Executive Function, Social Emotional Learning, and Social Competence in Autism Spectrum Disorder A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy in Clinical Psychology University of Regina By Nathalie Catherine Marie Berard Regina, Saskatchewan May, 2014 Copyright 2014: N.Berard
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Executive Function, Social Emotional Learning, and
Social Competence in Autism Spectrum Disorder
A Thesis
Submitted to the Faculty of Graduate Studies and Research
In Partial Fulfillment of the Requirements
For the Degree of
Doctor of Philosophy
in
Clinical Psychology
University of Regina
By
Nathalie Catherine Marie Berard
Regina, Saskatchewan
May, 2014
Copyright 2014: N.Berard
UNIVERSITY OF REGINA
FACULTY OF GRADUATE STUDIES AND RESEARCH
SUPERVISORY AND EXAMINING COMMITTEE
Nathalie Catherine Marie Berard, candidate for the degree of Doctor of Philosophy in Clinical Psychology, has presented a thesis titled, Executive Function, Social Emotional Learning, and Social Competence in Autism Spectrum Disorder, in an oral examination held on April 14, 2014. The following committee members have found the thesis acceptable in form and content, and that the candidate demonstrated satisfactory knowledge of the subject material. External Examiner: *Dr. J. Montgomery, University of Manitoba
Co-Supervisor: Dr. Lynn Loutzenhiser, Department of Psychology
Co-Supervisor: Dr. Dennis Alfano, Department of Psychology
Committee Member: Dr. Chris Oriet, Department of Psychology
Committee Member: *Dr. Kristi Wright, Department of Psychology
Committee Member: Dr. Kerri Staples, Faculty of Kinesiology and Health Studies
Chair of Defense: Dr. Ken Montgomery, Faculty of Education *via teleconference
i
ABSTRACT
The main objective of this study was to investigate the concurrent role of
multiple antecedents of social competence in a group of children with Autism
Spectrum Disorder (ASD). Existing models of social competence were adapted to
include three domains of executive function (EF: Cognitive, Behavioural, and
Emotional Regulation), and two domains of Social Emotional Learning (SEL:
Nonverbal Awareness, Social Understanding). The EF domains were related to
sustained attention, working memory, planning, behavioural inhibition, and affective
decision making; SEL domains included social comprehension, and identification and
interpretation of social cues. Social competence was defined in terms of social skills
and adaptive social functioning. The relationships amongst the EF and SEL domains,
and social competence were examined in a sample of 49 boys with ASD and 48
neurotypical boys, aged 8 to 13 years. Results showed that the ASD group performed
significantly below the control group on most SEL and EF domains. Children with
ASD were also rated significantly lower on social competence measures and parental
ratings of EF. Importantly, the EF domain of Cognitive Regulation predicted social
competence in boys with ASD whereas the SEL domain of Social Understanding
predicted social competence in neurotypical boys. These findings contribute
significantly to our understanding of social competence and quality of life in boys
with ASD. The observation that Cognitive Regulation predicts social competence in
boys with ASD has important clinical implications for specifically targeting EF in
both assessment and treatment.
ii
Acknowledgements
The completion of this dissertation was made possible with the incredible
support and guidance of my supervisors, Dr. Lynn Loutzenhiser and Dr. Dennis
Alfano. They helped instill confidence in my work and taught me the importance of
perseverance. Thank you both for pushing me past what I thought I was able to
accomplish. I am so grateful for your constant availability and your valuable
expertise, feedback, and mentorship throughout all stages of this research. My
appreciation also goes out to the members of my advisory committee: Dr. Kerri
Staples, Dr. Chris Oriet, and Dr. Kristi Wright and also to Dr. Janine Montgomery,
the external examiner. Thank you all for your thoughtful consideration of this work.
I am grateful for the encouragement and moral support of my friends and
colleagues. To my colleagues at the Autism Centre, thank you for supporting me
throughout this research. A special thanks to Dr. Della Hunter, who instilled in me a
thirst to learn more about executive functioning and its importance in the lives of
children.
Lastly, I want to thank the children and families with whom I have worked
over the years, who taught me so much about the importance of vulnerability, hope
and gratitude. I am particularly grateful to all the children and families who took the
time to participate in this study.
iii
Dedication
The support of my family has been invaluable to the completion of this
dissertation. I want to express my deep appreciation to my parents, Norbert and
Claudette, for the endless support they have given me throughout my life and for
always believing in me. Merci de m'avoir appuyé toute ma vie et lors de se long trajet
difficile et parfois frustrant. Cet accomplissement est pour vous, car malgré tous les
détours de la vie, vous m'avez appris qu'il faut garder le but en tête et persévéré.
To my husband, Colin, this has certainly been a shared journey. Thank you for
supporting my decision to attend graduate school. It has been a long and sometimes
difficult and frustrating road, and I could not have completed this dissertation without
your support, perspective and sense of humour along the way. I appreciate the
sacrifices you made so that I could achieve this goal. To my children, Cadell and
Milène, you are too young to understand the sacrifices you made that allowed me to
complete this research and fulfill my dream. I hope you always have the support I was
provided throughout this research so that you too may accomplish what you want in
life. Please remember that with confidence - belief in yourself, hard work and
determination you can fulfill your goals.
iv
Table of Contents
Abstract .......................................................................................................................... i
Acknowledgements ....................................................................................................... ii
Dedication .................................................................................................................... iii
Table of Contents ......................................................................................................... iv
List of Tables .............................................................................................................. vii
List of Figures ............................................................................................................ viii
List of Appendices ....................................................................................................... ix
1.0 Overview ................................................................................................................. 1
2.0 Introduction ............................................................................................................. 4
2.1 Social Competence ...................................................................................... 4
2.2 Autism Spectrum Disorder .......................................................................... 5
2.3 Models of Social Competence .................................................................... 9
2.4 Executive Functions .................................................................................. 15
2.5 Executive Function in Relation to ASD and Social Competence ............. 18
2.6 Social Emotional Learning ........................................................................ 28
2.7 Heterogeneity in ASD ............................................................................... 37
2.8 Relationships among SEL, EF and Social Competence............................ 38
2.9 Limitations of Current Research ............................................................... 39
2.10 Integration and Aims of the Current Research ........................................... 41
3.0 Research Hypotheses ............................................................................................ 44
4.0 Methods................................................................................................................. 47
4.1 Participant Recruitment ............................................................................. 47
v
4.2 Data Collection.......................................................................................... 48
4.3 Clinical File Review .................................................................................. 49
4.4 Participants ................................................................................................ 49
4.5 Child Measures of SEL and EF ................................................................. 50
4.6 Parent Measures of EF and Social Competence ....................................... 63
5.0 Results ................................................................................................................... 67
5.1 Data Analysis Plan ................................................................................... 67
5.2 Descriptive Information ............................................................................ 68
5.3 Hypothesis 1 .............................................................................................. 76
5.4 Hypothesis 2 .............................................................................................. 78
5.5 Hypothesis 3 .............................................................................................. 83
5.6 Hypothesis 4 .............................................................................................. 92
5.7 Hypothesis 5 ............................................................................................ 102
6.0 Discussion ........................................................................................................... 104
6.1 Social Competence ................................................................................. 104
6.2 Demographic Factors Associated with Social Competence .................. 106
6.3 Social Emotional Learning ..................................................................... 111
6.4 Executive Functions ............................................................................... 117
6.5 Model of Social Competence ................................................................. 121
6.6 Relationship between SEL and EF domains .......................................... 132
6.7 SEL and EF in Relation to Social Competence ..................................... 134
6.8 Predictors of Social Competence ........................................................... 139
6.9 Study Limitations ................................................................................... 149
vi
6.10 Conclusion and Future Directions ............................................................... 151
7.0 References ........................................................................................................... 156
vii
List of Tables
Table 1. Demographic variables for ASD and control group ..................................... 51
Table 2. Age, IQ, SEL & social competence correlations: control group .................. 71
Table 3. Age, IQ, SEL & social competence correlations: ASD group ...................... 72
Table 4. Age, IQ, EF & social competence correlations: control group ..................... 73
Table 5. Age, IQ, EF & social competence correlations: ASD group .......................... 74
Table 6. Descriptive statistics of SEL, EF and social competence measures ........... 79
Table 7. Correlations of SEL, EF & SC domains: control group ................................. 94
Table 8. Correlations of SEL, EF & SC domains: ASD group ..................................... 95
Table 9. Regression analyses predicting social competence: control group ............... 98
Table 10. Regression coefficients of the SEL and EF: control group ........................... 99
Table 11. Regression analyses predicting social competence: ASD group ............... 100
Table 12. Regression coefficients of the SEL and EF: ASD group............................. 101
viii
List of Figures
Figure 1a. McKown three-factor SEL model ............................................................ 13
Figure 1b. McKown two-factor SEL model .............................................................. 13
Figure 3. The proposed tripartite EF and SEL domains .......................................................19
Figure 4. Proposed social competence model ............................................................ 43
Figure 5a. SEL domains for the control group .......................................................... 84
Figure 5b. SEL domains for the ASD group .............................................................. 84
Figure 6a. EF domains for the control group ............................................................. 90
Figure 6b. EF domains for the ASD group ................................................................ 90
Figure 7. SEL, EF and social competence: ASD group ........................................... 141
Figure 8. SEL, parent-rated EF and social competence: ASD group ...................... 142
Figure 9. SEL, EF and social competence: control group ....................................... 143
Figure 10. SEL, parent-rated EF and social competence: control group ................. 144
ix
List of Appendices
Appendix A: Ethics approval ................................................................................... 197
Appendix B: Recruitment letter ............................................................................... 199
Appendix C: Consent and assent forms ................................................................... 200
Appendix D: Visual schedule .................................................................................. 209
Appendix E: List of all measures used in study ....................................................... 210
Appendix E: List of all measures used in study ....................................................... 211
Appendix G: Record forms for SEL & EF tasks ..................................................... 213
1
1.0 Overview
Social competence, broadly defined as the ability to effectively engage in social
interactions (Rose-Krasnor, 1997), is a robust and critical predictor of positive outcomes
throughout the lifespan in both neurotypical and clinical populations. (Burt et al., 2008;
Caprara, Barbaranelli, Pastorelli, Badura & Zimbardo, 2000; Crick & Dodge, 1994;
Eberly & Montemayor, 1998; Kumpfer, 1999; Rys & Bear, 1997; Mangham, McGrath,
Reid, & Stewart, 1995; McKown, Gumbiner, Russo & Lipton 2009; Smart & Sanson,
2003).
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder whose
cardinal feature is social impairment (Volkmar & Klin, 2005). Children with ASD are
thus at higher risk for poorer overall quality of life. Despite this increased risk, there is
currently limited understanding of social competence in ASD, factors that contribute to
the heterogeneity of social abilities in ASD, or effective clinical management strategies.
Developmental theories of social competence have tended to focus on the effects
of either socio-cognitive or socio-affective processes on social competence (Lipton &
Nowicki, 2009), and research on ASD has similarly tended to focus on socio-cognitive
and/or socio-emotional processes as a means of explaining deficits associated with the
diagnosis. More recent models have identified executive function (EF) as an additional
important potential contributor to social competence in children (Beauchamp &
Anderson, 2010). To date, however, the role of EF in the social competence of children
with ASD is unclear.
Typical interventions to assist children in learning behaviours to improve social
2
interactions generally fall under the rubric of social emotional learning (SEL) skills,
which encompass socio-cognitive skills and socio-emotional skills, such as emotion
recognition, social problem solving, and taking the perspective of others. SEL skill
interventions, however, have not proven as effective in clinical populations such as
children with ASD, particularly those with high functioning ASD (Rao, Beidel &
Murray, 2008; White, Keonig & Scahill, 2007). An important limitation to such
conventional interventions is the assumption SEL skills are the primary antecedents to
social competence. However, the recent identification of EF as an important antecedent to
social competence warrants a more comprehensive approach to understanding and
improving social competence in ASD. There are thus clear theoretical grounds to revisit
current models of social competence, particularly with respect to the important role that
EF, either alone or in conjunction with SEL skills, play in the development of social
competence in both neurotypical children and those with ASD. The ultimate aim is to
inform the literature and to improve upon current interventions to increase social
competence in ASD.
In the following sections, a brief overview of ASD is provided, highlighting the
heterogeneity in this population. Existing theories of social competence are reviewed
with a focus on identifying a more comprehensive set of antecedents to social
competence that can be applied to children with ASD. Next, recent conceptualisations of
EF as a form of executive control of cognitive, behavioural, and emotional functioning
are explored (Wasserman & Wasserman, 2013). Particularly, the literature highlighting
the potential role of executive functions (EF) in the social competence of children is
examined. Thereafter, limitations and controversies in the ASD and EF literature are
3
identified. Finally, the implications of a comprehensive model of social competence that
includes different domains of EF and SEL in ASD is addressed.
