative
Figure 5.4. Mood Ratings after Exclusion
90
Skin Conductance Level
A significant main effect with a large effect size was found for group with those with ASD
having higher skin conductance levels compared with controls during either game (F(1, 49) =
18.27, p < .001, Pη2 = .27). A trend towards significance was found for condition with
participants responding with greater skin conductance levels when they were excluded from
the game compared with when they were included equally (F(1, 49) = 3.98, p = .051, Pη2 =
.075). Further, significant linear (F(1, 49) = 50.26, p < .001, Pη2 = .51) and quadratic (F(1,
49) = 21.56, p < .001, Pƞ2 = .31) effects were found for time, both with large effect sizes, indicating a change in skin conductance levels over the course of the game across groups and condition, with increased skin conductance at the start of the game, reducing over time. A
Group x Condition interaction was found with a large effect size (F(1, 49) = 8.49, p = .005,
Pη2 = .15) with t-tests revealing the ASD group responded with greater skin conductance
levels (change from baseline) than controls in the exclusion condition (t(1, 49) = 5.295, p <
.001), but not in the inclusion condition (t(1, 49) = 1.574, p = .122, ns) . No interactions were
found for Group x Time (F(1, 49) = 1.24, ns) or Time x Condition (F(1, 49) = 2.59, ns).
Simple analyses in the exclusion condition result in main linear (F(1, 49) = 21.4, p < .001,
Pƞ2 = .304) and quadratic (F(1, 49) = 16.84, p < .001, Pƞ2 = .256) effects for time with large
effect sizes. A trend towards a significant linear (F(1, 49) = 3.875, p = .055) and cubic (F(1,
49) = 3.554, p = .065) interaction between time and group was found, with the ASD group
showing less change over time than the controls. Figures 5.6 and 5.7 display skin
conductance levels over time, baselined at zero to demonstrate habituation of arousal in both
the inclusion condition and exclusion condition.
91
Total Skin Conductance Means over Game
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Mean SCL Totals game during SCL Totals Mean Inclusion Exclusion
ASD Control
Figure 5.5. Skin Conductance Level Totals during game
Inclusion Condition
0 -0.02 1 2 3 4 5 6 7 8 9 10 11 12 -0.04 -0.06 -0.08 -0.1 -0.12 -0.14 -0.16 Log SCL Change from Baseline from Change SCL Log -0.18
ASD Control
Figure 5.6. Skin Conductance Levels Over Time throughout the Inclusion Condition
92
Exclusion Condition 0 -0.02 1 2 3 4 5 6 7 8 9 10 11 12 -0.04 -0.06 -0.08 -0.1 -0.12 -0.14 -0.16 Log SCL change from Baseline from change SCL Log -0.18
ASD Control
Figure 5.7. Skin Conductance Levels Over Time throughout the Exclusion Condition NB. Blue arrows indicates approximate time of exclusion.
Table 5.2. Relationship between self-report mood, arousal and trait empathy
IRI_PT IRI_EC EQ AQ Positive SCL Mood AQ -.582** -.294* -.771** - Positive .233 -.087 .377 -.289* - - Mood Negative -.155 -.024 -.284 .122 -.588 .269 Mood SCL -.187 -.269 -.384 .175 -.268 - * p<.05; ** p<.01 (PT: IRI Perspective Taking; EC: IRI Empathic Concern; EQ: Empathy Quotient; AQ: Autism Quotient; Positive Mood to Exclusion; Negative Mood to Exclusion SCL: Skin conductance level for Exclusion;
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Relationship between self-report mood, arousal to exclusion and trait empathy
A correlation matrix outlining the relationships between self-reported mood, skin conductance to exclusion and self-reported trait empathy is presented in Table 5.2. Consistent with Study 1 and Study 2, AQ scores are negatively associated with all other self-reported trait empathy questionnaires (EQ: r = -.771, p < .001; IRI-PT: r = -.582, p < .001; IRI-EC: r =
-.294, p = .040) indicating lower empathy in those with higher autistic traits. Autistic traits were also negatively associated with reporting of positive mood following ostracism (r = -
.289, p = .042). Interestingly, those who displayed higher physiological arousal when ostracised reported lower levels of trait empathy (as measured by the EQ: r = -.348, p = .014).
Higher EQ scores were also positively associated with positive mood, but negatively associated with negative mood following ostracism (Positive Mood: r = .337, p = .018;
Negative Mood: r = -.284, p = .048).
Discussion
Ostracism has behavioural, emotional and physical effects that can be both lasting and damaging. Whilst ostracism is a common experience for many individuals with ASD, their responses and experience of this is largely unknown. This study was the first to examine both the psychological effects of social needs and mood as well as the physiological effects of ostracism in individuals with ASD. Supporting previous findings, adults with ASD were as able as controls to identify when they were being ostracized from an online ball-toss game.
Both ASD participants and controls were correctly able to identify the approximate percentage of throws given when included fairly. However, individuals with ASD appeared 94
to have a slightly inflated sense of inclusion compared with controls when they played the
exclusion condition.
Both groups felt a greater sense of control, a greater sense of meaningful existence, a
greater sense of belonging and greater self-esteem when they were included equally in the game compared with when they were excluded by the “other players”. Consistent with prior research mood ratings between groups were more variable. Whilst all participants rated increased negative mood and reduced positive mood after being excluded from the game, the
ASD group responded with more negative mood responses overall. This may reflect two things: (1) lower mood in general in the ASD group; (2) lower insight into their own mood responses. Whilst depressed mood was not assessed in this study, it has been found to be more common in those with ASD (Berthoz, Lalanne, Crane, & Hill, 2013; Mukkades &
Fateh, 2010; Shtayermman, 2007). Sebastian et al. (2009) found that adolescents with ASD reported similar needs and anxiety ratings following Cyberball exclusion, consistent with our study, but found differences in mood ratings. ASD participants did not report any mood differences between inclusion and exclusion conditions, unlike controls who reported lower mood after being excluded. Whilst these results differ from ours, the ASD group in the current study reported more negative and less positive mood overall and this did not appear to reflect what they experienced physiologically following social exclusion. It may be that the
ASD group in the present study experienced a flattening or dulling of perceived emotion and were unable to interpret their arousal response as being emotionally salient.
Using skin conductance as a measure of arousal and emotional response, both individuals with ASD and matched controls experienced a trend towards greater arousal when they were excluded from the game compared with when they were included. This suggests greater distress or emotional pain following ostracism. Previous research has consistently demonstrated heightened arousal following negative experiences or viewing negatively 95 valenced stimuli in both the general population (Greenwald, Cook, & Lang, 1989; Kelly et al., 2012) and in individuals with ASD (Mathersul et al., 2013a).
Individuals with ASD experienced greater arousal than controls in both conditions. It may be the case that those with ASD were more interested in the game or experienced increased anxiety when engaging in the task. Of most interest, there was a trend suggesting that the ASD group experienced heightened arousal compared with controls when they experienced ostracism or exclusion from the game, even though they appeared to notice it less. Figure 5.6 demonstrates that this difference in arousal response was most evident at the end of the game in which they were excluded. The control group appeared to habituate to the ostracism and arousal levels reduced after a period of time. Conversely, the ASD group did not show this same pattern of habituation – their arousal level remained much higher for much longer. This suggests a greater level of distress and social pain following ostracism in adults with ASD such as may occur with poor emotion regulation in response to a distressing or negative experience.
When looking at the relationships between self-reported and physiological responses to ostracism, higher levels of self-reported trait empathy was associated with reduced physiological arousal during ostracism as well as increased positive mood scores and decreased negative mood scores. Together these associations provide evidence that those who have higher trait-level empathy are better able to regulate their emotional responses during social stress, such as when being ostracised.
Emotion regulation impairment is a frequently experienced by individuals with ASD
(Mazefsky et al., 2013). Bogdanov et al. (2013) revealed that individuals with higher empathy scores displayed faster habituation of arousal during a task in which they watched videos eliciting negative emotions. This fits with a model of empathy (as discussed in 96
Chapter 1) that suggests that empathy relies on good emotion regulation. Specifically, one
needs to be able to effectively regulate one’s emotional response to another’s distress in order
to be truly empathic. The results here, therefore, are consistent with the notion that people
with ASD may suffer from poor top-down regulatory mechanisms that enable them to control
their own emotional arousal following a negative emotional event and that relatedly, this
effects their ability to empathise.
Based on results from both psychological and physiological measures, it appears
individuals with ASD may not be attuned to their physiological response when they are
ostracized. Whilst they experienced a heightened arousal response to being excluded from the
game, they did not express this difference in their mood ratings. Individuals with ASD reported more negative mood overall compared with controls, but no difference was found between conditions. One explanation for this seeming dissociation between physiological response and self-reported mood is, once again, that people with ASD have difficulty cognitively appraising their own emotional states, leading to an impairment akin to alexithymia, i.e. misattribution of bodily changes and poor emotion regulation (Bird et al.,
2010; Silani et al., 2008). Alexithymia is far more common in people with ASD with reported rates above 50% compared with approximately 10% in the general population
(Bernhardt et al., 2014; Hill, 2004). Consistent with this interpretation, there is evidence that alexithymia rather than the ASD per se predicts activation of the areas of the brain associated with interoception (specifically the anterior insula) (Bird et al., 2010). Functional imaging studies have demonstrated that alexithymia relates to hypoactivity of fronto-insula cortices in both typical adults and those with ASD during empathy and interoception, irrespective of
ASD diagnosis (Bernhardt et al., 2014) as well as in non-clinical adults with high alexithymia compared with adults with low alexithymia (Moriguchi et al., 2007). 97
Research into the psychophysiological processes associated with alexithymia is less
clear. For example, Roedema and Simons (1999) found that alexithymic subjects showed lower skin conductance response to affective stimuli and gave muted self-reports of arousal compared to control subjects. Conversely, Bogdanov et al. (2013) found that participants with greater alexithymia had higher increases in autonomic arousal to emotion-inducing videos or music suggesting hyperresponsivity. This is in line with our results demonstrating heightened
SCL in the ASD group when ostracised. One explanation for these inconsistencies may be
addressed by Bermond’s (1997) proposition of two distinct types of alexithymia: Type I,
characterized by an absence of emotional experience and Type II, characterized by a normal or high degree of emotional arousal along with a low degree of accompanying cognitions
(Larsen, Brand, Bermond, & Hijman, 2003). The results of the current study suggest that the
ASD group performance is more consistent with the Type II category, displaying difficulties
expressing and explaining emotions, rather than their absence. This is consistent with the
results of Study 1 demonstrating awareness of arousal, but flat or blunted response to salience
of emotion (Trimmer, McDonald, & Rushby, 2016). It is also somewhat consistent with
Study 2 although, in that case there was not only a muting of subjective responses to
another’s pain, but also some indication of a lower arousal response. The variability in
responses again, suggests there is heterogeneity within the ASD sample used in these studies.
While alexithymia is more common in ASD, only about half of people on the Autism
Spectrum have high alexithymia. The prevalence of Type 1 and Type II alexithymia is
unknown. Heterogeneity of both the extent and type of alexithymic traits may account for the
differences in emotional responding to stimuli found in Studies 1-3 of this thesis, as well as
findings in other studies (Bird et al., 2010). Specific to social exclusion in ASD, further
research differentiating ASD and alexithymic traits in response to ostracism would help tease
this out. 98
Limitations and Conclusions
This study is not without limitations. First, our ASD group had significantly higher IQ than our control group. However, given the nature of the Cyberball task, it is unlikely to have significantly impacted responses and was not correlated with any of the dependent variables.
Second, whilst manipulation of ostracism was effective in this study, many participants
became aware of the manipulation throughout the game, potentially reducing the
effectiveness. If subjects were aware that the purpose of the study was to induce feelings of ostracism and that the “other players” were not real, the physiological responses may not have been as strong as the negative emotion elicited may have been diminished. Whilst this
information was anecdotal and was not recorded formally, it would be helpful in future
research to record this information when people are playing the game. For future research and
replication of this study, a more up-to-date version with better digital graphics may increase believability in the game.
Overall this study suggested that individuals with ASD experience normal levels of social motivation in that they recognize and experience ostracism in a similar way to controls, with perhaps heightened and more enduring physiological effects. However, these responses are not interpreted or expressed as emotionally significant relative to those without ASD.
This has important implications for clinical understanding and practice. Given the increased experience of ostracism in this population and the lasting physiological effects, teaching emotion regulation and coping strategies to this group is vital. This study demonstrates that social exclusion is both recognized and deeply felt by those with ASD. However, it may not be reported or expressed as it is in controls. Further, those with ASD may not possess the same coping ability to regulate the negative emotions experienced as a result of exclusion. As 99 such, coping and emotion regulation strategies should therefore be included in any intervention.
Whilst high-functioning adults with ASD appear to be motivated to engage in social behaviour and interactions, impaired cognitive empathy and poor social skills can lead to social exclusion or ostracism by others. Furthermore, as shown in Study 3, these individuals experience heightened emotional responses in these situations. This may in turn lead to increased anxiety, especially in social situations. Based on the model presented in Chapter 1, it appears that this group of individuals with ASD have intact social motivation. Poor social motivation is therefore an unlikely explanation for any abnormalities in empathic responding seen in Studies 1 and 2. However, those with ASD reacted with more emotion or anxiety.
What is not known is whether this increased emotional responding and anxiety affects the empathy process, both cognitive and emotional. Therefore, Study 4 examined people with
ASD’s performance on a measure of theory of mind and the relationship between this performance and two measures of anxiety: trait anxiety and more specifically, social anxiety.
This relationship was also compared, more generally, to self-report cognitive and emotional empathy.
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CHAPTER 6
Study 4: Anxiety and Empathy in ASD
ASD is rarely diagnosed alone, more often than not it is diagnosed along with comorbid
ADHD, anxiety, oppositional defiant disorder, depression, language impairment and/or
intellectual disability in children (Mefford, Batshaw, & Hoffman, 2012; Pickles, Charman,
Chandler, Loucas, & Baird, 2008). Anxiety disorders are one of the most common comorbid
diagnoses with ASD. Approximately 40% of children with ASD are estimated to have clinically
elevated levels of anxiety or at least one anxiety disorder (van Steensel, Bogels, & Perrin, 2011;
White, Oswald, Ollendick, & Scahill, 2009). These children are also more likely to have multiple anxiety disorders, with specific phobias and social anxiety being the most common
(Freeth, Bullock, & Milne, 2012; van Steensel et al., 2011). Furthermore, anxiety symptoms
tend to increase into adolescence and adulthood in individuals with ASD, especially symptoms
of social anxiety (White et al., 2011b).
Social anxiety is particularly important when conceptualising ASD as there is
significant overlap in diagnostic criteria between the two conditions (Kuusikko et al., 2008).
Social Anxiety Disorder is characterized by discomfort around social interaction and concern
about being embarrassed and judged by others (Association, 2013). Whilst people with social
anxiety desire social contacts and want to participate in social situations, their anxiety can
become unbearable when they make these attempts. Therefore, anxiety can often lead to
isolation, and either absence or dampening of development of social skills (Rubin, Bowker, &
Gazelle, 2010). A predominant feature of social anxiety is the focus on the self, rather than
others (Clark & Wells, 1995). Socially anxious individuals are less likely to approach others
and keep further distance when interacting with others (Rinck et al., 2010). The social 101
difficulties of social withdrawal, preference for being alone and not speaking in social
situations that are characteristic of social anxiety are also characteristic of ASD. Furthermore,
these avoidant characteristics associated with both disorders have been shown to impact
negatively on the individuals’ ability to develop social and emotional processing skills (Bellini,
2004). Thus, while social anxiety may be caused from a desire to fit in socially, (pro-social
motivation) it can drive a desire to avoid social situations (anti-social motivation) and therefore
can be conceptualised as affecting social motivation in the Empathy model in Chapter 1.
Indeed, Mansell et al. (1999) found that highly socially anxious individuals show an attentional bias away from emotional faces under conditions of socially-evaluative threat.
Additionally, anxiety can have direct effects on other aspects of the empathic process.
Being able to focus on the needs or feelings of others, rather than solely on one’s own feelings
is difficult in times of increased anxiety or for highly anxious people in general (Heerey &
Kring, 2007). Anxiety is associated with impaired mental flexibility and working memory
(Maehara & Saito, 2011) and will make meta-cognitive judgements difficult with respect to
both cognitive empathy (e.g. affective ToM) and affective empathy (e.g. responding to the other
person’s emotional state). Hezel and McNally (Hezel & McNally, 2014) compared socially
anxious and non-socially anxious individuals on measures of theory of mind. Those participants
with higher social anxiety performed worse than those without social anxiety, indicating people
with social anxiety appear to have difficulty understanding others’ thoughts and emotions.
Within the ASD population, Mathersul et al. (2013c) found increased resting state arousal in a
subgroup of individuals with ASD, which is indicative of anxiety. This group also reported
lower self-report emotional empathy on a self-report measure, indicating that higher arousal
(increased anxiety) may be associated with impaired empathic processes such as emotion
recognition and responsivity. Higher levels of anxiety have also been associated with greater
amygdala activation, i.e. arousal, when a group with ASD observed emotional faces (Kleinhans 102
et al., 2010). These studies suggest that if levels of distress and anxiety are high, understanding
of emotions in others is impaired. This, in turn, suggests that more complex processes such as
empathic understanding and expression of empathy, both cognitive and affective, are also likely
to be impacted.
Given this overlap of impairments in empathy seen in both social anxiety and in ASD
and the high prevalence of both trait and social anxiety in ASD, it is conceivable that social
anxiety makes a significant contribution to theory of mind and empathic deficits in individuals
with ASD. However, no study has yet examined the relationship between these two important
concepts in the ASD literature.
The Present Study
The aim of this study was to explore the relationship between self-report cognitive
and emotional empathy and measures of trait anxiety and social anxiety. The role of
anxiety was explored as a predictor of empathy deficits above and beyond autistic traits.
Based on previous research, we developed four hypotheses: (1) the ASD group would
report significantly greater levels of trait and social anxiety and reduced cognitive and
emotional empathy compared to the control group; (2) the ASD group would perform more poorly than the control group on an objective measure of cognitive empathy (theory of mind), the Faux Pas test; (3) increased levels of both trait and social anxiety would be associated with reduced self-reported cognitive and emotional empathy and impaired
performance on the Faux Pas test and (4) trait and social anxiety would account for a
significant proportion of deficits in (i) cognitive and (ii) emotional empathy over and
above autistic traits.
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Method
Participants and Procedure
Twenty five individuals (mean age 28 years, age range 16-61, 4 females) with a diagnosis of
high-functioning Autism Spectrum Disorder (ASD) and twenty-five non-clinical control individuals (mean age 27 years, age range 17-50, 5 females) were recruited as part of the larger
sample used in this thesis. Each participant completed the battery of questionnaires as part of
the larger project.
Materials and Measures
Faux Pas (Stone, Baron-Cohen, & Knight, 1998)
The adult version of the Faux Pas test was used in which participants were read a series of
stories and answered questions about whether someone in the story said anything awkward
and the underlying motive. Participants were able to read the story in front of them whilst
listening to the story being read. Ten stories contained a faux pas and ten stories did not.
Participants were asked eight questions following each story about: (1) detection of faux pas
(did anyone say something awkward?); (2) personal identification (who said something
awkward?); (3) the content (what was awkward?); (4) the explanation (why was it
awkward?); (5) the false belief (did the person know that?); (6) empathy (how did the person
feel?); and two questions to test memory and understanding of the stories. 104
For each of the ten stories containing a faux pas, participants got 1 point for each question
they answered correctly, so that they could receive a total score out of 60 points (six
questions x 10 faux pas stories) plus a score out of 10 for each of the faux pas questions. As
per scoring methodology advised by Stone and Baron-Cohen (1998), questions 1 & 2 are
grouped to form a single score out of 20. For participants who answered “no” to the first
question (detection of faux pas), a score of 0 was given for that whole story. For the ten
control stories (no faux pas), participants only scored points for each of the questions on
stories in which they correctly identified that no faux pas had occurred. If a participant
incorrectly reported a faux pas in a control story, they received a score of 0 for all questions
in that story. For each of the ten control questions (memory tests), participants received 1
point when they answered correctly, so that their total maximum score was 20 points (2
questions x 10 stories). A total question score was then computed for each of the five
questions (1+2, 3-6) by adding the faux pas story score (out of 10) with the control story
score (out of 10) to obtain a total score for each question out of 20.
Empathy
In order to reduce the number of subtests and examine cognitive and emotional empathy as two
separate constructs, an average score was computed for each subject between IRI-PT and EQ-
CE (here on referred to as “Cognitive Empathy”) and between IRI-EC and EQ-ER (here on referred to as “Emotional Empathy”). Whilst the EQ is usually used as a measure of general trait empathy and not split into subscales, Lawrence et al. (2004) performed factor analysis on the EQ resulting in three distinct subscales: Cognitive Empathy (EQ-CE), Emotional Reactivity
(EQ-ER) and Social Skills (EQ-SS). Mathersul et al. (2013c) reported results from a factor analysis on IRI and EQ subscales, with IRI-PT and EQ-CE resulting in a single “cognitive 105 empathy” factor and EQ-ER and IRI-EC resulting in a single “emotional empathy” factor.
Given the validity of these findings, the same constructs were used in this study.
Social Phobia and Anxiety Inventory (SPAI) (Turner, Beidel, Dancu, & Stanley, 1989)
The SPAI consists of 45 primary items (21 including sub-items) which are rated on a seven point Likert scale (0 = Never to 6 = Always). The SPAI has items measuring social phobia (32 items) and agoraphobia (13 items). For the purpose of this study, participants were only required to complete the 32 social phobia items. The social phobia score can range from 0 to
192 and the original cut-off score for “probable social phobia” according to the SPAI manual is any score greater to or equal to 80. The SPAI demonstrates excellent construct (Turner et al.,
1989) and concurrent validity (Herbert, Bellack, & Hope, 1991) as well as high short-term retest reliability (Turner et al., 1989).
State Trait Anxiety Index (STAI) (Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983)
The STAI is a widely used measure, often used in research. It has two scales: state anxiety and trait anxiety. Only the trait anxiety scale was used in this study. The trait anxiety scale consists of 20 items that are rated on a four point Likert scale ranging from “not at all” to “very much so”. The trait subscale has excellent internal consistency (α > .89) and the trait scale also has very good test-retest reliability (average r = .88) (Barnes, Harp, & Jung, 2002).
106
Baseline Arousal
Skin conductance level was used as a measure of baseline arousal, by taking the average skin conductance level across a 3-minute resting period in which the participant was told to relax
while watching a blank screen.
Data Analysis
Statistical analyses were conducted using IBM SPSS (Version 23). Five ANOVAs were
performed for the five Faux Pas questions to determine whether the ASD group differed from
the controls in their responses. Diagnostic group (ASD vs. control) was added as the
independent variable with Faux Pas score (Q1&2, Q3, Q4, Q5, Q6) as the dependent variable.
Due to the number of analyses (five comparisons), a Bonferroni correction of p < .01 was used to detect significance. IQ was added as a covariate each time due to significant differences in IQ scores between groups. IQ has been shown to influence performance on
cognitive empathy, but not emotional empathy (Schwenck et al., 2014).
Two t-tests were performed to determine group differences in responses on both the
Cognitive Empathy and Emotional Empathy scores. Partial correlations between the Faux
Pas, Cognitive Empathy and Emotional Empathy scores and AQ scores were examined using
Pearson’s correlation coefficient, with IQ as a covariate, due to differences between groups in
IQ. Regression analyses were planned to test the amount of variance accounted for by each of
IQ (due to significant differences between groups), autistic traits (AQ) and each of social anxiety (SPAI) and trait anxiety (STAI) in predicting each of Cognitive Empathy and
Emotional Empathy. Assumptions for the regression analyses including independence of residuals, linearity, normality and homoscedasticity of residuals and absence of problems of multicollinearlity between predictor variables were met (all tolerance values >.10). 107
Results
Demographics
Basic demographic information and scores on the questionnaires are detailed in Table 6.1.
Table 6.1. Participant Demographics for the group with ASD and the control group as well as Empathy and Anxiety scores ASD Control T statistic Significance
Mean Age (SD) 29 (13.8) 28 (9.2) .50 .62
Age Range 16-61 18-50
Males:Females 20:5 21:4
Mean IQ (SD) 116 (13.7) 108 (13.9) 2.54 .014
AQ Score 36.2 (8.54) 15.7 (6.54) 6.26 <.001
SPAI 110.31 (32.52) 62.87 (38.76) 4.687 <.001
STAI 52.24 (10.01) 37.62 (9.07) 5.472 <.001
CE 8.72 (4.76) 17.38 (3.75) 7.21 <.001
EE 10.48 (4.98) 17.38 (2.64) 6.15 <.001
108
There were no significant differences between groups on age (t(1, 48) = .46, p = .648) or gender
ratio (χ2 = .09, p = .762). However, a significant difference was found between groups for IQ
with the ASD group having significantly higher IQ (t(1, 49) = 2.21, p = .032). IQ was therefore
entered as a factor in all analyses. Confirming diagnostic group, the ASD group was
significantly higher on the AQ (t(1, 49) = 6.32, p < .001).
Faux Pas
All participants in both the ASD and the control groups answered all memory questions
correctly. Therefore, all stories were counted for both groups. Only the Explanation question
(“Why was it awkward?”) resulted in a significant group difference, with the ASD group
performing worse on this item (F(1, 38) = 7.26, p = .009, Pη2 = .16). IQ was a significant
covariate in this analysis (F(1, 38) = 14.30, p = .001, Pη2 = .27). No group differences were
found for any other question in the Faux Pas stories (Faux Pas Detection & Identification:
F(1, 38) = 1.165, p = .287, Pη2 = .030; Content: F(1, 38) = 1.198, p = .281, Pη2 = .031; False
Belief: F(1, 38) = 0.690, p = .411, Pη2 = .018; Empathy: F(1, 38) = 1.490, p = .230, Pη2 =
.038).
Anxiety
The ASD group had significantly higher scores than the control group on both the SPAI (t(1,
48) = 4.69, p < .001) and the STAI (t(1, 48) = 5.47, p < .001). Furthermore, the average SPAI
score for the ASD group was well above the clinical cut-off score (≥80), with 80 percent of the
ASD group scoring above the cutoff, compared with only 28 percent of the control group. This
indicates the likelihood of clinical levels of social anxiety symptoms in most participants with
ASD. 109
Empathy
The ASD group scored significantly lower than the control group on both the Cognitive
Empathy combined scale (t(1, 49) = 7.21, p < .001) and the Emotional Empathy combined scale
(t(1, 49) = 6.15, p < .001).
Correlations
Correlation analyses were performed for the combined sample (ASD & controls) as well as separate groups. Table 6.2 displays correlations for the total sample.
110
Table 6.2. Correlations between measures of theory of mind (Faux Pas), empathy (cognitive and emotional) and autistic traits in the combined group.
SPAI STAI CE EE AQ Baseline Arousal (SCL) SPAI - .166
STAI .698** - .050
CE -.334* -.623** - .131
EE -.105 -.453** .717 - -.016
FP_3 .0.00 .098 .026 .096 -.013 .044
FP_4 -.106 -.115 .237 .334* -.089 .108
FP_5 -.066 .003 .045 .069 -.202 .007
FP_6 -.087 .047 .022 .163 -.059 .114
AQ .469** .636** -.677** -.507** - -.051
**p < .01, *p < .05, (CE: Cognitive Empathy; EE: Emotional Empathy; FP_3: Faux Pas Content; FP_4: Faux Pas Explanation; FP_5: Faux Pas False Belief; FP_6: Faux Pas Empathy, SCL: Skin Conductance Level)
Total Sample
Social anxiety (SPAI) was negatively correlated with Cognitive Empathy (r = -.334, p = .043) and positively correlated with autistic traits (AQ; r = .469, p = .003). Trait anxiety (STAI) was negatively correlated with both Cognitive Empathy (r = -.623, p < .001) and Emotional
Empathy (r = -.453, p = .005) and positively correlated with autistic traits (r = .636, p < .001).
Autistic traits (AQ) was also significantly negatively correlated with both measures of empathy
(Cognitive Empathy: r = -.677, p < .001; Emotional Empathy: r = -.507, p = .001). A 111
significant positive relationship was also found between Question 4 of the Faux Pas and
Emotional Empathy score (r = .334, p = .044).
Correlations within Groups
When examining the relationships of social anxiety and empathy within each group separately,
the ASD group was found to have a significant negative association between Cognitive
Empathy and autism traits (r = -.584, p = .007). A correlation was also found between trait
anxiety and Cognitive Empathy (r = -.55, p = .012). Conversely, within the control group, a
significant positive relationship was found between social anxiety and Cognitive Empathy (r =
.519, p = 039) and also between trait anxiety and autism traits (r = .66, p = .006).
Effect of Anxiety on Empathy
To determine whether both trait anxiety and social anxiety contributed to Cognitive or
Emotional Empathy independently of autistic traits (AQ), three separate standard regressions
were conducted with IQ, AQ scores and anxiety scores, SPAI and STAI, as the independent
variables and either self-reported Cognitive Empathy or Emotional Empathy scores or
Explanation of Faux Pas score as the dependent variable. While there are small to moderate correlations between predictor variables, all tolerance levels were greater than 0.3 and variance inflation factors were less than 5 indicating an acceptable level of multicollinearity
(Tabachnick & Fidell, 2001). The results of these regression analyses are presented in Tables
5.3-5.5. The regression model predicting self-reported Cognitive Empathy was significant
(F(4, 44) = 14.53, p < .001). Both AQ (β = -.451, p = .002, sr2 = .111) and STAI scores (β = -
.460, p = .007, sr2 = .080) uniquely contributed to Cognitive Empathy. Social anxiety,
however, was not a significant predictor. In terms of predictors for self-reported Emotional 112
Empathy, the model was also significant (F(4, 44) = 7.28, p < .001). AQ scores (β = -.403, p
= .016, sr2 = .089) made a unique contribution as well as both anxiety scales, trait anxiety (β
= -.530, p = .009, sr2 = .105) and social anxiety (β = .375, p = .046, sr2 = .060). For the
regression model predicting Explanation of Faux Pas, only IQ uniquely contributed to
performance on the Explanation item on the Faux Pas test (β = .517, p = .002, sr2 = .247).
Neither measure of anxiety significantly predicted performance on the Explanation of Faux
Pas (STAI: β = -.564, p = .577; SPAI: β = -.949, p = .350).
Table 6.3. Unstandardised and standardised regression coefficients for the variables entered into the model (Cognitive Empathy)
Variable B SE B β p
IQ .056 .044 .129 .215
AQ -.231 .069 -.451 .002
STAI -.233 .082 -.460 .007
SPAI .011 .022 .073 .633
Table 6.4. Unstandardised and standardised regression coefficients for the variables entered into the model (Emotional Empathy)
Variable B SE B β p
IQ -.014 .046 -.037 .762
AQ -.179 .071 -.403 .016
STAI -.233 .085 -.530 .009
SPAI .047 .023 .375 .046
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Table 6.5. Unstandardised and standardised regression coefficients for the variables entered into the model (Explanation of Faux Pas)
Variable B SE B β p
IQ .074 .022 .517 .002
AQ <.001 .031 .003 .989
STAI -.021 .037 -.132 .577
SPAI -.010 .010 -.196 .350
Discussion
This study explored the relationship between self-reported cognitive and emotional empathy, objective theory of mind and trait and social anxiety in individuals with ASD and matched controls. Given the high rates of both trait and social anxiety in this clinical population and the effects of anxiety on social cognition, it was hypothesized that anxiety would contribute significantly to deficits in performance on a theory of mind task as well as self-reported empathy over and above autistic traits.