4
2.0 Introduction
2.1. Social Competence
Social competence is commonly conceptualized as a multifaceted construct that
occurs along a continuum and which predicts the effectiveness of interactions with others
(Bohlin & Hagekull, 2009; Odom, McConnell & Brown, 2008; Burt, Obradovic, Long &
Masten, 2008; Rose-Krasnor, 1997; Segrin, 2000; Trentacosta & Fine, 2010). Social
competence includes behaviours such as sharing, helping, cooperating, initiating and
maintaining relationships, sensitively interacting with others, negotiating needs and
effectively handling conflict situations. From a developmental perspective, social
competence involves “the active and skilful coordination of multiple processes and
resources available to the child to meet social demands and achieve social goals in a
particular type of social interaction (e.g., parent-child, peer relations) and within a
specific context (e.g., home, school)” (Iarocci, Yager & Elfers, 2007, p. 113).
While social competence develops throughout childhood, there is a correlation
between social competence in early childhood and later adolescence, suggesting that it is
typically established early and is stable across time (Eisenberg et al., 1997; Masten &
Coatsworth, 1998). Social competence has been found to predict quality of peer
relationships, academic success in late childhood, and work competence in adolescence
(Burt et al., 2008; Caprara et al., 2000; Eisenberg et al., 1997; Masten et al., 1995a,
1995b). Social competence is also one of the most frequently identified attributes of
resiliency and overall mental health in children (Parrila, Ma, Fleming & Rinaldi, 2002).
When a mental disorder is present, poor social competence has been correlated with
5
greater severity of illness, a higher risk of co-morbid disorders, and a poorer prognosis
(Jewel, Jordan, Hupp, & Everett, 2009). Given the importance of social competence to
quality of life, understanding the variables associated with social competence in child
mental health populations with social impairments is an important area of study. Autism
Spectrum Disorder (ASD) is a neurodevelopmental disorder whose prime feature is social
impairment (Volkmar & Klin, 2005). This research will focus on increasing our
understanding of social competence in ASD, factors that contribute to the heterogeneity
of social abilities in ASD, and implications for effective clinical management strategies.
2.2 Autism Spectrum Disorder
ASD is an aetiologically complex neurodevelopment disorder characterized by
core deficits in social functioning. It has been well-established that children with ASD
exhibit multiple pervasive deficits in social competence, including initiating interactions,
joint attention, play skills, reciprocal interactions, nonverbal communication, and peer
relations (Carter, Davis, Klin & Volkmar, 2005).
ASD subsumes three disorders that fall under the umbrella of ‘Pervasive
Developmental Disorders’ (PDD) in the Diagnostic and Statistical Manual of Mental
Disorders - Fourth Edition, Text Revision (DSM-IV-TR) (APA, 2000): Autistic Disorder,
Asperger's Disorder, Pervasive Developmental Disorder Not Otherwise Specified (PDD
NOS). All three disorders are defined by a core ‘triad’ of qualitative impairments in
social interaction, verbal and nonverbal communication deficits, and restricted, repetitive,
and stereotyped interest. The term autism spectrum disorder (ASD) is frequently used in
the literature to encapsulate these three disorders due to evidence that has suggested a
substantial overlap among them (Szatmari, 1992; Volkmar, Lord, Bailey, Schultz & Klin,
6
2004; Volkmar & Klin, 2005; Wing, 2005). This approach is also taken in the recent
DSM-5, which has identified ASD as a single diagnostic category encompassing Autistic
Disorder, Asperger’s Disorder, and PDD NOS based on the idea that a single spectrum
better reflects the behavioural pathology (Swedo, 2009, p.1). In keeping with this, the
term ASD will be utilised for the remainder of this study.
The most recent prevalence rates for ASD range from 15 to 23 per 1,000 (Centers
for Disease Control and Prevention, 2012); the prevalence of ASD in Canada has been
estimated to be 11 per 1,000, with the majority (82%) being male (NHSR, 2013).
2.2.1 Social competence in ASD. The pathognomonic feature of ASD is social
impairment (Mundy, 2003). Children with ASD have multiple social deficits, including
impairment in the use of non-verbal behaviours to regulate social interaction, difficulty
establishing and maintaining peer relationships, a lack of shared enjoyment in the
interests and accomplishments of others, and a general paucity of social or emotional
reciprocity (APA, 2000). Nevertheless, ASD is also a disorder marked by variability that
is evident in symptom manifestation, cognitive abilities, and developmental course and
outcomes. For example, children with ASD and normal intellectual functioning are
referred to as having high-functioning autism spectrum disorder (HFASD) , and different
social trajectories have been demonstrated for children with and without co-morbid
intellectual disabilities (Szatmari, Bryson, Boyle, Streiner & Duku, 2003; Klin & Jones,
2009). Predictors of social competence have also been found to be different for children
with HFASD and those with ASD and intellectual disabilities, who comprise
approximately 30% of the population (CDC, 2012). Specifically, IQ and language
abilities were found to predict social competence in children with ASD and low
7
intellectual function but found to have only weak associations in those with HFASD (Lis
et al., 2001; Sigman & Ruskin, 1999; Szatmari et al., 2003). Since language and IQ do
not necessarily predict social competence in children with HFASD, examining other
factors, such as social emotional comprehension and executive functions (EF), may
provide valuable information regarding social competence in this particular population.
Given the well-documented differences between children with ASD with and without an
intellectual disability, all information subsequently presented will specifically relate to
children with ASD without intellectual disabilities, unless otherwise mentioned.
It is also well demonstrated in the literature that children with ASD display a wide
range of social competence, with some doing better or worse than others. For example,
some children with ASD have reciprocal friendships whereas others do not (Orsmond et
al., 2004). In fact, within the triad of impairments associated with ASD, children with
ASD show significantly more variability in the social domains as compared to the
domains of communication and repetitive behaviours (Hazlett et al., 2009). Given the
variability in behavioural and clinical manifestations of ASD, examining the
heterogeneity in the social competence of children with average intelligence has
important clinical implications, particularly since the majority are in mainstream
classrooms and have to negotiate daily social demands with peers and teachers.
Significant variability in the social competence of children with ASD has been
identified (Szatmari et al., 2003; Volkmar, Lord, Bailey, Schultz & Klin, 2004), but the
reason for the variability remains unclear and has not been a focus of research. Why are
some children with ASD able to maintain peer relationships? Why are some children with
ASD able to respond appropriately to certain social demands? Research in this area has
8
likely been hindered by a focus on social deficits and maladaptive behaviours rather than
intact abilities. For example, social competence is primarily measured using the Autism
Diagnostic Interview-Revised (ADI-R) [Rutter, Le Couteur, & Lord, 2003] or Autism
Diagnostic Observation Schedule (ADOS) [Lord et al., 1989], which are diagnostic tools
designed to identify presence or absence of ASD symptoms and deficits. Equally
problematic is the measurement of social competence using brief screening instruments,
such as the Social Responsiveness Scale (SRS: Constantino & Gruber, 2005), which
assesses non-social aspects of impairment, such as reactions to sensory stimuli and
repetitive behaviours. Although these measures evaluate difficulties salient to ASD and
provide meaningful information regarding the triad of impairments associated with the
disorder, they do not evaluate many other important aspects of social functioning, such as
sharing, helping, and cooperating. The singular focus of deficit or disability models thus
has inherent limitations in terms of accounting for the heterogeneity in social functioning
in ASD. Furthermore, recent reviews have documented that most individual and group-
based social skills training programs designed for children with ASD fail to demonstrate
significant improvements in social competence (Rao et al., 2008; White, Keonig, &
Scahill, 2007), perhaps due to a restricted view of factors relevant to social competence.
It is now well-established in the developmental literature that social competence
encompasses multiple variables. In this light, researchers have recently begun to explore
the heterogeneity of ASD using multidimensional conceptualisations of social
competence. The following section addresses these new conceptualizations. This
population bears particular importance to the study of social competence. Children with
ASD are at high risk for co-morbid mental health disorders that do not form part of
9
diagnostic criteria for ASD (Mattila et al., 2010; Midouhas, Yogaratnam, Flouri and
Charman, 2013), as well as poor quality of life by virtue of the significant social
impairments associated with the disorder (Howlin et al., 2013; Baird, 2014). Moreover,
current social interventions have been fairly ineffective in improving social competence.
The importance of identifying better mechanisms for improving their quality of life via
increased social competence is therefore quite valuable.
2.3 Models of Social Competence
Early models of social competence focused predominantly on either social-
cognitive or social-affective processes or skills, as the key antecedents to social
competence. Socio-cognitive models emphasized the thought processes involved in social
competence and the importance of perception and interpretation of social information
(Crick & Dodge, 1994). Socio-affective models, on the other hand, emphasized the
importance of the perception of emotions in the development of social competence
(Halberstadt et al., 2001; Saarni, 1990).
One drawback of the above models is that they tend to focus on the contributions of
either emotion or cognition alone in social functioning rather than both. It is now
generally agreed that multiple processes must operate concurrently for the development
of effective social competence (Bierman, 2004; Crick & Dodge, 1994; Lipton &
Nowicki, 2009; McKown, 2007; McKown et al., 2009; McKown, Allen, Russo-Ponsaran
& Johnson, 2013). Another drawback is that while both models emphasize the
importance of self-regulation to social competence (Crick & Dodge, 1994; Eisenberg &
Fabes 1992; Eisenberg et al., 1997; Fabes et al., 1999; Halberstadt et al., 2001; Masten &
Coasworth, 1998; Smart & Sanson, 2003), self-regulation is not well-defined.
10
Self-regulation is an umbrella term referring to the capacity to manage one’s
thoughts, feelings and actions in adaptive and flexible ways across a range of contexts
(Saarni, 1997; Vohs & Baumeister, 2004; 2011), and the importance of self-regulation in
social functioning is well-documented in the literature (Duckworth & Seligman, 2005;
Heatherton & Wagner, 2001; Tangney et al., 2004; Vohs & Baumeister, 2004; 2011).
Recent conceptualizations of social competence that incorporate self-regulation have
been significantly advanced by the notion of Executive Function (EF) (Beauchamp
&Anderson, 2010; McKown et al., 2009; Yeates et al., 2004). EF refers to a multiple
higher order process system that serves as an integrated supervisory control mechanism
for thought and action that is mediated by prefrontal areas of the brain (Golstein,
Naglieri, Princiotta & Otero, 2014). Self-regulation is generally conceived of as the
ability to guide, monitor, and direct one’s performance to successfully manage behaviour
and achieve goals (Singer & Bashiri, 1999, Hofmann, Schmeichel & Baddely, 2012). EF
and self-regulation are thus inextricably linked (Hofmann et al., 2012; Rueda, Possner &
Rothburt, 2005), but have largely been examined separately in relation to social
competence. This is likely because the concept of self regulation has been studied
predominantly in social and personality research, whereas EF has been examined
predominantly in cognitive psychology and the neurosciences. In the last few years,
however, there has been some attempt to link these concepts, and there is accumulating
support for the idea that EF skills in fact promote self-regulation in terms of direction and
control across the domains of cognition, action, and emotion (Hofmann et al., 2012;
McCloskey, 2009, 2011).
Conceptually, it makes sense that EF contributes to self-regulation. For example,
11
when a child wants to hit a sibling but refrains from doing so, he/she is self-regulating an
impulse. When a child follows-through with a promise to help a friend with homework
instead of engaging in a fun activity, he/she is resisting temptation. Self-regulation also
includes the ability to delay gratification, such as when a child overrides the desire to buy
candy in order to save money to purchase a future item. Attention helps a child to resist
distraction, working memory helps to keep a goal in mind, inhibition helps withhold
predominant responses, and planning helps a child think ahead. Thus, in order to
successfully self-regulate behaviour, a child must successfully use various EF skills.
EF abilities are, therefore, central to self-regulatory processes and contribute
significantly to the development of socially acceptable behaviour. Recent
conceptualisations have emphasized the importance of EF skills in self-regulation
(Barkley, 2012; McCloskey, 2009, 2011), and have asserted that self-regulation should
indeed be subsumed under the overarching umbrella of EF (Berkman, Graham & Fisher,
2012; McCloskey, 2011).
2.3.1 The McKown model of social competence. This model represents a recent
integration of SEL and self-regulation (McKown et al., 2007). SEL skills comprise
various social-emotional comprehension skills, which constitute an integrated
representation of socio-cognitive and socio-affective factors. An important contribution
of this model is the robust empirical analysis of SEL antecedents to social competence.
McKown et al. (2013) proposed a two-factor and a three-factor model of SEL that
includes nonverbal awareness (affect recognition), social meaning (interpretation of
social cues) and social reasoning (social problem solving); the two-factor model included
nonverbal awareness in one domain and an amalgamation of social meaning and social
12
problem solving in the other domain. McKown et al. decided on the three factor model
due to its better fit with the empirical data and closer correspondence to their
conceptualisation of SEL (see Figures 1a & 1b). McKown et al. (2009; 2013)
subsequently demonstrated that SEL skills are independent predictors of social
competence for both typically developing children and children with various mental
health disorders. Each domain in the two and three factor models of SEL was positively
correlated with social competence. Overall, the better developed a child’s SEL, the more
positive their social interactions and peer relationships. The McKown model thus has
important theoretical and clinical implications because it demonstrates that SEL skills
have concurrent, incremental and discriminative validity for social competence (Lipton &
Nowicki, 2009; McKown, 2007; McKown et al., 2009; McKown et al., 2013). Moreover,
McKown et al. (2013) demonstrated that the construct of social emotional comprehension
can be reliably assessed with psychometric measures that directly reflect the critical
dimensions of SEL. Also, the McKown model includes the concept of self regulation,
defined as "the ability to modulate attention and behaviour in response to a situation"
(McKown et al., 2009; p.860) and provides data demonstrating that both SEL and self-
regulation are significant independent predictors of social competence; indeed, self-
regulation was shown to be more highly correlated to ratings of social competence than
SEL.
One drawback of the McKown model, however, is the narrow measurement of
social competence using only three subscales of the Social Skills Rating System (SSRS:
Gresham & Elliot, 1990) in one study and two subscales of the Behavior Assessment
System for Children (BASC: Reynolds & Kamphaus, 1992) in another. Together, these
Figure 1: McKown et al. (2009) three-factor
model of the relationship between social-
emotional learning (SEL) skill, self-regulation,
and social competence
13
Figure 1b: McKown et al. (2009) two-factor
model of the relationship between social-
emotional learning (SEL) skill, self-regulation,
and social competence
Figure 1a: McKown et al. (2009) three-factor
model of the relationship between social-
emotional learning (SEL) skill, self-regulation,
and social competence
14
subscales reflect important aspects of social competence for children related to
cooperation, assertiveness and self-control, but do not tap the full range of indicators of
social competence. Another limitation with the McKown model is the narrow
measurement of self-regulation that focused only on attention and inhibition, and that was
assessed only through parental ratings. Further, the measurement of self-regulation and
social competence similarly used the same parental report, potentially influencing the
findings due to shared rater and method bias. The McKown Model could also be
expanded to include consider a wider range of self-regulatory behaviours that might be
re-conceptualised as the broader range of EF skills. Specifically, while the McKown
Model includes inattention, which is one component of cognitive regulation, there are
other aspects of cognitive regulation, such as planning and working memory, which may
also play a role in social competence. Similarly, while the McKown Model includes
inhibition, which is one component of behavioural regulation, it neglects to consider
other aspects of behavioural regulation, such as self-monitoring and suppression of
automatic impulses that may also be important. Moreover, despite being identified as a
basic element in social interactions and a critical component of social competence
(Eisenberg et al., 2000b; Halberstadt et al.2001; Saarni, 1999; Semrud-Clikeman, 2007;
Sigman & Ruskin, 1999), emotion regulation is not addressed in the McKown Model.
The McKown Model, therefore, might be improved upon by expanding the range
of indicators of social competence and by including a more comprehensive
conceptualization of cognitive, behavioural and emotional regulation consistent with
recent notions of EF. Specifically, direct evaluation of a broad range of EF skills that
subserve self-regulatory processes may provide a more comprehensive examination of
15
antecedents of social competence and provide valuable information for targeted
intervention (Hofmann et al., 2012). To address these limitations, the present study
extends the McKown Model of social competence to include a comprehensive evaluation
of EF within the three domains of regulation – cognitive, behavioural and emotion
regulation – in order to determine the specific antecedents to social competence in ASD.
Such a comprehensive approach to studying factors that directly and indirectly interact in
the development and maintenance of social competence in both neurotypical and clinical
populations has previously been proposed (Beauchamp & Anderson, 2010; Yeates et al.,
2007). For example, in a study of 228 adolescents, Rinsky and Hinshaw (2012)
demonstrated that direct measures of planning, inhibition and working memory predicted
social competence in teens with and without ADHD. Interventions targeting specific EF
skills have demonstrated improvements in social behaviour in typical young children
(Diamond, Barnett, Thomas & Munro, 2007), and reduction in behaviour problems in
school-aged children (Riggs, Greenberg, Kusche & Pentz, 2006), and those with ADHD
(Berkman, Graham & Fisher, 2012).
2.4 Executive Functions
EF is generally defined as a multiple process system that serves to control and
integrate higher order cognitive processes that are involved in thought, action, and goal-
directed behaviour (Anderson, 2002; Kerr & Zelazo, 2004; Rasmussen & Bisanz, 2009).
Self-regulatory functions are considered a hallmark feature of EF (Giurak et al., 2009;
McCloskey, 2009, 2011; Wasserman & Wasserman, 2013; Ylisaker & Feeney, 2002;
Zhou, Chen & Main, 2012) and may be best understood as a core element of EF that is
16
involved in the conscious regulation of thought, emotion, and behavior (McCloskey,
2006; Zelazo, 2010).
Recent conceptualizations of EF propose a multifaceted construct based on three
distinct but interrelated dimensions of self-regulatory processes: cognitive, behavioural
and emotion regulation (Ganesingham et al., 2006; 2007a; 2007b; Gioia et al., 2002;
Jahromi & Stifter, 2008; McCloskey, 2011; Wasserman & Wasserman, 2013; Zhou et al.,
2012). Cognitive regulation is typically operationalised as attention, planning, working
memory, flexibility and processing speed (Anderson, 2002; Hill, 2004; Roberts &
Pennington, 1996; Russo et al., 2007). Behavioural regulation refers to various aspects of
inhibitory control, including the ability to regulate activity level, self-monitor and
suppress automatic impulses (Hinnant & Obrien, 2007). Emotion regulation is
distinguished from Behavioural and Cognitive Regulation by the extent to which it
involves affect and regulation. Emotion regulation has been defined as the ability to
modulate emotional expressions to meet situational demands and achieve personal goals
(Blandon, Calkins & Keane, 2010; Dennis, 2010; Lewis, Lamm, Segalowitz, Stieben &
Zelazo, 2006). While some have conceptualised EF dimensions as interrelated and
interdependent (Anderson, 2002), others have conceptualised them as interrelated but
distinct (Korkman, Kirk & Kemp, 2007; Lehto et al. 2003; Miyake et al., 2000). EF
regulatory processes, however, are fundamentally and conceptually linked at the
behavioural and neural levels (Calkins, 2010; Ganesalingam et al., 2007).
Developmentally, EF skills emerge at different times during early childhood and
continue to improve throughout adolescence and early adulthood (Best & Miller, 2010).
An integrated model of EF thus considers developmental aspects of EF and the important
17
contributions of cognitive, inhibitory and emotional aspects of EF to a child's developing
social competence (Barkley, 1997; Best & Miller, 2010; Blair, Zelazo & Greenberg,
2005; Hongwanishkul et al., 2005; Jahromi et al, 2008; McCloskey, 2009, 2011;
Ylvisaker & Sweeney, 2002).
While this three-dimensional conceptualisation of EF is supported in recent
literature (Egeland & Fallmyr, 2010; Gioia et al., 2002; Gioia et al., 2010; Jahromi &
Sifter, 2008), it has not been investigated in children with ASD. However, correlations
have been found among the cognitive, behavioural and emotion regulation domains in
young neurotypical children (Jahromi & Stifter, 2008) and children with acquired brain
injuries (Ganesingham et al., 2006, 2007). A three-part approach also accords with
present knowledge of multifaceted brain functioning (Egeland & Fallmyr, 2010;
Ganesingham et al., 2007; Gioia et al., 2002; Zelazo & Muller, 2002).
In sum, the three domains of EF – namely, cognitive, behavioural and emotional
regulatory processes – provide a more current and broader conceptualisation of EF to
explore in relation to social competence. While the McKown model provides an
empirically sound avenue to evaluate the role of SEL in the social competence of children
with ASD, adapting it in accordance with the tri-partite conceptualization of EF allows
for the direct and independent evaluation of two potential important antecedents of social
competence, namely SEL and EF. The current study extends McKown's SEL model to
further explore the relationship between the three domains of SEL, the three domains of
performance-based and parent-rated EF and social competence in children with high
functioning ASD (see Figure 3). Next, the three domains of EF – cognitive, behavioural
and emotional regulation – will be further explored in relation to children with ASD.
18
2.5 EF in Relation to ASD and Social Competence
Cognitive, behavioural and emotion regulation domains of EF have been linked to
social competence in typically developing children and certain clinical groups (e.g.
ADHD; TBI), and the conceptualisation of EF as comprising cognitive, behavioural and
emotion regulatory processes has been supported in the recent literature (Ganesingham et
al., 2006; 2007a; 2007b; Gioia et al., 2002). Deficits in some executive functions are also
well-documented in school-age children with ASD (see reviews: Hill, 2004; Pennington
& Ozonoff, 1996; Russo et al., 2007; Sergeant, Geurts, & Oosterlaan, 2002). For
example, it has been demonstrated that, as a group, children with ASD perform
significantly worse than neurotypical children on both performance-based and parent-
rated measures of EF, and that their performance is not correlated with IQ (Landa &
Goldberg, 2005; Liss et al., 2001). However, there is still no consensus on which specific
aspects of EF are most impaired in school-aged children with ASD (Barron- Linnankoski
et al., 2014; Corbett et al., 2009; Narcizi et al., 2013; Rinehart, Bradshaw, Tonge,
Brereton & Bellgrove, 2002; Semrud-Clikeman et al., 2010; van Rijn et al., 2013) and no
study has yet examined cognitive, behavioral, and emotional regulatory aspects of EF in
relation to social competence in ASD.
2.5.1 Cognitive regulation and social competence. Three aspects of cognitive
regulation - attention, planning and working memory – have been well-studied in
children with ASD. Attention processes include the capacity to selectively attend to
specific stimuli (selective attention) and to focus on a specific task and suppress
19
Figure 3. The proposed tripartite EF and SEL domains using
McKown and colleagues' 2009 three-factor model of SEL as a
framework
20
irrelevant stimuli (sustained attention) (Anderson, 2002). Planning is a "complex,
dynamic operation in which a sequence of planned actions must be constantly monitored,
re-evaluated, and updated" (Hill, 2004, p.192). Working memory entails simultaneously
remembering and manipulating incoming information (Russo et al., 2007).
A child's ability to attend to relevant social cues or social information is thought to
impact his/her behaviour in social situations. Furthermore, effective social interactions
rely on the ability to hold context- and content-specific information in mind regarding the
verbal and nonverbal messages being provided by another person, and then planning how
to respond appropriately in order to achieve a goal, be it social interaction, making a
request, or negotiating (Bennetto, Pennington & Rogers, 1996). In a series of studies with
typically developing children, sustained attention was significantly associated with
positive peer relationships in school-aged children (Hughes, Dunn, & White, 1998;
NICHD 2003; NICHD, 2009; Murphy, Brinkman & McNamara, 2007). In a longitudinal
study, attention and planning in childhood were significantly related to peer competence
in adolescence (Landry & Smith, 2010). Working memory has also been correlated with
factors related to social competence, such as facial affect recognition (Mathersul et al.,
2009), and with Theory of Mind (ToM) in children (Hughes, 1998). The importance of
working memory in social competence is often cited (e.g. Beauchamp & Anderson,
2010), but has rarely been directly examined in typically developing children.
Although there is some evidence that children with ASD perform significantly
worse than neurotypical peers on three aspects of cognitive regulation: sustained
attention, planning and working memory (Hill, 2004; Russo et al., 2007), findings are
mixed. For example, while some studies have also found that children with ASD perform
21
significantly worse than neurotypical controls and children with ADHD on measures of
attention (Corbett et al., 2009; Corbett & Constantine, 2006; Kenworthy et al., 2009; van
Rijn et al., 2013), others have not (Goldberg et al., 2005; Johnson et al., 2007; Sanders et
al., 2008). The inconsistent findings may be a result of the differences in the populations
sampled. For example, Goldberg and colleagues (2005) excluded children who were
rated above the cut-off scores on parent-rated measures of inattention, hyperactivity or
impulsivity. It is also unclear if both visual and auditory modes of sustained attention are
equally impacted in ASD (Corbett & Constantine, 2006; Corbett, Constantine, Hendren,
Rocke & Ozonoff, 2009).
There is substantial evidence that children with ASD have significant impairments
in planning tasks compared to neurotypical children (Hill, 2004; Liss et al., 2001;
Ozonoff & McEvoy, 1994; Robinson, Goddard, Dritschel, Wisley & Howlin, 2009) and
children with ADHD (Sergeant, Geurts & Oosterlaan, 2004).There is some evidence that
children with ASD have deficits in working memory, although the results are mixed.