Consistent with the literature (Freeth et al., 2012; Maras & Bowler, 2012) this study found much higher rates of self-reported trait and social anxiety in the ASD group compared with the control group. Indeed, on the SPAI, the ASD group in this study scored, on average, well above the clinical cut-off for likely diagnosis of social anxiety disorder, indicating clinically significant levels of social anxiety (Turner et al., 1989). 114
Using the Faux Pas task as an objective measure of theory of mind (cognitive empathy),
our groups only differed on one of the six faux pas questions: the explanation of why the faux
pas was awkward. Previous studies have found that adults with ASD perform worse on Faux
Pas total score (Spek et al., 2010) and on items 3-6, but not on the first two items (identification
and person who made the faux pas) (Zalla et al., 2009). One reason for this inconsistency may
be that our group of adult with ASD was particularly high-functioning. However, subtle
differences were evident. Whilst the ASD group answered most items correctly, they were less
descriptive, gave less detailed explanations and less variable in their responses compared with
the control group, especially for Question 6 (how the other person would feel), which requires
some level of cognitive empathy. For example, many of the participants in the ASD group gave
the response “awkward” or “bad”, and while this answer is correct, the participants in the
control group gave much more detailed descriptions of how the person in the story would feel,
such as “the person would feel very embarrassed or insulted by their friend”.
Due to the inconsistencies in the literature regarding the distinction between cognitive
and emotional self-report empathy, we combined two frequently used and well validated
measures on empathy, the IRI and the EQ, creating an average Cognitive Empathy score and an
average Emotional Empathy score. A similar method was used by Mathersul et al. (Mathersul
et al., 2013c). Using these two empathy scales, our findings are reasonably consistent with previous research. Like others, we found that the individuals with ASD reported reduced
Cognitive Empathy. In this study, we also found that individuals with ASD reported less
Emotional Empathy than controls. Whilst these results on emotional empathy are in line with some studies (Hirvela & Helkama, 2011), they oppose others (Rueda et al., 2015) who have found “normal” Empathic Concern ratings in ASD participants. Thus, it appears that loss of emotional empathy is not a consistent finding in ASD although it does occur. 115
Relationships between empathy and anxiety were mixed. In the combined group, both
trait (STAI) and social (SPAI) anxiety were negatively associated with Cognitive Empathy,
indicating that those with higher levels of anxiety (general and social) rated themselves as
having poorer cognitive empathy. Skills, such as theory of mind and mentalising, that require
perspective taking maybe more difficult for those individuals who are highly socially anxious.
Indeed, given that anxiety is known to impair working memory (Darke, 1988), it is also likely to impair the ability to undertake flexible, other orientated thought processes. Certainly working memory capacity has been found to make a significant contribution to performance on theory of mind tasks (Bibby & McDonald, 2005; Maehara & Saito, 2011).
Trait anxiety was also negatively associated with Emotional Empathy anxiety, but no relationship was found social anxiety and Emotional Empathy. Thus, while being generally anxious appears to be associated with having lower levels of shared emotional experience, social anxiety, specifically, does not appear to have this same relationship. The reason for this is not obvious. On the one hand it suggests that those with greater social anxiety are not necessarily those with consistently lower (or higher) emotional empathy. On the other hand, it is possible that Emotional Empathy is affected differently by social anxiety in different individuals. For example, whilst many individuals with ASD may have an emotionally empathic response to another person, increased social anxiety may in some cases heighten this response, e.g. by emotional contagion, shared understanding or compassion, and in other cases, it may detract from it, for example by reducing attention towards the other person through experiential avoidance, a characteristic commonly associated with social anxiety (Mahaffey,
Wheaton, Fabricant, Berman, & Abramowitz, 2013). In support of this interpretation, the ASD group in this study had greater variance in Emotional Empathy scores than the control group. If it is the case that emotional empathy is affected in complex ways by anxiety, this may explain 116
why evidence for an emotional empathy disorder in ASD differs across studies, especially as
anxiety levels are rarely taken into account.
One interesting finding was the difference between groups in the relationship between
Cognitive Empathy and both social and trait anxiety. Individuals with ASD who had high trait
anxiety reported reduced cognitive empathy, but there was no relationship with social anxiety.
However, control individuals with high social anxiety actually rated themselves as having
greater levels cognitive empathy. It may be the case that, for individuals with ASD, it is anxiety
in general, rather than social anxiety specifically, that is associated with poorer cognitive
empathy. Indeed, Mathersul et al. (2013c) found that individuals with ASD who had increased
resting baseline arousal (an indication of state anxiety), rated themselves as having less
empathy. Conversely, within the control group, increased social anxiety actually appears to be
associated with an increase in cognitive empathy and the ability to take the perspective of
another person. For example, Auyeung and Alden (2016) found that individuals with higher
levels of social anxiety responded with greater empathic accuracy, when under social threat. In
the current sample of ASD participants resting baseline was not associated with any measure of
empathy or anxiety. This discrepancy in findings of baseline arousal highlights the
heterogeneity of the ASD population suggesting that whilst in some individuals with ASD,
anxiety and increased arousal are associated with reduced self-reported empathy, in others it is
not. This heterogeneity may also indicate that there are other potential factors in play, rather
than ASD diagnosis, such as the presence of alexithymia traits, that are playing a role in the
relationship between anxiety and empathy.
As expected, autistic traits were negatively associated with both cognitive and emotional empathy, indicating that those with higher levels of autistic traits report themselves as having more difficulty with empathy in general. 117
In order to determine whether anxiety played a unique role in impairing empathy, we
examined relationships between two types of anxiety, trait and social, and three measures of
empathy: self-report Cognitive Empathy, self-report Emotional Empathy and an objective measure of theory of mind, the Faux Pas test. Regressions enabled us to look at the influence of anxiety whilst accounting for IQ, due to the higher level of IQ ability in our ASD group, as well as autistic traits. Increased trait anxiety was found to uniquely predict reduced self- reported Cognitive Empathy and reduced self-reported Emotional Empathy. Again, this suggests underlying trait anxiety plays a particularly detrimental role in both purely cognitive aspects of empathy, such as perspective taking, mentalising and theory of mind as well as emotional aspects such as shared feelings and empathic concern. The role of social anxiety is less clear. Higher levels of social anxiety predicted greater emotional empathy, however, based on differences between groups in the association between emotional empathy and social anxiety, it is likely that the control group are driving this relationship. As discussed previously, social anxiety may influence empathic ability differently in different people. Whilst autistic traits remained a strong predictor of low empathy scores (both cognitive and trait), these results suggest the need to consider anxiety as an independent influence when examining empathy deficits in this population. Interestingly, neither trait anxiety nor social anxiety predicted performance on the explanation item of the Faux Pas, which may have been due to the strong performance in the ASD group on this measure.
Limitations and Conclusions
This study had a few limitations. First, this study employed self-report measures. As alexithymia is highly co-morbid with ASD (Bernhardt et al., 2014; Hill et al., 2004) this may well have affected the extent to which people with ASD were able to report on their own 118 emotions and feelings in this study (Bird et al., 2010). Another issue to reflect on is the nature of using social anxiety measures within the ASD population. Overlap of symptoms between
ASD and social anxiety may mean we are measuring different concepts within each group, such as feelings of distress when in social interactions (in socially anxious individuals) and avoidance of social interactions (in individuals with ASD). For example, avoidance of social interaction may be caused by fear of negative evaluation (Social Anxiety Disorder) or by impaired social communication and restricted interests (ASD). Despite this, there is some reason for confidence in the results reported here. Firstly, a number of studies have demonstrated accurate and consistent self-reporting of own emotions in individuals with ASD who have average to high-average IQ (Hesselmark, Eriksson, Westerlund, & Bejerot, 2015;
Hill et al., 2004). Secondly, the finding that different self-report measures correlated in the hypothesised direction suggests that the questionnaires were being completed in a consistent manner across individuals.
In conclusion, this study demonstrated the close relationship between characteristics of anxiety and empathy deficits in ASD. Further, it suggests that underlying trait anxiety is likely to be an important contributing factor in the deficits in cognitive empathy seen in individuals with ASD. Relating these results to the model of empathy presented in Chapter 1 (Fig. 1.2), self-regulation of emotions (e.g., anxiety) is an important part of being able to empathise, especially at a perspective taking level. It may be the case that increased anxiety as well as poor emotion-regulation in individuals with ASD causes these difficulties in cognitive empathy, specifically perspective taking.
There are well-established, empirically supported treatments for anxiety in general and social anxiety in particular. These findings suggest, therefore, that there is a clear direction for ameliorating problems with empathy in people with ASD. Assessing and treating anxiety 119 should therefore be a serious consideration when working with empathy impairments in individuals with ASD.
120
CHAPTER 7
GENERAL DISCUSSION
The primary aim of this thesis was to explore the nature of empathy in individuals with ASD. Empathy is a multifaceted and complex social construct with many overlapping but distinct components. While, it has been well established that empathy involves both cognitive and emotional elements, there has been some debate among researchers as to whether these can be separated experimentally. While individuals with ASD have been shown to be impaired in cognitive empathy tasks, such as mentalising and theory of mind, their ability to experience and display emotional empathy is much less clear. Thus, this thesis examined whether adults with high-functioning ASD experience and respond with emotional empathy when viewing “real world” emotionally driven video stimuli. In order to gain a complete picture of responding in this population, both objective measures such as physiological recordings of bodily responses and subjective measures such as self-report of own experiences were used, as well as self-report of general levels of trait empathy.
The second aim of this thesis was to explore a concept strongly associated with empathy, social motivation. In order to examine social motivation, two constructs were explored: social exclusion and anxiety, both of which are commonly experienced and reported by individuals with ASD. Despite the difficulties individuals with ASD have with empathy, their experience of social rejection or exclusion is unknown. Therefore, this thesis aimed to induce feelings of social exclusion in order to explore the experiences and responses to ostracism in this population as well as explore whether these individuals are motivated to engage socially. Further, given the high rates on anxiety, especially anxiety of a social nature, in people with ASD, it was of interest to determine whether anxiety, with its attendant effects on both cognition and self-centredness would be associated with both impaired performance 121
on a theory of mind task as well as reduced self-report empathy. The findings of the four studies are outlined below.
In order to explore whether adults with ASD experienced emotional empathy in response to seeing another person in an emotional situation, Study 1 was designed to measure physiological and psychological responses to viewing people in emotionally distressing situations compared with neutral situations. The results of this study indicated a dissociation between self-report responding of emotion and physiological emotional response
via skin conductance and EMG in the ASD group. While, relative to the Control Group the
ASD group reported muted changes to their mood in response to viewing people in
emotionally distressing situations, their level of arousal and facial affect was comparable,
indicating normal physiological responding. In terms of the relationships between
physiological and psychological responding overall, those who rated the clips as more
distressing (increased negative mood ratings) responded with greater physiological arousal,
consistent with previous research (e.g., Fernandez et al., 2012). Within the ASD group, level
of self-reported arousal was associated with trait empathy as measured by standardized
empathy questionnaires, such that those with lower levels self-reported arousal also reported
lower levels of Empathic Concern.
Overall, Study 1 suggested that the ASD group had relatively normal physiological
changes in response to emotional stimuli and were also reasonably self-aware of those
changes. Despite this they demonstrated a muting of their subjective appraisal of their own
emotional responses. However, given the stimuli in Study 1 were complex and social, and
potentially tapped into cognitive empathy such as theory of mind, the results may be
confounded by demand on these cognitive resources. Consequently, Study 2 examined
emotional empathic response to pain. 122
Empathy for pain involves both an automatic sensorimotor, mimicry-like response, subjective appraisal of that response and emotion regulation in order to demonstrate appropriate empathic behaviour. Results of this study yielded no clear effects in either ASD or controls in terms of sensorimotor mimicry. However, there was some evidence of reduced emotional contagion in the form of arousal, when people with ASD viewed a painful stimulus being applied to another person’s hand although heart rate did not differ between groups.
Individuals with ASD also rated the painful stimuli as less painful and less unpleasant than controls. This lower arousal level to the painful stimuli does seem to imply a lack of engagement with the pain of others. However, no correlation was found between physiological responses and subjective responses within the ASD Group. Consistent with previous research, individuals who rated themselves as having more trait empathy responded with greater arousal and higher ratings of pain and unpleasantness to viewing painful stimuli compared with non-pain stimuli. Thus Study 2, like Study 1, suggested that subjective appraisal of emotion in response to emotional stimuli were somewhat aberrant in the ASD group. In addition, and in contrast to Study 1, Study 2 revealed some differences in emotional contagion, although no correlation was found between this and subjective appraisal.
Study 3 took a change of focus by looking at social motivation in ASD, given that social motivation is also important to empathy. It employed a well-established paradigm in order to induce feelings of ostracism in individuals with ASD and matched controls. Social exclusion was manipulated using the Cyberball task in which participants are either included or excluded from an online ball toss game. This study found that high-functioning adults with
ASD are able to interpret and recognize ostracism when it occurs. Further, they responded with increased levels of physiological arousal, as measured by skin conductance, when they were excluded from the game compared with controls. This increase in arousal was also maintained for longer than controls, indicating poorer emotion regulation. The ASD group 123 reported that their social needs (belonging, control, self-esteem, meaningful existence) were not met when they were excluded, as did the control group. However, the groups differed on level of self-reported mood, with the ASD group reporting lower overall mood responses.
Overall Study 3 suggested that people with ASD were socially motivated akin to the control group, with some possible impairment of emotion regulation in response to exclusion.
However, they were found to differ in their interpretation of the effects of ostracism on mood.
Finally, Study 4 explored the relationship between anxiety and empathy and whether anxiety plays a role in impairing one’s ability to empathise or mentalise. Using a well- established measure of advanced theory of mind, the Faux Pas test, the group of individuals with ASD in this study was able to identify and describe a faux pas when it occurred.
However, they were impaired at explaining the emotional implications of the faux pas.
Further, this impairment was associated with reduced self-report emotional empathy scores.
Consistent with previous research, the ASD group rated themselves as having significantly greater anxiety, both trait and social. This increased anxiety was associated with reduced self- reported trait cognitive and emotional empathy. Only increased trait anxiety predicted poorer self-reported empathy (both cognitive and emotional). However, neither trait nor social anxiety predicted theory of mind performance using the Faux Pas. These results highlight the detrimental effects of anxiety in being able to empathise with others, both on a cognitive perspective taking level as well as on an emotional level.
What does this mean for Emotional Empathy in ASD?
Results from the first two studies of this thesis indicate various levels of empathic ability in adults with ASD. 124
Emotion contagion empathic responses were found to be mixed. Individuals with
ASD demonstrated reduced differences in skin conductance of pain relative to non-pain stimuli compared with controls, indicating a dampening of emotional arousal or response to painful stimuli. However, when viewing emotionally driven stimuli in which a person is depicted being involved in a traumatic motor-vehicle accident or incident, individuals with
ASD appeared to respond with similar levels of skin conductance as controls. These conflicting results may be partly due to the nature of the stimuli used in each of these studies.