Variable results appear to be a function of the task, with children doing better when
required to respond to verbal rather than visual stimuli. Therefore, while children with
ASD performed the same as neurotypical peers on measures of verbal working memory
(Geurts et al., 2004; Ozonoff & Strayer, 2001), they performed significantly worse on
measures of visual working memory (Landa & Goldberg, 2005; Goldberg et al., 2005). In
one well-designed study, the Goldberg research team (2005) demonstrated that children
with ASD performed significantly worse than neurotypical peers on measures of planning
and visual working memory despite being matched on age, verbal and performance IQ
and social economic status (SES). These findings emphasize that the nature of the task
22
needs to be considered in any study design and analysis, and that visual working memory
should be specifically targeted in studies with the ASD population.
Few studies have examined the relationship between components of cognitive
regulation and social competence of children with ASD. Both spatial working memory
(Gilotty et al., 2002; Landa & Godlberg, 2005) and planning (Happe, Booth, Charlton &
Hughes, 2006) have been significantly associated with social deficits in children with
ASD. Happe and colleagues (2006) found that planning and working memory were
related to social competence for children with ASD but not for those with ADHD or
neurotypical children. In contrast, Joseph and Tager-Flusberg (2004) found no
relationship between cognitive indices of EF and social deficits in ASD. Thus, while
there is some evidence to support the hypothesis that the cognitive domains of EF are
associated with social competence in ASD, most studies focused on social impairment
and thus further study is clearly required (Riggs et al., 2006b).
2.5.2 Behavioural regulation and social competence. Behavioural regulation
refers to various aspects of inhibitory control, including the ability to regulate activity
level, self-monitor and suppress automatic impulses (Hinnant & Obrien, 2007). Effective
social interactions often require the withholding of dominant responses (Rueda, Posner &
Rothbart, 2005), and inhibitory skills are central in suppressing salient thoughts and
behaviours in favour of those that are more socially appropriate (Channon & Watts, 2003;
Ciairano et al., 2007). Many studies have evaluated the association between inhibition
and social outcome in school-aged children, but most have focused on problematic
behaviours rather than social competence. Decreased inhibition has been associated with
problematic behaviours, such as aggression, non-cooperative behaviour, and
23
noncompliance (Huyder et al., 2012; Thorell, Bohlin & Rydell, 2004; Vuontele et al.,
2012). The association between inhibition and aspects of social competence, such as
cooperative behaviour, has been documented in neurotypical children (Ciairano, Visu-
Petra & Settanni, 2007). Inhibition has also been found to predict concurrent and future
levels of social competence in neurotypical children (NICHD 2003; NICHD, 2009; Nigg
et al., 1999; Smith, 2010).
The evidence regarding inhibitory control in children with ASD remains
inconclusive, with some studies reporting deficits relative to control groups (Kenworthy
et al., 2009; Narzisi, Muratori, Calderoni, Fabbro, & Urgesi, 2013; Robinson et al., 2009;
van Rijn et al., 2013) and others reporting intact inhibition (Barron et al., 2014; Christ et
al., 2007; Hill & Bird, 2006 ). The inconsistency between studies is likely related to the
type of inhibition evaluated. Specifically, children with ASD typically perform as well as
neurotypical peers during tasks of neutral inhibition (e.g., push button for X and withhold
for Y), but perform significantly worse when required to inhibit an over-learned or
dominant response in favour of an unusual one (Hill, 2004; Homack & Riccio, 2004;
Russo et al. 2007; Sanders et al., 2008). Only a few studies have directly compared the
effect of modality on performance, and again, results are inconclusive. Sanderson &
Allen (2013) administered multiple inhibitory tasks to 31 six to eleven year old children
with ASD and a mental-age matched control group. They found intact performance on
tasks requiring the suppression of a habitual response, but impaired performance on tasks
that required both suppression of dominant response and the replacement with an
opposing response. In contrast, another series of studies found that children and
adolescents with ASD performed as well as a control group on both types of inhibition
24
(Christ, Holt, White & Green, 2007; Christ, Kester, Bodner &Miles, 2011). It is possible
that the wider age range in these studies had an impact on performance. Thus while it is
clear that children with ASD do not demonstrate a global inhibitory impairment, there is
evidence that performance differs across tasks and thus using multiple inhibitory tasks
when examining inhibition in children with ASD is warranted. Furthermore, although
task complexity likely impacts performance, it is unknown whether using stimuli that
require visual or auditory responses makes a difference.
Several studies have examined the relationship between behavioural regulation
and social functioning of children with ASD. Inhibition was significantly associated with
adaptive socialisation (Happe et al., 2006), symptom severity (Lieb, 2011; Scheeren,
Koot & Begeer, 2012) and behaviour problems (Lieb, 2011). Happe and colleagues
(2006) found that inhibition was related to social competence for children with ASD but
not for those with ADHD or the neurotypical children. In one small study of 3 to 10 year
old children with ASD, parental ratings of inhibition were significantly associated with
affect regulation (Konstantareas & Stewart, 2006). That is, children with lower inhibitory
control had less adaptive affect regulation strategies. In contrast, one study found no
significant association between inhibition and social deficits as measured by the ADOS
(Joseph & Tager-Flusberg, 2004).
2.5.3 Emotion regulation and social competence. Emotion regulation is
considered a'hot' executive function (Kerr &Zelazo, 2004) in the neuropsychological
literature, where it is more specifically defined as affective or value-based decision-
making and measured through various gambling tasks or simulated high-risk situations
(Ardila, 2013). The whole premise of these tasks is that those who have better affective
25
decision making are able to strategically adjust their decisions based on previous wins
and losses in highly motivating situations where there is some sort of personal gain to be
made (Blair, Zelazo & Greenberg, 2005; Faja, 2013; Hooper, 2004). It is the experience
of emotion that is tied to, or guides the decision making process (Buelow & Suhr, 2009).
Developmentally, successful performance thus requires that children forgo short-term
gains for long-term benefits and also involves the ability to adjust behaviour on the basis
of feedback cues (Cassotti et al., 2011), which helps children operate in emotionally and
motivationally charged situations (Carlson, Zayas & Guthormsen, 2009; Zelazo &
Carlson, 2012).
Emotion regulation was previously viewed as a separate functional dimension but
has recently been incorporated as part of the tripartite EF construct. Neuro-imaging
studies and neuropsychological research support the role of emotion regulation as a
domain of EF. Neuro-imaging and lesion studies have demonstrated that affective
decision making is associated with a distinct functional-anatomical network within the
ventromedial prefrontal cortex (Glascher et al., 2012; Mitchell, Rhodes, Pine & Blair,
2008), sharing neural underpinnings with both behavioural and cognitive regulation
(Dennis, 2010; Dennis, Malone & Chen, 2009; Hongwanishkul, Happaney, Lee &
Zelazo, 2005; Lewis et al., 2006). Behaviourally, several recent confirmatory factor
analyses of the Behavior Rating Inventory of Executive Functions (BRIEF) (Gioia,
Isquith, Guy & Kenworthy, 2000) also demonstrated a distinction between cognitive,
behavioural and emotion regulation (Egeland & Fallmyr; 2010Gioia et al., 2002).
The ability to regulate affect has been demonstrated to play an important role in
the development of social competence, particularly peer relationships (Aldao, Nolen-
26
Hoeksema & Schweizer Lewis, 2010;Blandon et al., 2010; Carthy et al., 2010;Eisenberg
et al., 1995, 1997; Gross & Thompson, 2007; Kochanska, Murray, & Harlan 2000;
Lewis, Zinbarg & Durbin, 2010; Thompson, Lewis & Calkins, 2008;), but the
relationship between affective decision making and social competence has not been
explored. Affective decision making has been studied in relationship to some EF tasks
(da Mata et al., 2011; Toplak, Sorge, Benoit, West & Stanovich, 2010), but not in
relationship to social competence. A few studies have found a significant association
between affective decision making and symptom severity of ADHD (Geurts, van der
Oord & Crone, 2006; Groen, Gaastra, Evans & Tuche, 2013; Toplak, Sorge, Benoit, West
& Stanovich, 2010). The few studies that investigated affective decision making within
the ASD population have all reported no differences compared to a control group
(deMartino et al., 2008; Faja, 2013; Johnson & Yechiam, 2006; Russo, 2002; Sawa et al.,
2013; South, 2011; South et al., 2008; Yechiam et al., 2010). However, several studies
reported that those with ASD respond differently to the emotionally relevant stimuli
(deMartino et al., 2008; Johnson et al., 2006; South et al., 2011; Yechian et al., 2010). In
a study of 8 to 18 year old children with ASD, South and colleagues (2011) demonstrated
that those with ASD adjusted their behaviour based on losses (punishment) whereas the
control group adjusted their behaviour based on gains (rewards). In addition, performance
on affective decision making was significantly associated with parent-rating of
behavioural inhibition. No studies to date, however, have examined the association
between emotion regulation and social competence in children with ASD.
2.5.4 Parent-ratings of EF. In addition to performance-based measures, numerous
studies incorporate parent-ratings when evaluating EF in children. Parent-ratings of EF
27
provide an alternate measure of EF as applied to everyday life, and are assumed to
provide 'real-world context' to EF measurement. In the most commonly-used measure –
the Behaviour Rating Inventory of Executive Function ((BRIEF: Gioia et al., 2002) –
parent ratings provide information on a range of skills related to metacognitive,
behavioural and emotion regulation skills, such as working memory, inhibition and
emotional control. All published studies using the BRIEF with the ASD population have
demonstrated significant EF deficits compared to neurotypical samples (Kalbfleisch
&Loughan, 2012; Kenworthy et al., 2008; 2009; 2010; Rosenthal et al., 2013; Troyb et
al., 2013), even for children and adolescents diagnosed with ASD before age five but
who no longer met DSM-IV criteria (see Troyb et al., 2013).
Parent-ratings of EF have, however, also been criticized for their lack of
correlations with performance-based measures. McAuley and colleagues (2010) reviewed
12 studies involving neurotypical children between ages 6 and 18 that compared
performance-based EF scores with the Behaviour Rating Inventory of Executive Function
(BRIEF), and found eight studies that reported some significant correlations between
isolated EF tasks and parent ratings of EF. Given the wide range of performance-based
EF measures used across these studies, the association between performance-based EF
measures and parent ratings of their child's EF remains inconclusive. More recently,
Toplak and colleagues (2013) reviewed 13 studies comparing performance-based and
parent-rated EF and reported that 35 out of a total of 182 correlations (19%) were
statistically significant. Despite these criticisms, researchers continue to use EF ratings in
research with children (Denckla, 2002; Isquith, Roth & Gioia, 2013; Rosenthal et al.,
2013; Toplak, West & Stanovich, 2013). It is important to recognize that performance-
28
based and parent-rated measures of EF are not equivalent measures per se, but that they
are likely measuring parallel and distinct EF processes. Incorporating a parent measure of
EF provides relevant information regarding a broad range of EF in everyday context and
the application of EF in less structured and more complex contexts (Denckla, 2002;
Donders& Larsen, 2012; Isquith, Roth & Gioia, 2013).
2.5.5 Summary. There is substantial evidence that children with ASD have
deficits in EF related to cognitive regulation and behavioural regulation. Although
emotion regulation has been largely ignored in the ASD research, there is some reason to
believe that affective decision making may be impaired in children with ASD. While
there is some evidence that the three domains of EF are associated with aspects of social
competence in both ASD and neurotypical children, further study is clearly required
(Happe et al., 2006; Riggs et al., 2006b). Studying the antecedents of social competence
in ASD in the context of a comprehensive model that incorporates the tripartite model of
EF and SEL may help to further explain the nature of social dysfunction in ASD and lead
to more effective intervention strategies. The relationship between SEL and social
competence of children with ASD is explored in the next section.
2.6 Social Emotional Learning
Social emotional learning skills are generally accepted as key antecedents of social
competence and are often an important focus of intervention in ASD (Rao et al., 2008).
The McKown (2013) model identifies three domains (Nonverbal Awareness, Social
Meaning and Social Reasoning) relevant to SEL.
2.6.1 Nonverbal awareness. Nonverbal awareness generally refers to affect
recognition, namely the ability to interpret nonverbal cues that signal others’ emotions.
29
Affect can be inferred via facial expressions, tone of voice and/or posture. Affect
recognition is considered a core skill that promotes the development of more complex
aspects of SEL (Lemerise & Arsenio, 2000; Izard, 2001; Singh et al., 1998). In other
words, a child must be able to first accurately recognise facial affect in order to interpret
the mental state of others, to understand others’ behaviour and to appropriately respond.
Izard (1971) first highlighted the role of affect recognition in the development of
social competence in children, and that since has been demonstrated to be a significant
predictor of longer-term social competence (Frith & Baron-Cohen, 1987; Izard et al.,
2001,; Joseph & Tager-Flusberg, 2004; Semrud-Clikeman, 2007). In a recent meta-
analysis, Trentacosta and Fine (2010) found nonverbal awareness to be a significant
correlate of social competence in a wide range of neurotypical samples. Furthermore,
there is evidence to suggest that inferring affect through facial expressions, voices and
posture is significantly correlated (McKown et al., 2009; Trentacosta & Fine, 2010).