While the empathy for pain paradigm used in Study 2 involved limited social information and no verbal language, the emotional videos employed in Study 1 were much more interactive and involved both social interaction and language. One explanation for these differences in arousal may be that other processes are involved in producing an emotional response to the emotional video clips in Study 1. For example, the stimuli used in Study 1 showed scenes in which facial expressions, verbal interaction and body language were all involved in demonstrating emotions such as distress, anger, fear and sadness. Many studies have shown that individuals with ASD, especially high-functioning adults, are as able as controls to recognise and identify emotions through facial expressions. Furthermore, typical autonomic responses have also been demonstrated to emotional social stimuli, such as in the IAPS database (Mathersul et al., 2013a). As the pain paradigm did not include any of this social information, it may be the case that individuals with ASD were less able to identify with the observed model or use other emotional processing mechanisms to induce an emotional response.
Another difference between the stimuli used in both the first two studies was the length of time each video clip was displayed. While the pain paradigm used short 3-second clips, the videos used in Study 1 were 30 seconds each, and only that of the last 10 seconds was used in the analyses. It may be the case that individuals with ASD take longer to process 125
information and therefore longer to respond, even physiologically. Indeed many studies have demonstrated that people with ASD do develop social cognitive skills, however, they are developed at a much slower rate (Roeyers & Demurie, 2010). Furthermore, adults with ASD have been shown to improve on empathic accuracy and mentalising skills when social interactions are more structured (Ponnet, Buysse, Roeyers, & De Clercq, 2008). This perhaps highlights the additional role of executive control and mental flexibility in mindreading ability, which has shown to be impaired in children with ASD (Decety & Meyer, 2008).
Heart rate and corrugator responses were also mixed between Study 1 and Study 2.
Whilst heart rate was not measured in Study 1, findings from Study 2 indicate “normal” autonomic response to pain stimuli compared to non-pain stimuli in ASDs compared to controls. This finding is inconsistent with the skin conductance results and may be a consequence of low statistical power. Evidence of this comes from Figure 3.3 in which it can be seen that pain-apple and pain-static differences are larger for the control group, but are not evident at all in the ASD group. Emotional expression, as measured by corrugator EMG, was typical in individuals with ASD in response to both emotional video scenes as well as painful stimuli. Interestingly, although not significant, Figure 3.4 shows a slight increase in pain relative to no-pain response in corrugator activation in the ASD group compared with the control group. The reason for this is uncertain. It is possible that in the pain study (Study 2) those with ASD were more perplexed or confused by the task (resulting in furrowing of the brows) or that there was an expectancy of which stimulus was next in the sequence. The finding of a significant correlation between the IRI-EC score and corrugator activity to the emotional videos in Study 1 in the ASD group is consistent with this explanation, suggesting those with lower self-reported emotional empathy were again, more puzzled by the emotional videos than those with higher levels of empathy. 126
In relation to subjective self-report responses to painful stimuli, the ASD group reported lower ratings of intensity and unpleasantness of pain than controls. This may be a result of the degree to which an emotional response was induced. If individuals with ASD do not feel as upset or distressed when viewing painful stimuli, they are likely to find the pain stimuli less intense and unpleasant. In the emotional videos however, participants with ASD rated the distressing scenes to the same degree as controls did in terms of self-reported arousal level. However, they differed from controls on the level of mood reported. Ratings by the ASD group appeared to show a flattening or dulling of mood response to the emotional clips. This may be a reflection of difficulties interpreting or identifying own mood, as is common in people with ASD (Bird et al., 2010).
Taken together, it may be interpreted that people with ASD are better able to empathise with others and experience emotion contagion when in situations which are rich in context and emotional information, so that they are able to rely on other processes in order to engage with the situation. Whilst these individuals appear to have normal emotion contagion via autonomic responses (skin conductance), this is dependent on their level of engagement in the situation. Skin conductance levels may also be seen as an indication of engagement or attention towards the stimulus. In light of this, it may be hypothesised that in Study 2, individuals with ASD were less engaged with the task as they found the pain both less intense and less unpleasant and therefore skin conductance was reduced to pain stimuli compared with controls. However, in Study 1 in which social information was richer, individuals with
ASD were more able to engage with the scenes and therefore skin conductance was
“normal”. It may be the case that individuals with ASD, whilst able to correctly identify physiological responses (i.e., arousal response), they are unable to interpret the meaning of this response in terms of emotional valence. This ties in with Bermond’s (1997) proposal of two distinct types of alexithymia: Type I, characterized by an absence of emotional 127
experience and Type II, characterized by a normal or high degree of emotional arousal along
with a low degree of accompanying cognitions (Larsen et al., 2003). The general pattern of
performance of the ASD group in this thesis appears to fall within the Type II category,
displaying difficulties explaining emotions, rather than their absence. However, reduced
arousal responses to pain stimuli are more compatible with Type I, suggesting that the ASD
group may consist of both types of alexithymia, highlighting the heterogeneity within this
clinical population.
Together these findings may be interpreted with the help of previous research on self-
awareness in ASD. As is outlined in the model in Chapter 1 of this thesis, Figure 1.2, self-
awareness, whilst separate from empathy, is an important interrelated construct. Self-awareness
or metacognition can be thought of as theory of one’s own mind. Indeed there are researchers
who propose that theory of mind and metacognition occur as part of one unitary process: that
the ability to recognise mental states in oneself depends on the same psychological mechanism
or process as recognising mental states in others (Carruthers, 2009; Frith & Happe, 1999) and
that individuals with ASD are therefore impaired at both. However, other theorists believe these
two processes are separate and that whilst individuals with ASD are impaired at recognising
mental states in others, self-awareness (of mental states) is intact in autism (Nichols & Stich,
2003). More recently Williams (2010) provided a review of literature surrounding this debate, and while the mechanisms behind theory of mind and self-awareness remain unclear, it is evident that individuals with ASD do demonstrate clear impairments in self-awareness of own mental states. Also supporting this, Farley et al. (2010) found that adolescents with ASD demonstrated difficulties with self-conceptualising relating to agency, that is, the formation, influences and control of the self, whereas self-awareness relating to distinctiveness (being separate from others) or continuity (awareness of self over time) were intact. This level of self-
conceptualisation relies more strongly on an imaginative and subjunctive style of reasoning, 128 which are known to be disrupted in ASD (Craig & Baron-Cohen, 1999). Whilst high- functioning individuals with ASD appear to be able to conceptualise the self in a concrete, physical way, they find it more difficult to imagine the self in an abstract way. An example of this sort of question is “How might you become different”, which requires the participant to engage in imaginative abstract thinking.
Difficulties in self-awareness may also help to explain inconsistencies in the literature regarding self-report measures of emotional and cognitive empathy, such as the IRI. The extent to which emotional empathy is affected by ASD is complicated by the suggestion that emotional empathy as measured by the Empathic Concern subscale of the IRI and the EQ is not purely emotional. Aaron et al. (2015) suggest that both Perspective Taking and Empathic
Concern as measured by the IRI measure what they refer to as “mature empathy”, or higher- order empathic processing. In both the attentional focus is on the other person, rather than the self. In this regard, Empathic Concern involves some level of cognitive processing, in that one is required to have an understanding of what the other person is thinking or feeling in order to have concern for them. This suggests that the Empathic Concern subscale of the IRI also taps cognitive empathy. Similar suggestions have been made regarding the EQ and other forms of self-report empathy measures (Lawrence et al., 2004). In one recent review of empathy measures by Baldner et al. (2014), six separate factors of empathy were identified. Baldner et al. also suggested that the Empathic Concern subscale of the IRI closely correlates with measures of sympathy. Indeed, even the cognitive empathy subscales from both the EQ and IRI did not map onto the same factor. This conflation of types of empathy in the IRI and other empathy scales reflects the controversy regarding whether cognitive and emotional empathy can really be separated. Whilst it is generally agreed that empathy does involve both a cognitive component and an emotional component, it is very difficult to separate the two components with different items in a self-report questionnaire. 129
As the scenes depicted in both Study 1 and Study 2 involved watching people on a
computer screen, there is an element of feeling removed or detached from the stimuli. It is
widely recognised that cognitive empathy, or perspective taking, is impaired in people with
ASD, as has also been demonstrated in questionnaire responses on the Perspective Taking
subscale in Study 2 and Study 4. Whilst video scenes are more generalisable to the real world
than static images, the characters in the video stimuli are not familiar to the subjects.
Individuals with ASD may have greater difficulty taking the perspective of unfamiliar people
or when stimuli are presented in a more detached manner (such as via a screen), as opposed
direct observation. Evidence supporting this explanation comes from neurophysiological
studies using fMRI measures. Schulter-Rüther et al (2014) demonstrated increased activation in inferior parietal cortex (IPC) in participants with ASD, an area implicated in self- referential processing (Lou et al., 2004). Schulter-Rüther et al. suggest that this increase in
activation may reflect that adults with ASD develop a different strategy for assessing their
own emotion. They suggest that people with ASD may experience an enhanced distinction
between self- and other-perspective and that this may contribute to reduced ability for
showing a contagious emotional response in favour of a more cognitively biased
understanding of the observed emotion (Schulte-Ruther et al., 2014).
Ratings of the pain stimuli in Study 2 by the ASD group demonstrated comparable ratings of distress and arousal to those in the control group, but lower ratings of intensity and
unpleasantness of the pain compared to controls. The first two questions relate to evaluations about the self, whereas the last two questions relate to evaluations about the pain or the
observed stimuli. This suggests that people with ASD are able to report on their own
subjective experiences resulting from the stimuli, but have more difficulty with reporting on
how the other person felt, i.e., perspective taking. This is consistent with the bulk of previous
literature indicating poor mentalising or perspective taking ability in those with ASD, but 130
intact ability to report on own feelings (Dziobek et al., 2008). Whilst these results are
somewhat contradictory to the results in Study 1, which found that ASD participants rated
less negative mood to emotionally distressing stimuli despite increased arousal, they did
experience and report increased arousal to the stimuli. Again, the mood response may have
been dampened due to an exaggerated separation between self- from other-perspectives, leading to reduced experience of emotional contagion. This explanation is in opposition to the work on alexithymia which has shown that individuals high in alexithymic traits have difficulty separating own feelings from those of another. As the ASD group in these two studies (Study 1 and 2) were well able to separate their own feelings from those of the observed person or “other”, it would suggest that alexithymia is not the explanation, or that, at least, there is variability in alexithymic traits and how these manifest within the ASD population.
What does this mean for Social Motivation in ASD?
The social motivation theory of autism has also long been explored and is often thought of as contradictory to the theory of mind theory. It is believed that early-onset impairments in social attention impedes adequate social learning experiences and that the resulting imbalance in attending to social and non-social stimuli disrupts social skills and social cognitive development (Chevallier, Kohls, Troiani, Brodkin, & Schultz, 2012).
Findings from Study 3 and Study 4 highlight the importance of social motivation in the empathy process. Study 3 employed a well-established task inducing feelings of ostracism and social exclusion in order to study social motivation in adults with ASD. Results indicated that those with ASD responded with greater arousal levels and showed slower habituation times to this emotional response to ostracism. These findings show that adults with ASD are 131
motivated to engage in a virtual social activity, and exhibit emotional distress when they are
subsequently rejected from that social experience.
Research demonstrating decreased social motivation in ASD has tended to focus on diminished pleasure or reward gain from social involvement. Chevallier et al. (2012) found
that children with high functioning autism reported diminished enjoyment in social situations,
but not physical or other sorts of pleasure (intellectual, achievement). Similarly, in a study
examining the relationship between autistic traits and social reward in the general population,
Foulkes et al. (2015) found that autistic traits were associated with lower levels of enjoyment
of admiration and sociability. Carre et al. (2016) also confirmed that adults with ASD reported themselves as “socially anhedonic” (lack of experienced pleasure in social situations). However, this study also found that ASD was related to high levels of negative affectivity and low levels of positive affectivity, highlighting the comorbidity of anxiety and depression in this population (Hofvander et al., 2009).
More recently it has been shown that the nature of the social stimuli has an impact on
social response in ASD (Chevallier et al., 2015). Using eye-tracking equipment, Chevallier et
al. found that differences in social attention among individuals with ASD were only apparent
when participants watched dynamic social stimuli depicting interaction, whereas static and
dynamic stimuli did not show this difference. These results highlight the importance of using
more realistic interactive stimuli in research in autism due to the generalizability to real life
social interactions. Similarly, Chen et al. (2016) also found evidence to suggest that
cognitively able adolescents and adults with ASD were motivated to interact in situation
where they felt competent and experienced social reciprocity. Emotional considerations such
as mood and anxiety may play a role in one’s desire to approach or avoid social interactions.
If one is confident and experienced, there is less likely to be social anxiety or insecurity and
one is more likely to seek out social contact and be able to maintain social interaction. 132
Conversely, if one is socially anxious, as many individuals with ASD are (S. W. White, T.
Ollendick, & B. C. Bray, 2011a), they are more likely to perceive social activities as difficult and therefore avoid these activities. Study 4 confirmed this heightened experience of social anxiety, and trait anxiety in general, in the ASD group. Furthermore, this ASD group was a highly cognitively able group, with mean IQ scores in the High Average range, increasing the likelihood of conscious awareness of being “different”. Furthermore, the ASD participants in these studies were able to identify and understand social exclusion when it occurred. Many children and adults with ASD experience increased anxiety when interacting with peers
(Bellini, 2006) as well as increased cortisol levels (indicative of a stress response) when socially interacting with unfamiliar peers during play conditions (Corbett, Schupp, Simon,
Ryan, & Mendoza, 2010). It may be argued then that anxiety, specifically social anxiety, plays a role in reduced social motivation seen in ASD. Supporting this theory, Corbett et al.
(2014) found that children with ASD exhibited diminished social motivation only when especially asked or prompted, but not when engaging in “free play”. Further, significant associations were observed between the children’s level of social engagement and cortisol levels, with higher cortisol response being associated with reduced verbal exchange and social engagement. This study also fits in with results found in Study 3. Whilst verbal exchange was not necessary in the Cyberball game, participants with ASD demonstrated greater skin conductance throughout both games (inclusion and exclusion) in which social engagement was necessary, suggesting increased arousal or stress response.
Anxiety, more specifically social anxiety, has been shown to have detrimental effects on the development of social skills (Rubin et al., 2010) and interferes with theory of mind ability (Hezel & McNally, 2014). This was also demonstrated in Study 4. Whilst the participants in the ASD group in this research were particularly high-functioning (mean IQ >
110), they reported significant levels of social and trait anxiety and performed significantly 133
worse on the explanation item of the Faux Pas Test indicating that whilst they could identify
and understand the faux pas made by the character in the story, they had difficulty describing
why the faux pas was awkward. Anxiety clearly plays an important, yet detrimental, role in
one’s motivation to engage and succeed in social interaction. Supporting this, Swain et al.
(2015) demonstrated a direct effect of social motivation on social anxiety, such that poorer social motivation was associated with higher social anxiety.
To summarise these findings, first, these results highlight the multiple layers of
emotional empathy, from bottom-up automatic sensorimotor responding to the top-down
subjective experience of emotional feelings, such as sympathy. These various levels appear to
be processed to various degrees in adults with ASD. While results regarding sensorimotor
contagion were inconclusive, the autonomic emotional response, as measured by skin
conductance appeared to be intact when the individual is engaged in the task. When the person is unable or unmotivated to engage with the stimulus, emotional contagion is impaired. At the high-order reasoning level of emotional empathy – that of subjective experience, individuals with ASD are able to report on their own experience of arousal, however, mood did not seem as greatly affected as in controls. Further, whilst individuals with ASD are able to identify their own experience, they are less affected when reporting on another’s experience. This suggests some difficulty introspecting about the meaning of emotional changes in oneself and a possibly exaggerated detachment between self and other.
Study 3 demonstrated that adults with ASD are motivated to engage in social interactions and experienced significantly greater and more enduring distress when they are rejected or ostracised from that interaction highlighting a possible role for poor emotion regulation.
Finally, social motivation was found to be affected by feelings of anxiety, specifically social anxiety, which negatively affected social understanding and theory of mind. 134
Relating this back to the model presented in Chapter 1 (Figure 1.2), findings from this thesis highlight the multilayered process of emotional empathy from bottom-up automatic responses such as mimicry and emotion contagion to top down processes such as subjective experience, self-reflection, emotion regulation and sympathy. The four studies in this thesis were unable to reveal conclusive, straightforward explanations regarding the nature of emotional empathic deficits in people with ASD, however, they did point to difficulties in
“higher level” processing more so than bottom up contagion. Individuals with ASD appeared to have “normal” emotional empathic responses at a physiological level, provided they were socially motivated and engaged, suggesting that emotional empathy at an automatic level requires individuals with ASD to be socially motivated to engage in the task or situation.
However, their interpretation and subjective experience of this emotion was sometimes reduced or blunted and their ability to self-regulate when stressed was compromised. Further, anxiety played a significant role in both social motivation and empathic ability. Poor self- regulation of anxiety and distress (Study 3 and 4) appears to impair cognitive empathy and perspective taking in people with ASD. Although alexithymia has been invoked as a potential explanation for some of these difficulties, it does not provide a simple explanation.
Alexithymia, in itself, is a disputed construct and may well constitute a number of disorders, ranging from those who lack physiological responses to emotional events to those who have normal responses but cannot describe them. The work in this thesis revealed a pattern more in keeping with the latter. However, the seemingly large divide between feelings for self and other, as revealed in Study 2, does not fit with research suggesting the opposite in alexithymic individuals.
While alexithymia is more common in individuals on the Autism Spectrum, it has been found to be present in approximately half of people with ASD (Bernhardt et al., 2014).
While these rates are higher than that of the general population, alexithymia is by no means 135
present in all individuals with ASD. Again, his highlights the nature of heterogeneity within
the Autism Spectrum. As previous research has found evidence suggesting that it is various
levels, as well as various types (i.e., Type I or Type II) of alexithymia, rather than ASD
diagnosis, that is associated with empathy abnormalities (Bird et al., 2010). This may help to
explain the variability in empathic responding in those with ASD in this thesis, as well as in
the larger population of individuals on the Autism Spectrum.
Clinical Implications for Understanding and Treating Social and Emotional Difficulties
in ASD
The findings presented in this thesis highlight important considerations for the
understanding and intervention of difficulties experienced by adults with ASD. Whilst the
bottom-up aspects of emotional empathy appear to be intact in adults with ASD, the top-
down processing of these responses is impaired. Furthermore, research has shown that adults
with ASD can improve on various measures of social cognition when given greater structure
and more guidance (Ponnet et al., 2008). This has significant implications for clinical
intervention. It may be possible to teach and train children and adolescents with ASD to
mentalise using more structured stimuli. In the last few years, new theory of mind intervention programs have been developed and tested with children with ASD with some promising results. For example, Beeger et al. (2015) demonstrated improved basic theory of mind in children with ASD following an 8-session group intervention program. While these
children improved on simple theory of mind tasks (e.g., false-belief tasks), mentalising on
others’ emotions and intentions did not improve. Similarly, Paynter and Peterson (2013) also
showed improvements on false-belief tasks in children following a theory of mind
intervention program and these improvements maintained for three weeks following the end 136
of treatment phase. Additionally, when paired with social skills training, Waugh and Peskin
(2015) found improved theory of mind along with increased social responsiveness,
specifically in areas of social communication and social motivation.
Second, it may be possible to teach children and adolescents to infer own emotions by teaching interoceptive awareness skills. Individuals with ASD have been shown to be poor at
this compared with controls (DuBois et al., 2016; Garfinkel et al., 2016). ASD has been
shown to be associated with disrupted emotion processing resulting in diminished accuracy
with which internal bodily sensations are detected impeding information from informing
emotional judgements (Garfinkel et al., 2016). Interoceptive awareness skills are often taught
in children and adults with anxiety sensitivity and may be adapted to individuals on the autism spectrum. Further, people who have accurate interoceptive awareness are also more sensitive to the emotions of others when viewing emotional facial expressions (Terasawa et al., 2014), suggesting that if one can be trained to improve one’s own interoceptive awareness, it may lead to improved emotion perception and sensitivity of emotions in others.
The treatment of anxiety, especially social anxiety, may also help to improve
cognitive empathy and mentalising in ASD. Study 4 demonstrated a relationship between
more complex social cognition and social anxiety in participants with ASD in that those with
higher social anxiety had more difficulty with a complex theory of mind task. It therefore
makes sense that by improving social anxiety, theory of mind and mentalising may also be
improved. Furthermore, given the high levels of distress following brief exposure to feelings
of ostracism, emotion regulation skills are vital. Emotion regulation has been consistently
found to be impaired in individuals with ASD, often leading to greater experiences of anxiety
and depression (Swain et al., 2016). Recently, one study has demonstrated improved
interoceptive awareness individuals who had undergone an intensive 3-month bodily focused
contemplative intervention that consisted of a “Body Awareness” and “Body Scan” technique 137
(Bomemann et al., 2015). These participants reported significantly improved ratings on three
indices which, together, can be described as the regulatory aspects of interoceptive
awareness, in that participants deliberately focus on their body in order to regulate emotion,
attention and gain insight about their emotional-motivational states. While this treatment was
targeted at individuals without emotional or neurodevelopmental disorders, it may be possible
to apply these same intervention strategies to clinical populations such as ASD with some
adaptations.
Given the overlap between many of the social cognition and communication
impairments in ASD, the inclusion of multiple skills and strategies into one comprehensive
intervention program may be more effective. Recently, the PEERS program has been
implemented and tested in both adolescents (Schoh et al., 2014) and adults (McVey et al.,
2016) with ASD. This program focuses on friendships in order to develop social skills for
youth and young adults on the Autism Spectrum. Results demonstrate support for the efficacy
of such programs in that they improve a number of indices measured including social skills,
self-reported empathy, retention of skills, increased number of social interactions and reduced
social anxiety (McVey et al., 2016). These results are extremely promising in that they
combine a number of interrelated skills which have positive flow-on effects to other areas of social cognition and interaction.
A third consideration highlighted by these findings is the importance of using more realistic “real world” stimuli in both research and treatment. Individuals with ASD have been shown to have difficulties relating to, and identifying with, scenes from movies or books, as measured by the Fantasy subscale of the IRI (Demurie et al., 2011; Hirvela & Helkama,
2011). Further, Chevallier et al. (2015) demonstrated “normal” social motivation in children and adolescents with ASD only when the stimulus was interactive and not when the stimuli were static or dynamic. This finding is also consistent with results reported in Study 1 and 138
Study 2 of this thesis, in that participants with ASD were more able to engage with real life
scenes of car accidents versus watching a brief 3-second video of a needle penetrating a
disembodied hand. By using more attention-grabbing, interactive real world stimuli, individuals with ASD are more likely to be engaged and motivated and therefore able to empathise with other subjects in the social interaction or stimuli.
There has been a recent increase in research exploring the use of virtual reality
technology to treat social anxiety and improve social skills with promising results, both in
populations with social anxiety disorder and ASD. Given that a high number of people with
ASD enjoy and spend large amounts of time on computers and electronic devices and have specific interests in technology (Goldsmith & LeBlanc, 2004), this may be an effective and
motivating approach to teach and apply social skills. Recent work has been conducted in
developing software and gaming devices for children and adults to teach social skills. Virtual
reality can provide a controlled and replicable environment in which specific skills can be
taught and tested in a dynamic manner. One virtual-reality social-cognition training program
was recently tested in children on the Autism Spectrum (Didehbani et al., 2016). This
program trained social cognition by using typical day-to-day social exchanges while interacting with a peer and being monitored by a trained “coach” clinician who would offer feedback throughout the interaction. After ten sessions of training, children with ASD improved on measures of affect recognition, theory of mind and analogical reasoning, a measure of executive functioning (Didehbani et al., 2016). This same program has also been shown to have significant improvements in emotion recognition and both verbal and non- verbal theory of mind tasks in adults with ASD (Kandalaft et al., 2013). Similarly, Lorenzo et al. (2016) showed a significant presence of more appropriate emotional behaviours when using an immersive virtual reality environment compared with the use of desktop virtual
reality programs, indicating the effectiveness of more engaging and interactive interventions. 139
The use of “serious games” that use game components such as storyline, long-term goals and
rewards, to create engaging learning experiences has also been trialled in ASD. Whyte et al.
(2015) highlight the need for cost effective and treatment effective interventions for children
on the Autism Spectrum. Given the high level of computer gaming that occurs in this
population, interventions harnessing this medium are likely to be both engaging and effective.
In a meta-analysis reviewing the effectiveness of various technology-based interventions for
ASD, Grynszpan et al. (2014) showed significant differences between groups who received technology-based interventions compared to control groups who did not. While results from each study were variable, in general, these interventions lead to improvements in social skills, emotion recognition and theory of mind tasks.
One final consideration is that of the nature of autism itself in ASD research. The autism spectrum is wide and variable, ranging from classic low-functioning autism with comorbid intellectual disability to the high-functioning more “socially awkward” individuals.
Indeed, Geschwind and Levitt (2007) describe this disorder as “the autisms” with a wide and heterogeneous group of features. While most research splits samples of ASD participants into high- versus low-functioning groups, there is still high variability within each group. This can have significant implications for the outcome of intervention studies. Specifically, many studies trialling the effectiveness of a certain intervention strategy, such as diet restriction
(Mulloy et al., 2010) or the use of weighted blankets (Gringras et al., 2014) to improve emotion regulation and negative behaviours in children with ASD, report statistical findings supporting the null hypothesis. However, there is often high statistical variability in this clinical sample. For example, Mathersul et al. (2013c) found greater variability in baseline resting arousal levels in ASD participants. Accordingly, the sample was split between those who had low resting arousal and those who had “normal” resting arousal and these groups differed significantly on measures of empathy and social cognition. 140
This also raises important questions about the mechanisms behind the characteristics
associated with the Autism Spectrum and developing interventions based on clusters of
specific symptoms and impairments rather than on diagnoses. For example, low arousal has
been shown to be associated with characteristics such as inattention (Hirstein, Iversen, &
Ramachandran, 2001). Conversely, higher levels of arousal have been shown to be associated
with comorbid anxiety (Chiu, Anagnostou, Brian, Chau, & Kushki, 2016; Corbett et al.,
2014) and pragmatic language impairments (Klusek, Martin, & Losh, 2013). Given these
individuals all cluster together under the diagnosis of ASD, conducting research with such a
heterogeneous group causes doubts about the generalizability and efficacy of this research.
Future research may be able to subgroup people with ASD based on neurobiological or
physiological functioning, such as arousal, in order to develop interventions that are suitable
for each subgroup.
Limitations and Future Directions
One concern specific to this thesis is the confirmation of diagnosis of ASD within the
adult population. The ASD sample used in this thesis was not confirmed using diagnostic
tools such as the ADI-R or ADOS for most participants in the ASD group. As this thesis
examined an adult ASD population, confirming diagnosis was difficult. Measures such as the
ADI-R have been found to be less reliable in adults, especially those at the higher functioning
end of the autism spectrum (Lord & Risi, 1998). Furthermore, interviewing parents of these
individuals in order to obtain developmental information is often impossible. While the AQ
was used as a confirmatory measure of diagnosis, it is only a trait measure.
Another limitation of this thesis was that alexithymia was not measured. Alexithymia
has been discussed throughout the thesis as a possible explanation for the findings that the 141
autistic group in this thesis responded with reduced or flattened subjective emotional
responses, while responding similarly to controls on measures of arousal and other
physiological responses. However, it is unknown what portion of individuals in the ASD
group had high alexithymic traits, and therefore no definitive conclusions can be drawn.
Future research in this area would benefit from measuring both alexithymia and Autism in
order to separate which components are associated with emotional responding and empathy.
Overall, this thesis has highlighted the multilayered nature of emotional empathy and
demonstrated specific areas of both impairment and “normal” functioning in adults with
ASD. Whilst people with ASD appear to have intact emotional contagion their subjective
interpretation of their own emotional response appears to be atypical or impaired. Further,
whilst high-functioning individuals demonstrate typical levels of social motivation, factors
such as anxiety and emotion dysregulation are associated with reduced empathic ability. One final consideration arising from this thesis is the heterogeneity of emotional and social cognitive functioning within this population.
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REFERENCES
Aaron, R., Benson, T., & Park, S. (2015). Investigating the role of alexithymia on empathic
deficits found in schizotypy and autism spectrum traits. Personality and Individual
Differences, 77(April), 215-220.
Abell, F., Happe, F., & Frith, U. (2000). Do triangles play tricks? Attribution of mental states
to animated shapes in normal and abnormal developement. Cognitive Development,
15, 1-16.
Asperger, H. (1944). Die Autistischen Psychopathen" im Kindesalter. Archiv für Psychiatrie
und Nervenkrankheiten, 117, 76-136.
Association, A. P. (2013). Diagnostic and statistical manual of mental disorders, 5th edition.
Washington, DC, USA: American Psychiatric Association.
Auyeung, K. W., & Alden, L. E. (2016). Social anxiety and empathy for social pain.
Cognitive Therapy and Research, 40(1), 38-45.
Avenanti, A., Bueti, D., Galati, G., & Aglioti, S. M. (2005). Transcranial magnetic
stimulation highlights the sensorimotor side of empathy for pain. Nature
Neuroscience, 8(7), 955-960.
Balconi, M., Bortolotti, A., & Crivelli, D. (2013). Self-report measures, facial feedback, and
personality differences (BEES) in cooperative vs. noncooperative situations:
Contribution of the mimic system to the sense of empathy. International Journal of
Psychology, 48(4), 631-640.
Balconi, M., Vanutelli, M. E., & Finocchiaro, R. (2014). Multilevel analysis of facial
expressions of emotion and script: Self-report (arousal and valence) and 143
psychophysiological correlates. Behavioral and Brain Functions Vol 10 Sep 2014,
ArtID 32, 10.
Baldner, C., & McGinley, J. (2014). Correlational and exploratory factor analyses (EFA) of
commonly used empathy questionnaires: New insights. Motivation and Emotion, 38,
727-744.
Barnes, L. L. B., Harp, D., & Jung, W. (2002). Reliability generalization of scores on the
Spielberger State-Trait Anxiety Inventory. Educational and Psychological Measures,
62, 603-618.
Baron-Cohen, S. (2004). The cognitive neuroscience of autism. Journal of Neurology,
Neurosurgery & Psychiatry, 75(7), 945-948. doi:10.1136/jnnp.2003.018713
Baron-Cohen, S. (2011). The autistic mind: The empathizing-systemizing theory Textbook of
autism spectrum disorders. (pp. 39-47): Arlington, VA, US: American Psychiatric
Publishing, Inc.
Baron-Cohen, S., Leslie, A. M., & Frith, U. (1985). Does the autistic child have a "theory of
mind"? Cognition, 21, 37-46.
Baron-Cohen, S., O'Riordan, M., Stone, V., Jones, R., & Plaisted, K. (1999). Recognition of
faux pas by normally developing children and children with Asperger syndrome or
high-functioning autism. Journal of Autism and Developmental Disorders, 29(5), 407-
418.
Baron-Cohen, S., Ring, H., Moriarty, J., Shmitz, P., Costa, D., & Ell, P. (1994). Recognition
of mental state terms: Clinical findings in children with Autism and a functional
neuroimaging study of normal adults. British Journal of Psychiatry, 165, 640-649.
Baron-Cohen, S., Ring, H., Wheelwright, S., Bullmore, E. T., Brammer, M., Simmons, A., &
Williams, S. (1999). Social intelligence in the normal and autistic brain: an fMRI.