More importantly, an association between the ability to recognize complex emotions in
faces and voices separately, as well as to recognize complex emotions when facial, vocal
and contextual information is integrated, has been demonstrated. These findings suggest
that isolated affect recognition tasks offer an ecologically valid assessment of nonverbal
awareness.
Studies examining facial affect recognition abilities in children with ASD have
found that they perform more poorly than neurotypical children when tasks involve the
identification of more complex and intense emotions, such as pride and jealousy (Begeer
et al., 2008). There is less agreement regarding the ability of children with ASD to
recognize facial affect in regard to more simple and basic emotions, such as happiness,
30
anger, sadness and fear. Some studies have found significant deficits when compared to
neurotyical peers (Bolte & Poustka, 2003; Mazefsky and Oswald, 2007; Rump,
Giovannelli, Minshew & Strauss, 2009; Semrud-Clikeman, Walkowiak, Wilkinson &
Butcher, 2010), whereas others have not (Adolf, Sears & Piven, 2001; Castelli, 2005;
Gross, 2004). This disparity may be due to the different levels of emotion intensity used
across studies, which is often not identified or examined. For example, it is possible that
children with ASD are able to identify more obvious (i.e., high intensity) but not more
subtle (i.e., low intensity) demonstrations of affect. One study that directly examined the
role of intensity found that children with ASD performed more poorly than neurotypical
children when the tasks involved identifying more subtle facial expressions (i.e., low
intensity) and tone of voice (Mazefsky & Oswald, 2007). Therefore, studies examining
affect recognition with the ASD population need to consider both the intensity and
complexity of the emotions being assessed.
Data on voice affect recognition of children with ASD is sparse and the findings are
mixed. Some report that children with ASD show deficits compared to typically
developing peers (Lindner &Rosen, 2006; Oerlemans et al., 2013; Peppe, McCann,
Gibbon, O'Hare & Rutherford, 2007), while others do not (Brooks & Ploog, 2013;
Mazefsky & Oswald, 2007; Paul, Augustyn, Klin & Volkmar, 2005). For example,
Mazefsky and Oswald (2007) found that children with HFASD performed significantly
worse than the standardization sample whereas children with Asperger's performed
within expected range. Paul et al. (2005) found that qualitatively adolescents with ASD
relied solely on rate of speech to match voice to affect whereas typically developing peers
31
used both rate of speech and intonation to recognize emotion. These results were recently
replicated by Brooks and Ploog (2013) using a novel video game format.
Affect recognition has also been significantly associated with social competence
and social impairments in some studies (Braverman, Fein, Lucci, and Waterhouse, 1989;
Joseph and Tager-Flusberg, 2004) but not others (Wong, Beidel, Sarver & Sims, 2012).
For example, Wong and colleagues (2012) found that although 9 to 13 year old children
with ASD performed significantly worse than neurotypical children in terms of affect
recognition, their performance was not related to social competence.
2.6.2 Social meaning. Social meaning involves the understanding and
interpretation of others’ communication. It encompasses the ability to interpret others’
intentions (ToM), interpret the social meaning of language (pragmatic language) and
appropriately respond to others’ emotions (empathy) (McKown et al., 2009). Two aspects
of social meaning – ToM and pragmatic language – have been thoroughly studied in
children with ASD whereas empathy has been less examined with this population.
Theory of mind (ToM) is the ability to perceive others’ mental states, such as their
beliefs and intentions (Baron-Cohen, 2009; Mckown et al., 2009). The ability to
understand that other’s thoughts and beliefs guide their behaviour allows a person to
understand, predict or explain the behaviour of others (Baron-Cohen, 2009; Peterson,
Garnett, Kelly & Attwood, 2009, p. 106). These 'mindreading' skills are considered a
critical element for social competence because they allow a person to consider the
perspective of others, which is critical to social competence.
Numerous studies have demonstrated a developmental progression of these skills.
Mastery of less complex, or first order ToM tasks (Sally thinks friend A believes X)
32
typically occurs around age five, and mastery of more complex, or second order ToM
tasks (Sally thinks friend A believes friend B believes X) by age eight (Miller, 2009).
Simple and complex ToM skills have been demonstrated to be significantly correlated
with social competence (Astington, 2003; Hughes & Leekam, 2004; Miller, 2009;
Peterson et al., 2009) and to be predictive of future social competence (Razza & Blair,
2009). There is consensus that the majority of children with ASD are able to pass simpler
ToM tasks (e.g., A believes X), but results are mixed regarding more advanced ToM
tasks (e.g., A believes B believes X). While most studies found a deficit compared to
neurotypical peers (Baron-Cohen, Jolliffe, Kaland et al., 2008; Mortimore & Robertson,
1997; Nilsen & Fecica, 2011; Peterson et al., 2009; Wellman & Liu, 2004;), others did
not (Begeer, Malle, Nieuwland & Keysar, 2010; Peterson et al., 2009). It has been
demonstrated that when mastery does occur, it is usually at a much older age than
typically developing peers (Miller, 2009; Peterson, Garnett, Kelly & Attwood, 2009).
ToM has been the most studied SEL in relation to social competence for children
with ASD. For example, Peterson et al. (2009) demonstrated that children with ASD who
passed ToM tasks were more socially competent than those who failed them. Tager-
Flusberg (2003) found that ToM was significantly associated with socialization skills in 4
to14 year old children with ASD, findings consistent with other studies (Frith et al., 1994;
2003; 2009; Peterson et al., 2009; Razza et al., 2009; Tager-Flusberg, 2003). However,
several studies have failed to find a significant relationship between ToM and social
competence (Begeer et al., 2011; Bennett et al., 2013) in similar-aged children with ASD.
Empathy is the ability to respond to someone else’s emotions in an appropriate
manner (McKown et al., 2009; Baron-Cohen, 2009). It involves the ability to affectively
33
take the perspective of someone else and to demonstrate an emotional response congruent
to their emotional state (Eisenberg, 2003). Two meta-analyses found a significant
correlation between empathy and social competence (Eisenberg & Miller, 1987;
Underwood & Moore, 1982) in neurotypical children and a recent longitudinal study of
social competence (Smart & Sanson, 2003) reported that empathy was the most stable
skill from early childhood to early adulthood. The significant association between social
competence and empathy thus has important treatment implications for high-risk
populations.
Poor empathy is a hallmark of ASD, but there are relatively few studies examining
empathy in children with ASD. The majority of studies have demonstrated that children
with ASD perform significantly worse than neurotypical peers across the age span
(Auyeung et al., 2009; Baron-Cohen & Wheelwright, 2004; Charman et al., 1997;
Wakabayashi et al., 2007; Yirmiya, Sigman, Kasari & Mundy, 1992), but there are some
exceptions (Charman, Sweetenham, Baron-Cohen, Cox, Baird & Drew, 1997; Mcdonald
& Messinger, 2012). For example, in one recent study where adolescents were asked to
look at vignettes of emotionally charged situations and rate how they would feel if they
were the actors, no difference between teens with ASD and neurotypical peers was found
(Jones et al., 2010). The inconsistent findings may be explained by several factors. First,
the modality used may impact results. In the Jones and colleagues (2010) study, the
adolescents were not required to identify the emotion but rather rate the intensity of a
designated emotion (e.g., "How guilty would you feel?"). Thus, while children with ASD
may be able to rate emotions in a similar way to neurotypical peers, they may have more
difficulty when asked to identify or respond to the observed emotions (e.g. Yirmiya et al.,
34
1992). Second, the inconsistent findings might be related to the inclusion of children of
all developmental levels, as studies that include children with lower cognitive levels tend
to demonstrate impairment in empathy (Bacon, Fein, Morris, Waterhouse & Allen, 1998;
Scambler et al., 2007). Third, most ASD research focuses on cognitive empathy (the
ability to infer someone else's feelings), not affective empathy (having a congruent
response to others' emotions). Dissociation between cognitive and affective empathy has
been demonstrated in several studies. For example, impaired cognitive empathy but intact
affective empathy was demonstrated in children and adults with ASD using self-rated
questionnaires (Jones et al., 2010; Rogers et al., 2007), as well as a direct measure of
empathy (Dziodek et al., 2008; Schwenck et al., 2012). Given that cognitive empathy
likely parallels ToM, more information on the affective empathy of children with ASD is
needed. There has been some study on the relationship between affective empathy and
social competence in ASD, with mixed results; positive correlations between empathy
and quality of social interaction have been found in young children with ASD (Scambler,
Hepburn, Rutherford, Wehner & Rogers, 2007; Travis, Sigman & Ruskin, 2001) but not
in older school-aged children (Scheeren, Koot, Mundy, Mous & Begeer, 2013).
Pragmatic language refers to the use and interpretation of language for social
meaning (Adams, 2002). Pragmatic skills involve “being able to use social contextual
cues in order to understand a speaker’s meaning” (Coplan & Weekes, 2009 p. 240). They
include rules for social communication, such as turn taking, responding to statements
made by others, and maintaining a topic of conversation, as well as understanding
subtleties of language, such as sarcasm and idioms. Pragmatic skills are significantly
associated with social competence in preschool (Gertner, Rice, & Hadley, 1994; Mccabe
35
& Meller, 2004) and school-aged children (Adams, Lloyd, Aldred & Baxendale, 2006;
Coplan & Weeks, 2009; McKown, 2007; Place & Becker, 1991; Russel & Grizzle, 2008).
While pragmatic language impairments are frequently cited in the ASD literature,
they have not been extensively examined in empirical research. For example, in a recent
review Loukusa and Miliken (2009) found only 20 studies on pragmatic language in the
past two decades. In these studies, there was agreement across age ranges that children
with ASD performed significantly worse than neurotypical peers on pragmatic language
tasks. Few studies have directly examined the relationship between the pragmatics of
language and social competence in the ASD population, and the studies that have been
done produced mixed findings (Landa & Goldberg, 2005; Leonard, Milich & Losch,
2011).
2.6.3 Social reasoning. Social reasoning refers to the ability to problem solve when
faced with social dilemmas or social provocations. It involves detecting a problem, and
generating appropriate and effective solutions. Social reasoning is based on Crick and
Dodge's (1994) Social Information Processing (SIP) model. SIP is a robust theory of
social competence that has had a tremendous influence on our understanding of the
manner in which social information is processed.
A plethora of research confirms that social problem-solving skills account for
significant variance in social functioning throughout the life span (e.g. Bauminger,
Edelsztein, & Morash, 2005; Ganesingham, Yeates, Sanson & Anderson, 2007b; Mayeux
& Cillessen, 2003; Sibley, Evans & Serpell, 2010; Tur-Kaspa, 2004). Social problem-
solving skills also account for significant variance in social functioning in different
populations, such as children with ADHD (Sibley et al., 2010), children with internalising
36
disorders (Luebbe, Bell, Allwood, Swenson & Early 2010; Siu & Shek, 2010) and
typically developing children (McKown, 2007; McKown et al., 2009).
There is general consensus that children with ASD are significantly poorer at
social problem solving compared to neurotypical peers. A number of studies have
consistently demonstrated that children (Meyer et al., 2006; Ziv, Hadad & Khateeb,
2013), adolescents (Flood, Hare & Wallis, 2011; Channon, Sharman, Heap, Crawford &
Rios, 2001) and adults (Goddard, Howlin, Dritschel & Patel, 2007) with ASD perform
significantly worse than neurotypical peers. Two studies specifically examining social
problem solving skills in 7 to 13 year old children with ASD are particularly noteworthy.
Demopoulos et al. (2013) studied 436 children with ADHD and 137 children with ASD
with varying levels of intelligence, and found significant differences in social problem
solving between the children and adolescents with ASD compared to the children with
ADHD, and in comparison to normative sample data. In a series of studies by Channon et
al., (2001), children were presented with short videos of situations involving some type of
social problem, such as a neighbour's dog constantly barking and interfering with the
actor's ability to do homework and sleep. The children were then asked a series of
questions regarding what the actor should do. The researchers found that while children
with ASD could identify the problem and come up with as many solutions as their
neurotypical peers, they needed more prompts to do so, and the social appropriateness
and effectiveness of their responses was significantly poorer. These impairments were
found during both videotaped and story format vignettes.
Significant relationships have also been found between social problem solving
and prosocial behaviours (Flood, Hare & Wallis, 2011), social difficulties and anxiety
37
(Meyer et al., 2006), as well as problem behaviours (Ziv, Hadad & Khatee, 2013) in
children with ASD.
Overall, there is certainly some clear evidence that children with ASD do more
poorly across the three domains of SEL (Nonverbal Awareness, Social Meaning, Social
Reasoning) compared to neurotypical peers. Furthermore, although less well studied,
there is reason to assert that the three domains of SEL contribute to social competence in
neurotypical children, and several studies have demonstrated that SEL skills are
significantly associated with some aspects of social functioning in children with ASD.
Most of these studies, however, examined SEL skills in relation to social impairment or
problematic behaviour, rather than social competence, the focus of the present study.
2.7 Heterogeneity in ASD
The clinical heterogeneity of children with ASD has been demonstrated
throughout the SEL and EF literature. Statistically, a high degree of variances of scores in
ASD have been reported on SEL skills (Wright et al., 2008; Yirmiya, Sigman, Kasari &
Mundy, 1992), EF measures (Happe et al., 2006; Kenworthy et al., 2009) and parent
ratings of EF (Boyd et al., 2009). Thus, in addition to overall deficient performance in EF
and SEL areas of functioning, there is a greater range of skills and deficits in children
with ASD than in their neurotypical counterparts. While comparisons with neurotypical
peers have provided robust and valuable information regarding specific areas of SEL and
EF in children with ASD, few studies have directly examined the wide variance in
performance or explored the relationship between these skills and social competence. In
short, even though a diagnosis of ASD denotes significant social impairments, there is
still a wide range of social competence amongst this population (Bruining et al., 2010;
38
Happe et al., 2006; Hazlett et al., 2009; Pelphrey et al., 2005; Pelphrey et al., 2007).
Exploring SEL and EF in the same study allows the thorough examination of factors that
may help explain the heterogeneous nature of social competence in ASD.
2.8 Relationships Among SEL, EF and Social Competence
The relationship between EF and SEL skills in children with ASD is unclear. The
EF components of working memory and sustained attention have been significantly
associated with the SEL skill of affect recognition (Mathersul et al., 2009). The most
robust findings come from the ToM literature and demonstrate that the SEL ability to
understand others' beliefs is significantly associated with planning and inhibition
(Carlson & Moses, 2001; Hill, 2004; Hughes, 2002; Joseph & Tager-Flusberg, 2004;
Pellicano, 2010). It is also possible that the relationship between EF and ToM may be
stronger for the ASD population than for neurotypical children. For example, Ozonoff et
al., (1991) found significant positive correlations between EF and the SEL skill of ToM
for children with ASD, but notfor neurotypical children. While such findings offer
important information regarding the association between EF and SEL, they do not allow
for a comprehensive understanding of the relationship.
At present, there is limited knowledge of the mechanism by which EF and SEL
may relate to social competence. One idea on how EF could affect social competence
suggests that cognitive, behavioural, and emotion regulation could impact the detection,
discrimination and processing of the subtle, dynamic, and complex cues in the social
environment. Impaired EF could thus disrupt accurate processing of nonverbal cues,
social meaning, and social reasoning, and thereby compromise a child's ability to
appropriately navigate the social world. The argument could then be made that EF
39
impacts social competence via its relationship with nonverbal awareness, social meaning,
and social reasoning. Support for this view comes from the ToM literature, which has
demonstrated that EF predicts ToM, but not vice versa (Channon et al., 2001; Hughes et
al., 1998; Hughes et al., 2004; Ozonoff et al., 1991). Longitudinal studies also provide
compelling evidence that the EF components of working memory, planning, and
inhibition play a critical role in the emergence of ToM in both the neurotypical and ASD
population (Flynn, 2007; Hughes et al., 2000; Pellicano, 2010; Pellicano, 2007; Tager-
Flusberg & Joseph, 2005).
Mediating factors in the relationship between EF and social competence have not
yet been examined in ASD, but have been studied in other pediatric populations. For
example, in the area of traumatic brain injury, studies by Yeates and colleagues found
that SEL skills and EF independently predict social competence (Yeates et al., 2004;
Yeates et al., 2007), and social problem solving and pragmatic language were found to
partially mediate the effects of EF on social competence in children with frontal lobe
injuries (Channon & Watts, 2003; Muscara et al., 2008). Thus, despite the complexities
of the relationship between EF, SEL and social competence, the premise that SEL skills
may mediate the influence of EF on social competence is worthy of further evaluation in
children with ASD. As reviewed earlier, tripartite models for EF and SEL represent the
best statistical and theoretical conceptualisations for such study (Ganesalingham et al.,
2006; Gioia et al., 2010; McKown et al., 2009).
2.9 Limitations of the current literature
Most previous studies focused on: (1) differences between children with ASD and
other diagnostic groups or neurotypical children; or (2) trying to explain social deficits in
40
ASD. Very few studies have explored the relationship between SEL or EF and social
competence in children with ASD. Those that did typically targeted particular SEL or EF
skills rather than explore the impact of the range of skills on social competence. While
some significant associations have been demonstrated, the relationship between EF and
SEL in relation to social competence for the ASD population is mainly speculative at this
time. Further, the measurement of social competence is problematic in many previous
studies. First, many studies that claim to examine social competence are in fact really
looking at the association between SEL skills or amongst isolated components of an SEL
skill and EF. For example, McCabe & Meller (2004) used measures of SEL skills of
affect recognition and social problem solving to assess social competence. Second, the
majority of studies in this area use the social interaction domain score from either the
Autism Diagnostic Interview-Revised (ADI-R) or Autism Diagnostic Observation Scale
(ADOS) to measure social functioning. This is problematic because the associations
between social symptoms or deficits and social ability or adaptive functioning in daily
life are weak (Klin et al., 2007). Furthermore, the ADI-R and ADOS focus on core DSM-
IV symptoms rather than on a range of everyday social behaviours in natural settings, and
therefore, information regarding a wide range of social behaviours and interactions is not
considered. For example, the ADOS relies on an in-clinic administered assessment rather
than on information obtained from more natural settings, and from those that involve peer
interactions. There is also reason to believe that the ADOS social domain scores
overestimate everyday social functioning in children with ASD. For example, in one
study, 17% of the ASD sample did not reach criteria for an ASD diagnosis on the ADOS
41
but were included because of confirmation of social impairment through comprehensive
interview and expert opinion (Landa & Goldberg, 2005).
2.10 Integration and Aims of the Current Study
Social competence clearly plays an important role in overall quality of life in
ASD. While the previous literature has demonstrated that children with ASD have
specific deficits in SEL and EF compared to neurotypical children, few studies have
examined the relationship between these variables and social competence in ASD. There
is also a wide range of social competence in children with ASD that has not yet been
fully studied.
The recent tri-partite model of EF allows a much broader conceptualization of
cognitive, behavioural and emotion regulation than has previously been used in the ASD
literature. Expanding on the previous narrow approaches to EF in the ASD literature
allows for the study of multiple possible antecedents to social competence and provides a
potential practical basis for targeted interventions. A comprehensive model of SEL has
also yet to be used in the study of children with ASD. Incorporating the empirically
validated and robust domains of SEL from the McKown model with the tripartite model
of EF provides a more comprehensive approach to studying the social competence of
children with ASD. Concurrently examining multiple domains of SEL and EF will also
allow the assessment of important relationships between the constructs. A mediation
model whereby SEL and EF impact social competence in children with ASD can also be
taken into consideration. Further, prior studies have tended to rely on social impairment
or behaviour problems rather than social ability or competence. More comprehensive
measures of social competence, such as parent observations of peer relations, social skills
42
and adaptive social behaviour are needed to better examine the relationships among SEL,
EF, and social competence (Ladd, 2005; Brown et al., 2008).
The overall aim of the current study is to thoroughly examine the relationship
between SEL and EF abilities of children with ASD in relation to social competence.
This study addresses many of the limitations of previous research in this area by
comprehensively examining SEL and EF using recent tripartite models, and by
conceptualizing and assessing social competence using a broad multidimensional
approach (see Figure 4). The goal is to contribute valuable information regarding
theoretical relationships among SEL, EF, and social competence in ASD, as well as
implications for targeted clinical intervention to enhance social competence in children
with ASD.
43
Figure 4. The proposed relationship between social
competence, SEL and EF: a partial mediation pathway using
McKown and colleagues' 2009 three-factor model of SEL as a
framework
44
3.0 Research Hypotheses
Hypothesis 1: Children with ASD will perform significantly different than neurotypical
children in specific domains of EF, SEL and social competence.
a. It is hypothesized that, overall, children with ASD will score significantly
lower than neurotypical children on all measures.
b. It is hypothesized that a greater percentage of children with ASD than
neurotypical children will score within the clinical impairment ranges on
measures of SEL, EF and social competence.
Hypothesis 2: Measures of Nonverbal Awareness, Social Meaning and Social
Understanding will represent latent SEL domains for the ASD and control
groups.
a. It is hypothesized that the factor-derived Nonverbal Awareness domain
will comprise scores from tests of facial affect and voice affect recognition
b. It is hypothesized that the factor-derived Social Meaning domain will
comprise scores from tests of ToM, pragmatic language, and empathy
c. It is hypothesized that the factor-derived Social Reasoning domain will
comprise scores from tests of social problem solving.
45
Hypothesis 3: The measures of Cognitive, Behavioural and Emotional regulation will
represent three latent EF domains for the ASD and control groups.
a. It is hypothesized that the factor-derived Cognitive Regulation domain will
comprise scores from visual attention, visual working memory, visual
attention, auditory attention, and planning.
b. It is hypothesized that the factor-derived Behavioural Regulation domain
will comprise scores from auditory inhibition reaction time, auditory
inhibition accuracy, and visual inhibition
c. It is hypothesized that the factor-derived Emotional Regulation domain
will comprise scores from affective problem solving.
Hypothesis 4: The proposed model of SEL and EF will account for significant variance in
social competence among neurotypical children and children with ASD.
a. It is hypothesized that SEL will be significantly correlated with social
competence, such that children scoring higher on measures of SEL will be
rated higher in social competence by parents.
b. It is hypothesized that EF will be significantly correlated with social
competence, such that children scoring higher on measures of EF will be
rated higher in social competence by parents.
46
c. It is hypothesized that SEL and EF will independently predict significant
variance in social competence.
d. It is hypothesized that EF will be more strongly associated with social
competence for the children with ASD than neurotypical children.
Hypothesis 5: SEL will partially mediate the effect of EF on social competence.
a. It is hypothesized that SEL will be significantly correlated with EF.
b. It is hypothesized that the EF will have a significantly smaller effect on
social competence after controlling for SEL.
47
4.0 Methods
4.1 Participant Recruitment
Ethical approval was obtained from the University of Regina, the Regina
Qu'Appelle Health Region and Five Hills Research Ethics Boards (see Appendix A).
Children with ASD were recruited through three Saskatchewan-based Autism Programs:
The Autism Centre in Regina (Regina Qu’Appelle Health Region), Autism Spectrum
Disorders Program in Moose Jaw (Five Hills Health Region) and Autism Services of
Saskatoon. The coordinator at each program emailed the recruitment letter (see Appendix
B) to the parents of children with ASD registered in their program. The researcher also
attended ASD parent support meetings and parent psycho-educational groups at the
Autism Centre to give a brief presentation about the study. Potential participants were
provided the recruitment letter and information on how to contact the researcher. Posters
about the study were put up at the Autism Centre and contact information was available
at the reception desk. Finally, social media was used to recruit children. The moderator of
the Saskatchewan Parents of Children with Autism Spectrum Disorders Facebook group
posted the recruitment flyer for the study on the group webpage.
Children for the control group were recruited through a variety of means in the
Regina area. These included posts on Facebook and emails to friends requesting that they
forward to anyone who may be interested; emails to University of Regina and Regina
Qu'Appelle Health Region employees; posters at pediatrician's offices, in community
centers and grocery stores. The researcher also handed out recruitment flyers to parents at
community soccer, baseball, and football games. The families who expressed interest in
48
the study were provided written information about the study and the researcher's contact
information.
4.2 Data Collection
Prior to participating in the study, parents were provided an electronic copy of the
consent and assent forms to read (see Appendix C). Data collection occurred at the
University of Regina and University of Saskatchewan. Upon arrival, parents were
provided a written copy of the consent form and signatures were obtained. Parents of
children with ASD provided diagnostic reports or their consent to access health records to
confirm the diagnosis of ASD, which had previously been made by either psychiatrists or
psychologists based on DSM-IV-TR criteria (American Psychiatric Association 2000) as
part of the clinical care of the child. Parents of children in the control group provided
verbal confirmation that their son did not have any mental health diagnoses or learning
disabilities. Before beginning, the researcher reviewed the assent form with the child and
provided a Cineplex Cinema gift certificate worth $10.00. Children were then provided a
visual schedule of all tasks they would be doing and instructed that they could ask for a
break at any time. Individual testing sessions took approximately 100 to 130 minutes.
Participants chose from a basket of small rewards (e.g. Mario cards; Star Wars stickers)
at the end of the session.