European Journal of Neuroscience, 11, 1891-1898. 144
Baron-Cohen, S., & Wheelwright, S. (2004). The Empathy Quotient: An Investigation of
Adults with Asperger Syndrome or High Functioning Autism, and Normal Sex
Differences. Journal of Autism and Developmental Disorders, 34(2), 163-175.
doi:10.1023/b:jadd.0000022607.19833.00
Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Chubley, E. (2001). The autism-
spectrum quotient (AQ): Evidence from Asperger Syndrome/High-functioning
Autism, males and females, scientists and mathematicians. Journal of Autism and
Developmental Disorders, 31, 5-17.
Bartz, J. A., & Hollander, E. (2006). The neuroscience of affiliation: Forging links between
basic and clinical research on neuropeptides and social behavior. Hormones and
Behavior, 50(4), 518-528. doi:10.1016/j.yhbeh.2006.06.018
Bauminger, N., & Kasari, C. (2000). Loneliness and friendship in high-functioning children
with autism. Child Development, 71(2), 447-456.
Beadle, J., Paradiso, S., Salerno, A., & McCormick, L. (2013). Alexithymia, emotional
empathy, and self-regulation in anorexia nervosa. Annals of Clinical Psychiatry,
25(2), 107-120.
Bellini, S. (2004). Social Skill Deficits and Anxiety in High-Functioning Adolescents with
Autism Spectrum Disorders. Focus on Autism and Other Developmental Disabilities,
19(2), 78-86.
Bellini, S. (2006). The Development of Social Anxiety in Adolescents With Autism
Spectrum Disorders. Focus on Autism and Other Developmental Disabilities, 21(3),
138-145.
Bermond, B. (1997). Brain and alexithymia. In A. J. Vingerhoets, F. Bussel, & J. Boelhouwer
(Eds.), The (non)expression of emotions in health and disease (pp. 115-130). Tilburg,
The Netherlands: Tilburg University Press. 145
Bernhardt, B., Valk, S., Silani, G., Bird, G., Frith, U., & Singer, T. (2014). Selective
disruption of sociocognitive structural brain networks in autism and alexithymia.
Cerebral Cortex, 24(12), 3258-3267.
Berthoz, S., & Hill, E. L. (2005). The validity of using self-reports to assess emotion
regulation abilities in adults with autism spectrum disorder. European Psychiatry,
20(3), 291-298. doi:10.1016/j.eurpsy.2004.06.013
Berthoz, S., Lalanne, C., Crane, L., & Hill, E. (2013). Investigating emotional impairments in
adults with autism spectrum disorders and the broader autism phenotype. Psychiatry
Research, 208(3), 257-264.
Bibby, H., & McDonald, S. (2005). Theory of mind after traumatic brain injury.
Neuropsychologia, 43(1), 99-114. doi:10.1016/j.neuropsychologia.2004.04.027
Bird, G., Silani, G., Brindley, R., White, S., Frith, U., & Singer, T. (2010). Empathic brain
responses in insula are modulated by levels of alexithymia but not autism. Brain: A
Journal of Neurology, 133(5), 1515-1525. doi:10.1093/brain/awq060
Bird, G., & Viding, E. (2014). The self to other model of empathy: Providing a new
framework for understanding empathy impairments in psychopathy, autism and
alexithymia. Neuroscience and Biobehavioral Reviews, 47, 520-532.
Blackhart, G., Eckel, L., & Tice, D. (2007). Salivary cortisol in response to acute social
rejection and acceptance by peers. Biological Psychiatry, 75, 267-276.
Blair, J., Sellars, C., Strickland, I., Clark, F., & et al. (1996). Theory of mind in the
psychopath. Journal of Forensic Psychiatry, 7(1), 15-25.
doi:10.1080/09585189608409914
Blair, R. J. R. (2008). Fine cuts of empathy and the amygdala: Dissociable deficits in
psychopathy and autism. The Quarterly Journal of Experimental Psychology, 61(1),
157-170. doi:10.1080/17470210701508855 146
Bogdanov, V. B., Bogdanova, O. V., Gorlov, D. S., Gorgo, Y. P., Dirckx, J. J., Makarchuk,
M. Y., . . . Critchley, H. (2013). Alexithymia and empathy predict changes in
autonomic arousal during affective stimulation. Cognitive and Behavioral Neurology,
26(3), 121-132.
Bons, D., van den Broek, E., Scheepers, F., Herpers, P., Rommelse, N., & Buitelaaar, J. K.
(2013). Motor, emotional, and cognitive empathy in children and adolescents with
autism spectrum disorder and conduct disorder. Journal of Abnormal Child
Psychology, 41(3), 425-443.
Bora, E., Yucel, M., & Pantelis, C. (2009). Theory of mind impairment in schizophrenia:
Meta-analysis. Schizophrenia Research, 109, 1-9.
Bufalari, I., & Ionta, S. (2013a). The social and personality neuroscience of empathy for pain
and touch. Frontiers in Human Neuroscience, 7, 393.
Bufalari, I., & Ionta, S. (2013b). The social and personality neuroscience of empathy for pain
and touch. Frontiers in Human Neuroscience Vol 7 Jul 2013, ArtID 393, 7.
Calderoni, S., Fantozzi, P., Maestro, S., Brunori, E., Narzisi, A., Balboni, G., & Muratori, F.
(2013). Selective cognitive empathy deficit in adolescents with restrictive anorexia
nervosa. Neuropsychiatric Disease and Treatment, 9, 1583-1589.
Carpenter, M. (2011). Social cognition and social motivations in infancy. In U. Goswami
(Ed.), The Wiley-Blackwell handbook of childhood cognitive development (2nd ed.).
(pp. 106-128): Wiley-Blackwell.
Carruthers, P. (2009). Mindreading underlies metacognition. Behavioral and Brain Sciences,
32(2), 164-182. doi:10.1017/s0140525x09000831
Castelli, F., Frith, C., Happé, F., & Frith, U. (2002). Autism, Asperger syndrome and brain
mechanisms for the attribution of mental states to animated shapes. Brain: A Journal
of Neurology, 125(8), 1839-1849. doi:10.1093/brain/awf189 147
Chen, C., Hung, A.-Y., Fan, Y.-T., Tan, S., Hong, H., & Cheng, Y. (2016). Linkage between
pain sensitivity and empathic response in adolescents with autism spectrum conditions
and conduct disorder symptoms. Autism Research, No Pagination Specified.
Chevallier, C., Grezes, J., Molesworth, C., Berthoz, S., & Happe, F. (2012). Brief report:
Selective social anhedonia in high functioning autism. Journal of Autism and
Developmental Disorders, 42(7), 1504-1509.
Chevallier, C., Kohls, G., Troiani, V., Brodkin, E. S., & Schultz, R. T. (2012). The social
motivation theory of autism. Trends in cognitive Sciences, 16(4), 231-239.
Chevallier, C., Parish-Morris, J., McVey, A., Rump, K. M., Sasson, N. J., Herrington, J. D.,
& Schultz, R. T. (2015). Measuring social attention and motivation in Autism
Spectrum Disorder using eye-tracking: Stimulus type matters. Autism Research, 8(5),
620-628.
Chiu, T., Anagnostou, E., Brian, J., Chau, T., & Kushki, A. (2016). Specificity of autonomic
arousal to anxiety in children with autism spectrum disorder. Autism Research, 9(4),
491-501.
Clark, D., & Wells, A. (1995). A cognitive model of social phobia. In R. G. Heimberg, M. R.
Liebowitz, D. A. Hope, & F. Schneider (Eds.), Social Phobia: Diagnosis, assessment
and treatment (pp. 69-93). New York: Guildford Press.
Corbett, B. A., Schupp, C., Simon, D., Ryan, N., & Mendoza, S. (2010). Elevated cortisol
during play is associated with age and social engagement in children with autism.
Molecular Autism, 1, 13.
Corbett, B. A., Swain, D. M., Newsom, C., Wang, L., Song, Y., & Edgerton, D. (2014).
Biobehavioral profiles of arousal and social motivation in autism spectrum disorders.
Journal of Child Psychology and Psychiatry, 55(8), 924-934. 148
Cox, C. L., Uddin, L. Q., Di Martino, A., Castellanos, F., Milham, M. P., & Kelly, C. (2012).
The balance between feeling and knowing: Affective and cognitive empathy are
reflected in the brain's intrinsic functional dynamics. Social Cognitive and Affective
Neuroscience, 7(6), 727-737.
Craig, J., & Baron-Cohen, S. (1999). Creativity and Imagniation in Autism and Asperger
Syndrome. Journal of Autism and Developmental Disorders, 29(4), 319-326.
Darke, S. (1988). Anxiety and working memory capacity. Cognition and Emotion, 2(2), 145-
154.
Davis, M. H. (1983). Measuring individual differences in empathy: Evidence for a
multidimensional approach. Journal of Personality and Social Psychology, 44(1),
113-126. doi:10.1037/0022-3514.44.1.113
Dawson, G., & Bernier, R. (2007). Development of social brain circuitry in autism. Coch,
Donna [Ed], 28-55. de Sousa, A., McDonald, S., & Rushby, J. (2012). Changes in emotional empathy, affective
responsivity, and behavior following severe traumatic brain injury. Journal of clinical
and experimental neuropsychology, 34(6), 606-623.
Decety, J., Jackson, P. L., & Brunet, E. (2007). The cognitive neuropsychology of empathy.
In T. Farrow & P. Woodruff (Eds.), Empathy in mental illness. (pp. 239-260): New
York, NY, US: Cambridge University Press.
Decety, J., & Meyer, M. (2008). From emotion resonance to empathic understanding: A
social developmental neuroscience account. Development and Psychopathology,
20(4), 1053-1080. doi:10.1017/s0954579408000503
Decety, J., & Moriguchi, Y. (2007). The empathic brain and its dysfunction in psychiatric
populations: Implications for intervention across different clinical conditions.
BioPsychoSocial Medicine, 1, 22. 149
Demurie, E., De Corel, M., & Roeyers, H. (2011). Empathic accuracy in adolescents with
autism spectrum disorders and adolescents with attention-deficit/hyperactivity
disorder. Research in Autism Spectrum Disorders, 5(1), 126-134.
doi:10.1016/j.rasd.2010.03.002
Derntl, B., Seidel, E.-M., Schneider, F., & Habel, U. (2012). How specific are emotional
deficits? A comparison of empathic abilities in schizophrenia, bipolar and depressed
patients. Schizophrenia Research, Oct 2012, No pagination specified.
Durlik, C., & Tsakiris, M. (2015). Decreased interoceptive accuracy following social
exclusion. International Journal of Psychophysiology, 96(1), 57-63.
Dziobek, I., Rogers, K., Fleck, S., Bahnemann, M., Heekeren, H. R., Wolf, O., & Convit, A.
(2008). Dissociation of cognitive and emotional empathy in adults with Asperger
Syndrome using multifaceted empathy test (MET). Journal of Autism and
Developmental Disorders, 38, 464-473.
Eisenberg, N., & Eggum, N. (2009). Empathic responding: Sympathy and personal distress.
In J. Decety & W. Ickes (Eds.), The Social Neuroscience of Empathy. Cambridge,
Massachusetts, USA: The MIT Press.
Ekman, P., & Friesen, W. V. (1976). Pictures of Facial Affect. San Francisco: CA:
Consulting Psychologists Press.
Elsabbagh, M., Divan, G., Koh, Y.-J., Kim, Y. S., Kauchali, S., Marcin, C., . . . Fombone, E.
(2012). Global prevalence of Autism and other Pervasive Developmental Disorders.
Autism Research, 5(3), 160-179.
Engert, V., Vogel, S., Efanov, S., Duchesne, A., Corbo, V., Ali, N., & Pruessner, J. (2011).
Investigation into the cross-correlation of salivary cortisol and alpha-amylase
responses to psychological stress. Psychoneuroendocrinology, 36(9), 1294-1302. 150
Falck-Ytter, T., Bölte, S., & Gredebäck, G. (2013). Eye tracking in early autism research.
Journal of Neurodevelopmental Disorders, 5(1), no pagination specified.
Fan, Y.-T., Chen, C., Chen, S.-C., Decety, J., & Cheng, Y. (2014). Empathic arousal and
social understanding in individuals with autism: Evidence from fMRI and ERP
measurements. Social Cognitive and Affective Neuroscience, 9(8), 1203-1213.
Farley, A., Lopez, B., & Saunders, G. (2010). Self-conceptualisation in autism. Autism : the
international journal of research and practice, 14(5), 519-530.
Fernandez, C., Pascual, J. C., Soler, J., Elices, M., Portella, M. J., & Fernandez-Abascal, E.
(2012). Physiological responses induced by emotion-eliciting films. Applied
Psychophysiology and Biofeedback, 37(2), 73-79.
doi:http://dx.doi.org/10.1007/s10484-012-9180-7
Freeth, M., Bullock, T., & Milne, E. (2012). The distribution of and relationship between
autistic traits and social anxiety in a UK student population. Autism : the international
journal of research and practice, 17(5), 571-581.
Fridlund, A., & Cacioppo, J. (1986). Guidelines for human electromyographic research.
Psychophysiology, 23, 567-589.
Frith, U., & Happe, F. (1999). Theory of mind and self-consciousness: What is it like to be
autistic? Mind and Language, 14(1-22).
Geschwind, D. H., & Levitt, P. (2007). Autism spectrum disorders: developmental
disconnection syndromes. Current Opinion in Neurobiology, 17, 103-111.
Goel, V., & Dolan, R. J. (2003). Explaining modulation of reasoning by belief. Cognition,
87(1), B11-B22. doi:10.1016/s0010-0277(02)00185-3
Golan, O., Baron-Cohen, S., & Hill, J. (2006). The Cambridge Mindreading (CAM) face-
voice battery: Testing complex emotion recognition in adults with and without 151
Asperger Syndrome. Journal of Autism and Developmental Disorders, 36(2), 169-
183.
Goldenfeld, N., Baron-Cohen, S., & Wheelwright, S. (2005). Empathizing and systemizing in
males, females, and autism. Clinical Neuropsychiatry: Journal of Treatment
Evaluation, 2(6), 338-345.
Goldsmith, T. R., & LeBlanc, L. A. (2004). Use of technology in interventions for children
with autism. Journal of Early and Intensive Behavior Intervention, 1(2), 166-178.
Goubert, L., Craig, K., & Buysse, A. (2011). Perceiving others in pain: Experimental and
clincal evidence on the role of empathy. In J. I. Decety, W. (Ed.), The Social
Neuroscience of Empathy (pp. 153-165). Cambridge, Massachusetts, USA: The MIT
Press.
Greenwald, M. J., Cook, E., & Lang, P. (1989). Affective judgements and
psychophysiological response: dimensional covariation in the evaluation of pictorial
stimuli. Journal of Psychophysiology, 3, 51-64.
Gringras, P., Green, D., Wright, B., Rush, C., Sparrowhawk, M., Pratt, K., . . . Wiggs, L.
(2014). Weighted blankets and sleep in autistic children - A randomized controlled
trial. Pediatrics, 134(2), 298-306.
Gu, X., Liu, X., Van Dam, N. T., Hof, P. R., & Fan, J. (2013). Cognition-emotion integration
in the anterior insular cortex. Cerebral Cortex, 23, 20-27.
Hagenmuller, F., Rossler, W., Wittwer, A., & Haker, H. (2014). Empathic resonance in
Asperger syndrome. Research in Autism Spectrum Disorders, 8(7), 851-859.
Happe, F. (1994). An advanced test of theory of mind: Understanding of story characters'
thoughts and feelings by able autistic, mentally handicapped and normal children and
adults. Journal of Autism and Developmental Disorders, 24(2), 129-154. 152
Happe, F., Ehlers, S., Fletcher, P., Frith, U., Johansson, M., Gillberg, C., . . . Frith, C. C.