Parents completed the demographic form and questionnaires while the researcher
was administering the measures to the child in an adjoining room. All testing was
completed individually in a room free from distractions. The administration of the tasks,
which was the same for all participants, was in counterbalanced order, alternating
between SEL and EF measures. While the children were doing a task on the computer
49
and again at the end of the session, the researcher reviewed the parental forms to ensure
full completion.
4.3 Clinical File Review
As per RQHR ethics approval, the researcher reviewed 31 health records from the
RQHR to confirm diagnoses. Eighteen parents of children with ASD provided
psychological or psychiatric assessment reports as confirmation of an ASD diagnosis.
Intellectual assessment scores for 11 children with ASD were obtained from the RQHR
file review. These 11 children had completed the Wechsler Intelligence Scales for
Children (WISC-IV) within the past 3 years and their full scale IQ scores were used for
the analyses.
4.4 Participants
A sample of 99 boys, aged 8-13 years and their parent(s) participated in this
study. The clinical group consisted of 51 boys with ASD and the control group consisted
of 48 boys without mental health diagnoses or learning disabilities. Data from two
participants with ASD were not used in the analyses because to the researcher they
seemed to demonstrate inadequate comprehension of the instructions for the tasks. In
terms of ASD diagnoses, seven children had a diagnosis of autistic disorder, 22 had a
diagnosis of Pervasive Developmental Disorder Not Otherwise Specified and 20 had a
diagnosis of Asperger’s Disorder. The children with ASD had received their diagnosis
between 2.5 and 10 years of age (M = 6.12, SD = 1.97).
Table 1 provides demographic information on the children and parents in this study. As
intended, the two groups did not significantly differ in terms of age, F(1, 96) = 1.10, p =
.30, family income, F(1, 96) = 3.21, p = .076, or parent education level, F(1, 96) = 3.73, p
50
= .06. Parents in both groups reported high family incomes (77% percent of participants
had annual family incomes over $75 000) and high levels of education (86% of parents
had completed post-secondary education). Children in the control group had slightly
higher full scale IQ scores (m= 110.81, SD=10.00) than the children in the ASD group
(m= 105.69, SD=13.77), F(1, 96) = 4.37, p = .04. None of the children in the control
group were taking any stimulant medications or other psychopharmacological
interventions and 18 boys (36.7%) from the ASD group were on stimulant medication
specifically targeting attention problems, including Methylphenidate (Concerta),
Lisdexamfetamine (Biphentin), Dimesylate (Vyvanse) and Atomoxetine (Strattera).
4.5 Child Measures
Children were administered a measure of intellectual functioning to estimate IQ, 6
SEL tasks, and 8 EF tasks. A complete list of measures and can be found in Appendix E.
4.5.1 Intellectual ability. The Wechsler Abbreviated Scale of Intelligence
(WASI) (Wechsler, 1999) was used as a brief estimate of intellectual ability. The WASI
is part of a wide series of individually-administered Wechsler standardized instruments
designed to assess general intelligence, IQ. The WASI has a wide age range (6-89 years)
and is commonly used to estimate general intelligence in studies involving child clinical
populations (Blunden, Lushington, Lorenzen, Martin & Kennedy, 2005; McClure et al.,
2005; Raggio, Scattone & May, 2010), including ASD (Barnea-Goraly et al., 2004;
Fuentes, Mostofsky & Bastian, 2010; Garcia-Nonell et al., 2008). The WASI uses a
measure of Verbal Comprehension (Vocabulary) and Perceptual Reasoning (Matrix
Reasoning) to provide an estimate of full scale IQ (FSIQ). The construct validity of the
WASI has been demonstrated through various convergent validity coefficients (r= 0.85 to
51
Table 1.
Demographic Variables for ASD and Control Group
ASD
Mean (SD)
CONTROL
Mean(SD)
F
p
N
49
48
Age (yrs.)
Range
10.0 (1.60)
8-13
10.31(1.69)
8-12
.36 .73
IQ
Range
105.69(13.77)
83-140
110.81(10.00)
90-131
4.37* .04
Education(yrs.)
Range
15.27(2.19)
8-16
16.13(2.20)
8-22
3.73 .06
Income
Range
105 549(43 068)
<25 000-
>150 000
120 000(36 495)
67 500-
>150 000
3.21 0.08
*indicates significance at p<.05
52
0.89) (Canivez & Konold, 2009; Hays, Reas & Shaw, 2002) and exploratory and
confirmatory factor analyses (Canivez & Konold, 2009). Correlations between the WASI
estimated IQ score and the WISC-IV FS IQ is .83 (Saklofske, Prifitera, Rolphus, Zhu &.
Weiss, 2005).
WASI subtests are scaled in T score units (M =50, SD = 10), and the IQ scores are
scaled in traditional IQ - standard score units (M = 100, SD =15). The WASI Vocabulary
and Matrix Reasoning subtests were administered to all children in the control group and
to those children in the ASD group without a prior IQ score. Standard administration and
scoring were conducted according to instructions in the manual.
4.5.2 Social Emotional Learning. SEL was assessed using both structured and
semi-structured tasks. SEL domains that were assessed included Nonverbal Awareness,
Social Meaning, and Social Reasoning.
Tasks of facial and voice affect recognition were used to evaluate Non-Verbal
Awareness. The Diagnostic Analysis of Nonverbal Accuracy 2 (DANVA – 2; Nowicki &
Duke, 1994) was used to measure nonverbal awareness. The DANVA-2 measures the
ability to identify nonverbal cues regarding the emotional states of others. Reliability and
validity data for these measures suggests it is an appropriate measure of facial affect and
voice affect recognition for 8 to 13 year old children (Nowicki 1997; Nowicki & Carlton,
1993; Nowicki & Duke, 1994; Nowicki & Mitchell, 1998). In a trend analysis of 8
studies, accuracy scores were shown to increase with age (Nowicki and Rowe, 1997) and
social competence (McKown et al., 2009).
In this study, children were asked to identify whether faces or voices were happy,
angry, sad or worried. In the Child Faces condition, participants viewed 24 photographs
53
of children between the ages of 7 and 12 on the computer. The pictures included 12
female and 12 male expressions showing an equal number of happy, sad, angry and
fearful faces of high and low intensity. In the Child Voices condition, the participants
made judgments about the emotions of others from audio recordings of children repeating
the same sentence ("I’m going out of the room now but I’ll be back later"). The
recordings included 12 male and 12 female voices with equal amounts of happy, sad,
angry and fearful tone of voice of high and low intensity. The four emotions were printed
on a piece of paper to help participants remember the choices. The number of correct
choices and errors was tallied for each test. Raw scores were used for most analyses but
standard scores were also derived from a table of age norms.
Tasks of ToM, empathy, and pragmatic language were used to evaluate Social
Meaning. The Strange Stories task (Happe, 1994; White, Hill, Happé, & Frith, 2009) was
used to assess ToM. The Strange Stories were originally designed for children and
adolescents with ASD and has become one of the most widely used ToM tasks in
published research (Miller, 2009). The Strange Stories task is considered a second-order
reasoning task designed to assess the ability to understand mental states such as belief,
intention and deception, as well as the ability to understand that others have their own
thoughts and ideas which may be different from their own. It consists of a series of brief
vignettes in which participants have to accurately identify the underlying intention behind
a character's statement. Normative data for the Strange Stories is based on typically
developing 5-12 year old children (O'Hare et al., 2009). Strong validity, inter-rater and
test-retest reliability have consistently been reported across studies (Hughes et al., 2000;
Kaland et al., 2002; Kaland et al., 2005; Korkman, Kirk, & Kemp, 2007; O'Hare et al.,
54
2009). Performance on Strange Stories has been significantly correlated with age
(McKown et al., 2009) but not IQ (Happe, 1994; Jolliffe & Baron-Cohen, 1999; Kaland
et al., 2008). Five vignettes from Happe’s (1994) set were used in this study. All
participants were provided a written copy and also read the scenario and then asked two
questions. The first question checked for comprehension ("was it true, what X said?") and
the second question asked for justification ("Why did X say/do that"?), which requires
knowledge of another individual’s point of view to answer correctly. Scoring is based on
the participants' response to the mental state question, which can be scored as 0
(incorrect), 1 (partial) or 2 (full and accurate, which involves thoughts, feelings, desires).
Total number of correct items was used as the raw score.
The Bryant Empathy Index (BEI) was used to assess affective empathy. The BEI is
a widely used seven-item self-report questionnaire that measures a child's empathy-
related responses. Consistent with previous studies (Krevans and Gibbs, 1996; Michalska
et al., 2013; Robinson & Strayer 2007; and Zoll & Enns, 2005), questions tapping attitude
were eliminated and the seven questions measuring affective empathy from the BEI were
used in the current study. Participants rated to what extent they would feel a given
emotion based on various situations, such as "It makes me sad to see a boy who can’t find
anyone to play with" and "I really like to watch people open presents, even when I don’t
get a present". The affective portion of the BEI has good internal consistency (Aristu et
al., 2008, del Barrio et al., 2004 & de Wied et al., 2007) and demonstrates strong test–
retest reliability for 8 -15 year old children (Bryant,1982; de Wied et al., 2007; Krevans
& Gibbs, 1996). Concurrent validity has been demonstrated through significant
correlations with the Empathy Continuum (deWied et al, 2005; Cohen & Strayer, 1996;
55
Fraser, 1996; Krevans & Gibbs, 1996; Strayer, 1993) and with the Eisenberg self-report
questionnaire (Krevans & Gibbs, 1996). In this study, the seven questions were read to
the children and they were also provided a written copy. All children understood the
procedure, as indicated by their responses to trial items such as "I like ice
cream/chocolate" and "I like soap in my eyes" (taken from Strayer & Roberts, 1989). A
4-point visual rating system was used in which participants chose whether the statement
was 'not at all true' (0), 'a little true' (1), 'pretty much true' (2) or 'very much true' (3). The
parents were also asked to complete this questionnaire based on their observations of
their sons' behaviours in order to examine whether participants' self-ratings of empathy
corresponded to parental ratings. The sum of points was used as the total raw score.
Pragmatic language was assessed using the Test of Pragmatic Language – Second
Edition (TOPL-2; Phelps-Terasaki and Phelps-Gunn, 2007). The TOPL-2 is a 43-item
norm-referenced oral language assessment for children aged 6 to 18. It is a well-designed
assessment tool that provides a comprehensive evaluation of pragmatic language and
allows the evaluation of an individual's ability to view a social situation and make
judgments from the vantage point of an objective bystander (Hoffman et al., 2013). It
assesses seven underlying areas of pragmatic language, including Physical Context,
Audience, Topic, Purpose, Visual-Gestural Cues, Abstractions, and Pragmatic
Evaluation. The TOPL-2 used a standardization sample of 1136 children between the age
of 6 and 18. Good content, criterion and construct validity were demonstrated in the
development of the TOPL-2. Concurrent and discriminant validity was also established in
recent independent studies (Hoffman & Martens, 2013; Volden & Philips, 2010). The
TOPL-2 was also demonstrated to predict outcomes beyond the contribution of general
56
language skills and nonverbal IQ (Volden & Philips, 2010). In this study, the 18
questions from the Pragmatic Evaluation component were administered to all participants
in order to reduce redundancy and overlap with other measures of SEL, such as facial-
affect recognition (visual-gestural cues), and ToM (abstractions). The Pragmatic
Evaluation questions measure the awareness of rules of language in relation to specific
social situations, while considering the situational context and intent of the
communication (Phelps-Terasaki & Phelps-Gunn, 2007). Participants were shown
pictures of common social situations, read a short vignette, and then asked to generate a
response for one of the characters in the picture. One example depicts a boy holding
broken roller blades whose parents are telling him they do not want to buy him new ones
because he didn't take care of his old ones. The child is first asked, “What can Chad say
to talk his parents into getting him new rollerblades?" and subsequently "how do you
know what he says will talk them into it?". The child’s response was scored as correct (1)
or incorrect (0) according to criteria provided in the TOPL-2 manual. The sum of the
points for the 18 questions was used as raw score.
The Test of Social Problem Solving (TOPS-3E: Bowers, Huisingh & LoGiudice,
2005) was used to evaluate Social Reasoning.The TOPS-3E is a standardized measure of
social reasoning composed of 18 photographs that depict different social situations.
Children are asked to interpret information about social situations by responding to
questions based on six areas of social reasoning. These include defining problems,
identifying the causes of social situations, predicting outcomes, understanding social
conventions, and generating solutions to social problems. For example, for one picture of
a group of children sitting on the lawn, participants are told "this group was coming home
57
from their fieldtrip", then asked a series of questions, such as "how can you tell
something went wrong on their way home" and also "they've been waiting a long time,
why hasn't the school sent another bus to pick them up".