(1996). "Theory of mind" in the brain: Evidence from a PET scan study of Asperger
syndrome. NeuroReport, 8, 197-201.
Hartgerink, C., van Beest, I., Wicherts, J., & Williams, K. (2015). The ordinal effects of
ostracism: A meta-analysis of 120 Cyberball studies. PLoS ONE, May, No pagination
specified.
Heerey, E., & Kring, A. (2007). Interpersonal consequences of social anxiety. Journal of
Abnormal Child Psychology, 116(1), 125-134.
Herbert, J., Bellack, A., & Hope, D. A. (1991). Concurrent validity of the Social Phobia and
Anxiety Inventory. Journal of Psychopathology and Behavioural Assessment, 13(4),
357-368.
Hess, U., & Blairy, S. (2001). Facial mimicry and emotional contagion to dynamic emotional
facial expressions and their influence on decoding accuracy. International Journal of
Psychophysiology, 40(1), 129-141.
Hesselmark, E., Eriksson, J. M., Westerlund, J., & Bejerot, S. (2015). Autism Spectrum
Disorders and self-reports: Testing validity and reliability using the NEO-PI-R.
Journal of Autism and Developmental Disorders, 45(5), 1156-1166.
Hezel, D. M., & McNally, R. J. (2014). Theory of mind impairments in social anxiety
disorder. Behavior Therapy, 45(4), 530-540.
Hill, E. (2004). Executive dysfunction in autism. Trends in cognitive Sciences, 8(1), 26-32.
Hill, E., Berthoz, S., & Frith, U. (2004). Brief report: Cognitive processing of own emotions
in individuals with autistic spectrum disorders and their relatives. Journal of Autism
and Developmental Disorders, 34(2), 229-235. 153
Hirstein, W., Iversen, P., & Ramachandran, V. (2001). Autonomic responses of autistic
children to people and objects. Proceedings of the Royal Society of London. Series B.
Biological Sciences, 268(1479), 1883-1888.
Hirvela, S., & Helkama. (2011). Empathy, values, morality and Asperger's syndrome.
Scandinavian Journal of Psychology, 52(6), 560-572.
doi:http://dx.doi.org/10.1111/j.1467-9450.2011.00913.x
Hofvander, B., Delorme, R., Chaste, P., Nyden, A., Wentz, E., & Stahlberg, O. (2009).
Psychiatric and psychological problems in adults with normal-intelligence autism
spectrum disorders. BMC Psychiatry, 9, 35.
Hollander, E., Kolevzon, A., & Coyle, J. T. (2011). Textbook of Autism Spectrum Disorders.
Washington, DC, USA: American Psychiatric Publishing Inc.
Hooker, C. I., Verosky, S. C., Germine, L. T., Knight, R. T., & D'Esposito, M. (2008).
Mentalizing about emotion and its relationship to empathy. Social Cognitive and
Affective Neuroscience, 3(3), 204-217. doi:10.1093/scan/nsn019
Hsee, C., Hatfield, E., Carlson, J., & Chemtob, C. (1990). The effect of power on
susceptibility to emotional contagion. Cognition and Emotion, 4(4), 327-340.
Iffland, B., Sansen, L., Catani, C., & Neuner, F. (2014a). Rapid heartbeat, but dry palms:
reactions of heart rate and skin conductance levels to social rejection. Frontiers in
Psychology, August, No pagination specified.
Iffland, B., Sansen, L., Catani, C., & Neuner, F. (2014b). The trauma of peer abuse: effects of
relational peer victimization and social anxiety disorders on physiological and
affective reactions to social exclusion. Frontiers in Psychiatry, 5(March), No
pagination specified. 154
Jackson, P. L., Brunet, E., Meltzoff, A. N., & Decety, J. (2006). Empathy examined through
the neural mechanisms involved in imagining how I feel versus how you feel pain.
Neuropsychologia, 44(5), 752-761.
Jackson, P. L., Meltzoff, A. N., & Decety, J. (2005). How do we perceive the pain of others?
A window into the neural processes involved in empathy. NeuroImage, 24(3), 771-
779. doi:10.1016/j.neuroimage.2004.09.006
Jackson, P. L., Rainville, P., & Decety, J. (2006). To what extent do we share the pain of
others? Insight from the neural bases of pain empathy. Pain, 125(1-2), 5-9.
Jolliffe, T., & Baron-Cohen, S. (1999). The Strange Stories test: A replication with high-
functioning adults with autism or Asperger syndrome. Journal of Autism and
Developmental Disorders, 29(5), 395-406.
Jones, A. P., Happé, F. G. E., Gilbert, F., Burnett, S., & Viding, E. (2010). Feeling, caring,
knowing: Different types of empathy deficit in boys with psychopathic tendencies and
autism spectrum disorder. Journal of Child Psychology and Psychiatry, 51(11), 1188-
1197. doi:10.1111/j.1469-7610.2010.02280.x
Kelly, M., McDonald, S., & Rushby, J. (2012). All alone with sweaty palms - Physiological
arousal and ostracism. International Journal of Psychophysiology, 83(3), 309-314.
Kessels, R. P. C., Spee, P., & Hendriks, A. W. (2010). Perception of dynamic facial
emotional expressions in adolescents with autism spectrum disorders (ASD).
Translational Neuroscience, 1(3), 228-232.
Kikuchi, Y., Senju, A., Tojo, Y., Osanai, H., & Hasegawa, T. (2009). Faces do not capture
special attention in children with autism spectrum disorder: A change blindness study.
Child Development, 80(5), 1421-1433. 155
Kleinhans, N. M., Richards, T., Weaver, K., Johnson, L., Greenson, J., Dawson, G., &
Aylward, E. (2010). Association between amygdala response to emotional faces and
social anxiety in autism spectrum disorders. Neuropsychologia, 48(12), 3665-3670.
Klusek, J., Martin, G. E., & Losh, M. (2013). Physiological arousal in autism and Fragile X
syndrome: Group comparisons and links with pragmatic language. American Journal
on Intellectual and Developmental Disabilities, 118(6), 475-495.
Kouchaki, M., & Wareham, J. (2015). Excluded and behaving unethically: Social exclusion,
physiological responses and unethical behaviour. Journal of Applied Psychology,
100(2), 547-556.
Kuusikko, S., Pollock-Wurman, R., Jussila, K., Carter, A. S., Mattila, M.-L., Ebeling, H., . . .
Moilanen, I. (2008). Social anxiety in high-functioning children and adolescents with
autism and Asperger syndrome. Journal of Autism and Developmental Disorders,
38(9), 1697-1709.
Kylliainen, A., Wallace, S., Coutanche, M. N., Leppanen, J. M., Cusack, J., Bailey, A. J., &
Hietanen, J. K. (2012). Affective-motivational brain responses to direct gaze in
children with autism spectrum disorder. Journal of Child Psychology and Psychiatry,
53(7), 790-797. doi:http://dx.doi.org/10.1111/j.1469-7610.2011.02522.x
La Greca, A., & Lopez, N. (1998). Social anxiety among adolescents: Linkages with peer
relations and friendships. Journal of Abnormal Child Psychology, 26(2), 83-94.
Lacroix, A., Guidetti, M., Rogé, B., & Reilly, J. (2009). Recognition of emotional and
nonemotional facial expressions: A comparison between Williams syndrome and
autism. Research in Developmental Disabilities, 30(5), 976-985.
Lang, P., Bradley, M., & Cuthbert, B. (1997). International Affective Picture System (IAPS):
Instruction manual and affective ratings, Technical report A-4. Gainsville, Florida,
USA: The Centre for Research in Psychophysiology. 156
Lang, P., Bradley, M., & Cuthbert, B. (1999). International affective picture system (IAPS):
Technical manual and affective ratings. Gainsville, Florida, USA: University of
Florida, Centre for Research in Psychophysiology.
Lang, P. J., Greenwald, M. J., Bradley, M., & Hamm, A. O. (1993). Looking at pictures:
affective, facial, visceral and behavioural reactions. Psychophysiology, 30, 261-273.
Larsen, J. K., Brand, N., Bermond, B., & Hijman, R. (2003). Cognitive and emotional
characteristics of alexithymia: A review of neurobiological studies. Journal of
Psychosomatic Research, 54(6), 533-541.
Law Smith, M. J., Montagne, B., Perrett, D. I., Gill, M., & Gallagher, L. (2010). Detecting
subtle facial emotion recognition deficits in high-functioning Autism using dynamic
stimuli of varying intensities. Neuropsychologia, 48(9), 2777-2781.
Lawrence, E. J., Shaw, D., Baker, S., Baron-Cohen, S., & David, A. S. (2004). Measuring
empathy: reliability and validity of the Empathy Quotient. Psychological Medicine,
34, 911-924.
Leary, M., & Baumeister, R. (2000). The nature and function of self-esteem: Sociometer
theory. Advances in Experimental Social Psychology, 32, 1-62.
Lockwood, P., Millings, A., Hepper, E., & Rowe, A. C. (2013). If I cry, do you care?
Individual differences in empathy moderate the facilitation of caregiving words after
exposure to crying faces. Journal of Individual Differences, 34(1), 41-47.
Lockwood, P. L., Bird, G., Bridge, M., & Viding, E. (2013). Dissecting empathy: High levels
of psychopathic and autistic traits are characterized by difficulties in different social
information processing domains. Frontiers in Human Neuroscience, 7, 760.
Lord, C., & Risi, S. (1998). Frameworks and methods in diagnosing Autism Spectrum
Disorders. Mental Retardation and Developmental Disabilities Research Reviews, 4,
90-96. 157
Louwerse, A., van der Geest, J., Tulen, J., van der Ende, J., van Gool, A., Verhulst, F., &
Greaves-Lord, K. (2013). Effects of eye gaze directions of facial images on looking
behaviour and autonomic responses in adolescents with autism spectrum disorders.
Research in Autism Spectrum Disorders, 7(9), 1043-1053.
doi:http://dx.doi.org/10.1016/j.rasd.2013.04.013
Lundqvist, L., & Dimberg, U. (1995). Facial expressions are contagious. Journal of
Psychophysiology, 9(3), 203-211.
Lustenberger, D. E., & Jagacinski, C. M. (2010). Exploring the effects of ostracism on
performance and intrinsic motivation. Human Performance, 23(4), 283-304.
doi:10.1080/08959285.2010.501046
Maehara, Y., & Saito, S. (2011). I see into your mind too well: Working memory adjusts the
probability judgment of others' mental states. Acta Psychologica, 138(3), 367-376.
Mahaffey, B. L., Wheaton, M. G., Fabricant, L. E., Berman, N. C., & Abramowitz, J. S.
(2013). The contribution of experiential avoidance and social cognitions in the
prediction of social anxiety. Behavioural and Cognitive Psychotherapy, 41(1), 52-65.
Mansell, W., Clark, D., Ehlers, A., & Chen, Y. (1999). Social anxiety and attention away
from emotional faces. Cognition and Emotion, 13(6), 673-690.
Maras, K., & Bowler, D. M. (2012). Brief report: Suggestibility, compliance and
psychological traits in high-functioning adults with autism spectrum disorders.
Research in Autism Spectrum Disorders, 6(3), 1168-1175.
Marshall, W. L., Hudson, S. M., Jones, R., & Fernandez, Y. M. (1995). Empathy in sex
offenders. Clinical Psychology Review, 15(2), 99-113. doi:10.1016/0272-
7358(95)00002-7
Mason, R. A., Williams, D. L., Kana, R. K., Minshew, N., & Just, M. A. (2008). Theory of
Mind disruption and recruitment of the right hemisphere during narrative 158
comprehension in autism. Neuropsychologia, 46(1), 269-280.
doi:10.1016/j.neuropsychologia.2007.07.018
Mathersul, D., McDonald, S., & Rushby, J. A. (2013a). Automatic facial responses to
affective stimuli in high-functioning adults with autism spectrum disorder. Physiology
& Behavior, 109, 14-22. doi:http://dx.doi.org/10.1016/j.physbeh.2012.10.008
Mathersul, D., McDonald, S., & Rushby, J. A. (2013b). Automatic facial responses to briefly
presented emotional stimuli in autism spectrum disorder. Biological Psychology,
94(2), 397-407.
Mathersul, D., McDonald, S., & Rushby, J. A. (2013c). Autonomic arousal explains social
cognitive abilities in high-functioning adults with autism spectrum disorder.
International Journal of Psychophysiology, 89(3), 475-482.
Mathersul, D., McDonald, S., & Rushby, J. A. (2013d). Psychophysiological correlates of
social judgement in high-functioning adults with autism spectrum disorder.
International Journal of Psychophysiology, 87(1), 88-94.
doi:http://dx.doi.org/10.1016/j.ijpsycho.2012.11.005
Mazefsky, C. A., Herrington, J., Siegel, M., Scarpa, A., Maddox, B. B., Scahill, L., & White,
S. W. (2013). The role of emotion regulation in autism spectrum disorder. Journal of
the American Academy of Child & Adolescent Psychiatry, 52(7), 679-688.
McCall, C., & Singer, T. (2013). Empathy and the brain. Baron Cohen, Simon [Ed], 195-213.
McIntosh, D. N., Reichmann-Decker, A., Winkielman, P., & Wilbarger, J. (2006). When the
social mirror breaks: Deficits in automatic, but not voluntary, mimicry of emotional
facial expressions in autism. Developmental Science, 9(3), 295-302.
McPartland, J. C., Crowley, M. J., Perszyk, D. R., Naples, A. J., Mukerji, C. E., Wu, J., . . .
Mayes, L. C. (2011). Temporal dynamics reveal atypical brain response to social 159
exclusion in autism. Developmental Cognitive Neuroscience, 1(3), 271-279.
doi:http://dx.doi.org/10.1016/j.dcn.2011.02.003
Mefford, H., Batshaw, M., & Hoffman, E. (2012). Genomics, intellectual disability and
autism. New England Journal of Medicine, 366, 733-743.
Minio-Paluello, I., Baron-Cohen, S., Avenanti, A., Walsh, V., & Aglioti, S. M. (2009).
Absence of embodied empathy during pain observation in Asperger syndrome.
Biological Psychiatry, 65(1), 55-62. doi:10.1016/j.biopsych.2008.08.006
Moody, E. J., McIntosh, D. N., Mann, L. J., & Weisser, K. R. (2007). More than mere
mimicry? The influence of emotion on rapid facial reactions to faces. Emotion, 7(2),
447-457. doi:10.1037/1528-3542.7.2.447
Moriguchi, Y., Decety, J., Ohnishi, T., Maeda, M., Mori, T., Nemoto, K., . . . Komaki, G.
(2007). Empathy and judging other's pain: An fMRI study of alexithymia. Cerebral
Cortex, 17(9), 2223-2234. doi:10.1093/cercor/bhl130
Moriguchi, Y., Ohnishi, T., Lane, R. D., Maeda, M., Mori, T., Nemoto, K., . . . Komaki, G.
(2006). Impaired self-awareness and theory of mind: An fMRI study of mentalizing in
alexithymia. NeuroImage, 32(3), 1472-1482. doi:10.1016/j.neuroimage.2006.04.186
Morris, C. G. (1996). Psychology: An introduction (9th ed.). 778, rentice-Hall.
Mukkades, M. N., & Fateh, R. (2010). High rates of psychiatric co-morbidity in individuals
with Asperger's Disorder. World Journal of Biological Psychiatry, 11, 486.
Mulloy, A., Lang, R., O'Reilly, M., Sigafoos, J., Lancioni, G., & Rispoli, M. (2010). Gluten-
free and casein-free diets in the treatment of autism spectrum disorders: A systematic
review. Research in Autism Spectrum Disorders, 4(3), 328-339.
Neuhaus, E., Beauchaine, T. P., & Bernier, R. (2010). Neurobiological correlates of social
functioning in autism. Clinical Psychology Review, 30(6), 733-748.
doi:10.1016/j.cpr.2010.05.007 160
Nichols, S., & Stich, S. (2003). Mindreading: An integrated account of pretence, self-
awareness and understanding other minds. Oxford, UK: Oxford University Press.
Niedenthal, P. M. (2007). Embodying emotion. Science, 316(5827), 1002-1005.
Oberman, L. M., Winkielman, P., & Ramachandran, V. S. (2009). Slow echo: Facial EMG
evidence for the delay of spontaneous, but not voluntary, emotional mimicry in
children with autism spectrum disorders. Developmental Science, 12(4), 510-520.
doi:http://dx.doi.org/10.1111/j.1467-7687.2008.00796.x
Perry, A., & Shamay-Tsoory, S. (2013). Understanding emotional and cognitive empathy: A
neuropsychological perspective. Baron Cohen, Simon [Ed], 178-194.
Pickles, A., Charman, T., Chandler, S., Loucas, T., & Baird, G. (2008). Psychiatric disorders
in children with Autism Spectrum Disorders: Prevalence, comorbidity and associated
factors in a population-derived sample. Journal of the American Academy of Child &
Adolescent Psychiatry, 47(8), 921-929.
Ponnet, K., Buysse, A., Roeyers, H., & De Clercq, A. (2008). Mind-reading in young adults
with ASD: Does structure matter? Journal of Autism and Developmental Disorders,
38(5), 905-918. doi:10.1007/s10803-007-0462-5
Raz, G., Jacob, Y., Gonen, T., Winetraub, Y., Flash, T., Soreq, E., & Hendler, T. (2014). Cry
for her or cry with her: Context-dependent dissociation of two modes of cinematic
empathy reflected in network cohesion dynamics. Social Cognitive and Affective
Neuroscience, 9(1), 30-38.
Rieffe, C., Oosterveld, P., Terwogt, M., Mootz, S., van Leeuwen, E., & Stockmann, L.
(2011). Emotion regulation and internalizing symptoms in children with autism
spectrum disorders. Autism : the international journal of research and practice, 15(6),
655-670. 161
Rinck, M., Rortgen, T., Lange, W.-G., Dotsch, R., Wigboldus, D. H., & Becker, E. S. (2010).
Social anxiety predicts avoidance behaviour in virtual encounters. Cognition and
Emotion, 24(7), 1269-1276.
Roedema, T. M., & Simons, R. F. (1999). Emotion-processing deficit in alexithymia.
Psychophysiology, 36(3), 379-387.
Roeyers, H., & Demurie, E. (2010). How impaired is mind-reading in high-functioning
adolescents and adults with autism? European Journal of Developmental Psychology,
7(1), 123-134. doi:10.1080/17405620903425924
Rogers, K., Dziobek, I., Hassenstab, J., Wolf, O., & Convit, A. (2007). Who cares?
Revisiting empathy in Asperger Syndrome. Journal of Autism and Developmental
Disorders, 37, 709-715.
Rotge, J. Y., Lemogne, C., Hinfray, S., Huguet, P., Grynszpan, O., Tartour, E., . . . Fossati, P.
(2014). A meta-analysis of the anterior cingulate contribution to social pain. Social
Cognitive and Affective Neuroscience, 10, 19-27.
Rozga, A., King, T. Z., Vuduc, R. W., & Robins, D. L. (2013). Undifferentiated facial
electromyography responses to dynamic, audio-visual emotion displays in individuals
with autism spectrum disorders. Developmental Science, 16(4), 499-514.
Rubin, K., Bowker, J., & Gazelle, H. (2010). Social withdrawal in childhood and
adolescence: Peer relationships and social competence. In K. Rubin & R. Coplans
(Eds.), The development of shyness and social withdrawal (pp. 131-156). New York,
USA: Guildford Press.
Rueda, P., Fernandez-Berrocal, P., & Baron-Cohen, S. (2015). Dissociation between
cognitive and affective empathy in youth with Asperger syndrome. European Journal
of Developmental Psychology, 12(1), 85-98. 162
Ruggieri, S., Bendixen, M., Gabriel, U., & Alsaker, F. (2013). Cyberball: The impact of
ostracism on the well-being of early adolescents. Swiss Journal of Psychology, 72(2),
103-109.
Samson, A. C., Hardan, A. Y., Podell, R., Phillips, J., & Gross, J. J. (2015). Emotion
regulation in children and adolescents with autism spectrum disorder. Autism
Research, 8(1), 9-18.
Samson, A. C., Huber, O., & Gross, J. J. (2012). Emotion regulation in Asperger's Syndrome
and High-Functioning Autism. Emotion, 12(4), 659.
Scheeren, A. M., Koot, H. M., Mundy, P. C., Mous, L., & Begeer, S. (2013). Empathic
responsiveness of children and adolescents with high-functioning autism spectrum
disorder. Autism Research, 6(5), 362-371.
Schulte-Rüther, M., Greimel, E., Markowitsch, H. J., Kamp-Becker, I., Remschmidt, H.,
Fink, G. R., & Piefke, M. (2011). Dysfunctions in brain networks supporting
empathy: An fMRI study in adults with autism spectrum disorders. Social
Neuroscience, 6(1), 1-21. doi:10.1080/17470911003708032
Schulte-Ruther, M., Greimel, E., Piefke, M., Kamp-Becker, I., Remschmidt, H., Fink, G. R., .
. . Konrad, K. (2014). Age-dependent changes in the neural substrates of empathy in
autism spectrum disorder. Social Cognitive and Affective Neuroscience, 9(8), 1118-
1126.
Schwenck, C., Gohle, B., Hauf, J., Warnke, A., Freitag, C. M., & Schneider, W. (2014).
Cognitive and emotional empathy in typically developing children: The influence of
age, gender, and intelligence. European Journal of Developmental Psychology, 11(1),
63-76.
Schwenck, C., Mergenthaler, J., Keller, K., Zech, J., Salehi, S., Taurines, R., . . . Freitag, C.
M. (2012). Empathy in children with autism and conduct disorder: Group-specific 163
profiles and developmental aspects. Journal of Child Psychology and Psychiatry,
53(6), 651-659. doi:http://dx.doi.org/10.1111/j.1469-7610.2011.02499.x
Sebastian, C., Blakemore, S.-J., & Charman, T. (2009). Reactions to ostracism in adolescents
with autism spectrum conditions. Journal of Autism and Developmental Disorders,
39(8), 1122-1130.
Shamay-Tsoory, S. G. (2009). Empathic processing: Its cognitive and affective dimensions
and neuroanatomical basis. In J. Decety & W. Ickes (Eds.), The Social Neuroscience
of Empathy. Cambridge, Massachusetts, USA: The MIT Press.
Shamay-Tsoory, S. G. (2014). Dynamic functional integration of distinct neural empathy
systems. Social Cognitive and Affective Neuroscience, 9(1), 1-2.
Shamay-Tsoory, S. G., & Aharon-Peretz, J. (2007). Dissociable prefrontal networks for
cognitive and affective theory of mind: A lesion study. Neuropsychologia, 45(13),
3054-3067. doi:10.1016/j.neuropsychologia.2007.05.021
Shamay-Tsoory, S. G., Aharon-Peretz, J., & Perry, D. (2009). Two systems for empathy: A
double dissociation between emotional and cognitive empathy in inferior frontal gyrus
versus ventromedial prefrontal lesions. Brain: A Journal of Neurology, 132(3), 617-
627. doi:10.1093/brain/awn279
Shamay-Tsoory, S. G., Shur, S., Harari, H., & Levkovitz, Y. (2007). Neurocognitive basis of
impaired empathy in schizophrenia. Neuropsychology, 21(4), 431-438.
doi:10.1037/0894-4105.21.4.431
Shamay-Tsoory, S. G., Tomer, R., Berger, B. D., Goldsher, D., & Aharon-Peretz, J. (2005).
Impaired "Affective Theory of Mind" Is Associated with Right Ventromedial
Prefrontal Damage. Cognitive and Behavioral Neurology, 18(1), 55-67.
doi:10.1097/01.wnn.0000152228.90129.99 164
Shtayermman, O. (2007). Peer victimization in adolescents and young adults with Asperger's
Syndrome: A link to depressive symptomology, anxiety symptomology and suicidal
ideation. Journal of Human Behaviour in the Social Environment, 18, 301-328.
Silani, G., Bird, G., Brindley, R., Singer, T., Frith, C., & Frith, U. (2008). Levels of
emotional awareness and autism: An fMRI study. Social Neuroscience, 3(2), 97-112.
doi:10.1080/17470910701577020
Singer, T., Seymour, B., O'Doherty, J., Kaube, H., Dolan, R. J., & Frith, C. D. (2004).
Empathy for pain involves the affective but not sensory components of pain. Science,
303(5661), 1157-1162.
Southam-Gerow, M. A., & Kendall, P. (2002). Emotion regulation and understanding:
Implications for child psychopathology and therapy. Child Psychology Review, 22,
189-222.
Spek, A. A., Scholte, E. M., & Van Berckelaer-Onnes, I. A. (2010). Theory of mind in adults
with high-functioning autism and Asperger syndrome. Journal of Autism and
Developmental Disorders, 40, 280-289.
Sperry, L. A., & Mesibov, G. B. (2005). Perceptions of social challenges of adults with
autism spectrum disorder. Autism : the international journal of research and practice,
9(4), 362-376.
Spielberger, C. D., Gorsuch, R., Lushene, R., Vagg, P. R., & Jacobs, G. (1983). Manual for
the State-Trait Anxiety Inventory. Palo Alto, CA, USA: Consulting Psychologists
Press.
Stauder, J. E. A., Bosch, C. P. A., & Nuij, H. A. M. (2011). Atypical visual orienting to eye
gaze and arrow cues in children with high functioning autism spectrum disorder.
Research in Autism Spectrum Disorders, 5(2), 742-748.
doi:10.1016/j.rasd.2010.08.008 165
Stavropoulos, K. K., & Carver, L. J. (2013). Research review: Social motivation and oxytocin
in autism-Implications for joint attention development and intervention. Journal of
Child Psychology and Psychiatry, 54(6), 603-618.
Stone, V., Baron-Cohen, S., & Knight, R. T. (1998). Frontal lobe contributions to theory of
mind. Journal of Cognitive Neuroscience, 10(640-656).
Swain, D., Scarpa, A., White, S., & Laugeson, E. (2015). Emotion dysregulation and anxiety
in adults with ASD: Does social motivation play a role? Journal of Autism and
Developmental Disorders, 45(12), 3971-3977.
Symes, W., & Humphrey, N. (2010). Peer-group indicators of soical inclusion among pupils
with autistic spectrum disorders (ASD) in mainstream secondary schools: A
comparative study. School Psychology International, 31(5), 478-494.
Tager-Flusberg, H. (2007). Evaluating the theory-of-mind hypothesis of autism. Current
Directions in Psychological Science, 16(6), 311-315. doi:10.1111/j.1467-
8721.2007.00527.x
Trimmer, E. M., McDonald, S., & Rushby, J. (2016). Not knowing what I feel: Emotional
empathy in Autism Spectrum Disorder. Autism : the international journal of research
and practice, May, 1-8.
Turner, S., Beidel, D. C., Dancu, C., & Stanley, M. (1989). An empirically derived inventory
to measure social fears and anxiety: The social phobia and anxiety inventory.
Psychological Assessment: A journal of Consulting and Clinical Psychology, 1, 35-
40.
Umeda, S., Mimura, M., & Kato, M. (2010). Acquired personality traits of autism following
damage to the medial prefrontal cortex. Social Neuroscience, 5(1), 19-29.
doi:10.1080/17470910902990584 166
Vachon-Presseau, E., Martel, M. O., Roy, M., Caron, E., Jackson, P. L., & Rainville, P.
(2011). The multilevel organization of vicarious pain responses: Effects of pain cues
and empathy traits on spinal nociception and acute pain. Pain, 152(7), 1525-1531.
Van Overwalle, F. (2009). Social cognition and the brain: a meta-analysis. Human brain
mapping, 30(3), 829-858. doi:10.1002/hbm.20547 van Steensel, F. J., Bogels, S. M., & Perrin, S. (2011). Anxiety disorders in children and
adolescents with Autistic spectrum disorders: A meta-analysis. Clinical Child and
Family Psychology Review, 14(3), 302-317.
Vasey, M. W., & Thayer, J. F. (1987). The continuing problem of false positives in repeated
measures ANOVA in psychophysiology: A multivariate solution. Psychophysiology,
24(4), 479-486.
Vivanti, G., D'Ambrogio, T., & Zappella, M. (2006). Evidence of altered response to pain in
autism. Giornale di Neuropsichiatria dell'Eta Evolutiva, 26(2), 181-189.
Wechsler, D. (1999). Wechsler Abbreviated Scale of Intelligence. San Antonio, Texas, USA:
The Psychological Corporation.
White, S. W., Ollendick, T., & Bray, B. C. (2011a). College students on the autism spectrum:
Prevalence and associated problems. Autism : the international journal of research
and practice, 15(6), 683-701.
White, S. W., Ollendick, T. H., & Bray, B. C. (2011b). College students on the autism
spectrum: Prevalence and associated problems. Autism : the international journal of
research and practice, 15(6), 683-701.
White, S. W., Oswald, D. P., Ollendick, T., & Scahill, L. (2009). Anxiety in children and
adolescents with autism spectrum disorders. Clinical Psychology Review, 29(3), 216-
229. 167
Will, G., van Lier, P., Crone, E., & Güroğlu, B. (2016). Chronic childhood peer rejection is
associated with heightened neural responses to social exclusion during adolescence.
Journal of Abnormal Child Psychology, 44(1), 43-55.
Williams, D. (2010). Theory of own mind in autism: Evidence of a specific deficit in self-
awareness? Autism : the international journal of research and practice, 14(5), 474-
494.
Williams, K. D., Cheung, C. K. T., & Choi, W. (2000). Cyberostracism: Effects of being
ignored over the Internet. Journal of Personality and Social Psychology, 79(5), 748-
762. doi:10.1037/0022-3514.79.5.748
Williams, K. D., & Sommer, K. L. (1997). Social ostracism by coworkers: Does rejection
lead to loafing or compensation? Personality and Social Psychology Bulletin, 23(7),
693-706. doi:10.1177/0146167297237003
Yamada, T., & Decety, J. (2009). Unconscious affective processing and empathy: An
investigation of subliminal priming of the detection of painful facial expressions.
Pain, 143(1-2), 71-75.
Yirmiya, N., Erel, O., Shaked, M., & Daphna, S.-L. (1998). Meta-analysis comparing theory
of mind abilities in individuals with autism, individuals with mental retardation and
normally developing individuals. Psychological Bulletin, 124(3), 283-307.
Yirmiya, N., Sigman, M. D., Kasari, C., & Mundy, P. (1992). Empathy and cognition in high-
functioning children with autism. Child Development, 63(1), 150-160.
doi:10.2307/1130909
Zalla, T., Sav, A.-M., Stopin, A., Ahade, S., & Leboyer, M. (2009). Faux pas detection and
intentional action in Asperger Sydnrome. A replication on a French sample. Journal
of Autism and Developmental Disorders, 39, 373-382. 168
Zech, E., Luminet, O., Rime, B., & Wagner, H. (1999). Alexithymia and its measurement:
Confirmatory factor analyses of the 20-item Toronto Alexithymia Scale and the
Bermond-Vorst Alexithymia Questionnaire. European Journal of Personality, 13(6),
511-532.
Zwaigenbaum, L., Bryson, S., Rogers, T., Roberts, W., Brian, J., & Szatmari, P. (2005).
Behavioral manifestations of autism in the first year of life. International Journal of
Developmental Neuroscience, 23, 143-152.