The TOPS-3E has a normative sample of over 1000 six to twelve year-old
children. Strong validity, test–retest reliability and inter-rater reliability for the
elementary edition have been demonstrated (Bowers et al. 2005; Zachman, Huisingh,
Barrett, Orman & LoGiudice, 1994). Strong correlations with a social information
processing interview were also reported (McGee et al., 2009). The TOPS-3E has been
used in several studies with children with ASD (Baghdadli, Binot-Dubois, Pinot &
Michelon, 2010; Stichter, O’Connor, Herzog, Lierheimer & McGhee, 2012; Stichter,
O'Connor, Hertzog, Lierheimer & McGhee, 2012) as well as children with FASD
(McGee, Bjorkquist, Price, Mattson & Riley, 2009), with both clinical groups performing
significantly worse on the TOPS-3E than neurotypical children (Baghdadli et al., 2010;
McGhee et al., 2009). Participants in this study were administered an abbreviated version
of the TOPS-3E which included answering 50 questions based on 9 pictures targeting all
6 areas of social reasoning. Responses were scored on a 3-point scale (0-2) according to
criteria provided in the TOPS-3E manual. Total points were tallied and used as raw the
score.
4.5.3 Executive function. EF was assessed using structured performance-based
tasks and parent ratings. The performance-based EF tasks were chosen on the basis of
their reported sensitivity to EF in children within the 7-12 year old range, which
corresponded well with the age range of the study sample. The measures that were used
58
assessed a theoretically meaningful range of executive functions. The EF domains that
were assessed included measures of Cognitive, Behavioural and Emotion Regulation.
Measures of attention, planning and working memory were used to evaluate
Cognitive Regulation. The Auditory Attention subtest of the NEPSY-II (Korkman, Kirk
& Kemp, 2007) was used to assess auditory selective and sustained attention. The
standardization sample of the NEPSY-II included 1200 children between the ages of 3
and 16. Strong psychometric properties for reliability and validity were reported for the 8
to 13 year-old range. Construct validity was obtained by various means, including expert
review, extensive literature reviews, concurrent validity and empirical clinical
evaluations with clinical populations. For this task, children are required to point to a
colored circle each time they hear a target word in a series of random words on an audio-
recording. Accuracy scores were calculated by summing total items correct on the target
words, with a maximum score of 30.
The Tasks of Executive Control (TEC: Isquith, Roth, & Gioia, 2010) is computer-
administered measure that was used to assess visual attention and visual working
memory. The visual attention task is a simple choice response task. Participants are asked
to sort all zebras appearing in the center of the screen into a red box, while non-zebra
objects are to be sorted into the blue box. Sorting is done by pressing the relevant shift
key, identified with a red or blue dot, with the corresponding right or left hand.
Participants were not allowed to proceed with the task until they demonstrated adequate
understanding of the task (defined as correctly completing the 10 practice items for each
condition). Participants performed 100 trials per each task. The working memory task
uses an n-back paradigm – a well-established measure of working memory (Owen,
59
McMillan, Laird, & Bullmore, 2005). Participants are required to monitor a series of
visual stimuli and to respond whenever a stimulus is presented that is the same as the one
presented in a previous trial from n-steps back in the sequence. Two levels of working
memory demand were presented (1, 2-back). In the first working memory task,
participants were asked to place an object in the red box if it appeared twice in a row (1-
back). In the second working memory task, participants were asked to place objects in the
red box if it appeared 2 objects back in the sequence (2-back).
Psychometric evaluation of the TEC has been primarily conducted through the
standardization sample of 1138 children aged 5 to 18 years old, although some recent
studies have demonstrated convergent validity with other measures of EF and in clinical
populations, including ADHD (Gomez-Guerrero, 2011; Mairena et al., 2012) and mild
TBI in children (Isquith, Roth & Gioia, 2010; Krivizty et al., 201; Roebuck-Spencer,
Roth, Blackstone, Johnson & Gioia, 2011; Wolfe et al., 2013). The computerized scoring
produces accuracy scores for the two tasks. Visual attention has a maximum score of 100
and working memory tasks have a combined maximum score of 40.
The Tower of London Drexel University – Second Edition (TOLDX
-2: Culbertson
& Zimmer) was used as a measure of planning ability. This task is modeled after
Shallice’s (1982) Tower of London. In this modified version, children are asked to
rearrange three different colored beads situated on three vertical pegs of descending
height, in order to replicate a pattern on the examiner's peg board. Participants were
instructed to replicate the examiner's bead configuration in as few moves as possible
without violating two rules (moving only one ball at a time directly from one peg to
another; and not putting more beads on a peg than it will accommodate). There are 10
60
trials, which increase in difficulty from a minimum of three to seven moves. The validity
of the Tower task has been demonstrated in numerous neuropsychological studies
conducted during the past few decades (Kaller, Unterrainer & Stahl, 2012; Swanson,
2005). The ToLDX
-2 standardization sample consisted of 990 7 to 80 year olds, of which
370 were between the ages of 8 and 12. Criterion, convergent and divergent validity have
been established, in addition to moderate to high test-retest reliability. Numerous
published studies have demonstrated that the ToLDX
-2 is sensitive to executive-function
deficits in clinical populations, including children with ADHD (Culbertson & Zillmer,
1998, 2005), pediatric traumatic brain injury (Donders & Larsen, 2012) and ASD
(Wallace, Silvers, Martin & Kenworthy, 2009). The children's version (7-15 years old) of
the ToLDX
-2 was administered to all participants. The raw 'total moves' score was used
for analysis..
Behavioural regulation was measured using two inhibition measures, one from the
TEC and one from the NEPSY-II. The TEC measure is a visual inhibition task that uses
a go/no-go paradigm, in which a child must execute a response to visual stimulus 'A' but
withhold a response to visual stimulus 'B'. It involves a continuously presented series of
visual stimuli composed of frequent “go” cues to which participants respond as rapidly
as possible and infrequent “no-go” cues to which participants are not to respond. The
higher frequency of go cues creates a dominant tendency to respond that must then be
inhibited for no-go cues (Schulz et al., 2007). The computerized scoring is based on
accuracy of targets correct, with a maximum of 20. The NEPSY-II measure is an
auditory inhibition task that assesses the ability to inhibit automatic responses in favour
of novel ones. The task is a variation of the Stroop Color and Word Test (Homack &
61
Riccio 2004; Stroop, 1935). There are two components to this task. For the naming task,
participants are asked to name a series of shapes and direction of arrows (up and down).
In the inhibition component, participants are asked to name the opposite shape (e.g. when
see circle say square) or direction (when the arrow is up say down). The reliability and
validity of this task is well-established for the 7-12 year old range (Korkman et al., 2007).
Scores are based on completion time and errors (a combination of self-corrected and
uncorrected errors).
The Hungry Donkey Task (HDT: Crone & van der Molen, 2004) was used to
assess Emotional Regulation; it is an affective decision making measure based on the
Iowa Gambling Task (IGT: Bechara, Damasio, Damasio, & Anderson, 1994), a well-
established and widely-used adult measure. In the HDT, participants are asked to ‘‘feed’’
as many apples as possible to the hungry donkey by selecting from either two or four
doors, which have different proportions of gains and losses of apples. The premise is that
the tasks resemble real-life decisions in terms of reward, punishment, and uncertainty of
outcomes (Crone, Bunge, Latenstein, & van der Molen, 2005). There are three versions
of the Hungry Donkey task. Task complexity and punishment frequency are manipulated
in each version by using two or four doors and a 10% or 50% punishment schedule
(Crone et al., 2005). For this study, the two choice - 10% condition was chosen because
the two-choice version reduces demand on working memory (Kerr and Zelazo, 2004) and
developmental changes in decision making are only apparent when the loss is infrequent
(Crone et al., 2005).
Discriminant validity for the HDT has been demonstrated in several studies
(Crone, Vendel & van der Molen, 2003; Geurts et al., 2006; Hooper, Luciana, Conklin &
62
Yarger, 2004; van Duijvenvoorde, Jansen, Visser & Huizenga, 2010). Age was positively
associated with making more advantageous choices (Crone et al., 2004, 2005, 2007;
Huizenga, Crone & Jansen, 2007), indicating HDT performance is sensitive to
developmental changes in childhood and adolescence. Furthermore, individual
differences in 8-12 year old children’s affective decision making could be detected at the
neural level (P300) (Carlson, Zayas & Guthormsen, 2009) and physiological level (Crone
et al., 2005) when losses incurred.
For this study, participants had to choose between two identical decks of cards,
behind which unpredictable losses of 10 or 50 apples were presented on 10% of the trials.
All pictures presented for this task were obtained from Inquisit Millisecond software. On
the side facing down, door A has either a picture of 2 apples or a picture of 10 apples
with an X through them while door B has either a picture of 4 apples or a picture of 50
apples with an X though them. Immediately after indicating their response and turning
over a card, the examiner either placed beads in or took them out of a glass jar in
correspondence to the card selection. This provided a visual stimulus for the gains and
losses of apples. In addition, prior to beginning the task, participants were shown a bin of
high-desire prizes and told ‘‘if at the very end of the game, you have won more apples
than you have lost, then you can chose a prize from this toy bin" in an effort to increase
their motivation and personal desire to win (as per Crone & van der Molen, 2004). In
reality, all participants were invited to select a prize at the end.
The HDT consisted of 100 trials. Door A is advantageous in the long run because
it results in smaller immediate gain and smaller unpredictable losses whereas Door B
results in high immediate rewards but also much higher unpredictable losses. Net
63
difference scores were calculated by subtracting the number of disadvantageous choices
(Door B) from number of advantageous choices (Door A) (Bechara et al., 1994; Crone &
van der Molen, 2004; Skogli, Egeland, Anderson, Hovik & Oie, 2013).
4.6 Parent Measures of EF and social competence
Parents completed a questionnaire package that included ratings of their child’s
EF and social competence; demographic data was also obtained from the parents at this
time (demographic questionnaire is in Appendix F).
4.6.1 Ratings of EF. The Behavior Rating Inventory of Executive Functions
(BRIEF: Gioia, Isquith, Guy, & Kenworthy, 2000) was used to obtain parent ratings of
children's EF. The BRIEF is a psychometrically sound and well-established standardized
parent report inventory of executive functioning for 5 to 18 year olds (Baron, 2000;
Strauss, Sherman & Spreen, 2006). The BRIEF is often used as a complement to
traditional performance-based measures of executive function to provide information
regarding everyday application of executive functions. It is sensitive to a broad range of
neurologic and developmental conditions such as ASD (Gilotty et al., 2002; Kenworthy
et al., 2008), ADHD (Reddy, Hale, & Brodzinsky, 2011; Toplak, Bucciarelli, Jain, &
Tannock, 2009) and TBI (Mangeot, Armstrong, Colvin, Yeates & Taylor, 2002). The
standardized Parent Report version comprises 86 items that tap a wide range of executive
functions involved in the regulation of attention, behavior and emotion.
The BRIEF was normed on a sample of 2139 children between the ages of 5-18,
of which 1191 were 8 to 12 year olds (Gioia et al., 2000). Reliability was demonstrated
through internal consistency, interrater reliability and test-retest reliability. Validity for
the BRIEF was demonstrated though content and construct validity. The BRIEF was
64
originally produced with eight subscales and two indices (Metacognition and Behavioral
Regulation). However, exploratory and confirmatory factor analyses based on both parent
and teacher report subsequently identified nine subscales and three indices: Cognitive,
Behavioural and Emotional Regulation (Egeland & Fallmyr, 2010; Gioia, Isquith,
Retzlaff, & Espy, 2002).
The BRIEF is the most widely-used parent EF rating for clinical use and research
(Donders, DenBraber & Vos, 2010; Toplak, West, & Stanovich, 2013) and it has
demonstrated its reliability and validity as a measure of everyday executive function
(Huizinga and Smidts, 2013; Kenworthy, Yerys, Anthony, & Wallace, 2008; Mahone et
al., 2002; Mangeot et al., 2002; Toplak, Bucciarelli, Jain, & Tannock, 2009). Of note,
significant age effects have been reported, indicating that parental ratings of executive
functions improved with age throughout childhood on all scales (Huizinga and Smidts,
2013). The BRIEF has also been significantly correlated to measures of adaptive
functioning in a small group of children (n=53) with ASD (Gilotty et al., 2002) and
predicted severity level of injury and adaptive and problem behaviours in a group of 189
children with TBI (Mangeot et al., 2002).
Parents rated the frequency of their child's behaviour on 3-point scale (never;
sometimes; often), with higher scores indicating more difficulties with executive
functioning. Questions include "underestimates time needed to finish tasks", "has trouble
thinking of different way to solve a problem when stuck", "needs to be told to begin a
task even when willing" and "has explosive angry outbursts". The nine subscales of the
BRIEF were calculated in this study (Initiate, Working Memory, Plan/Organize,
Organization of Materials, Task Monitor, Self Monitor, Inhibit, Shift and Emotional
65
Control) according to procedures described in the literature (Egeland & Fallmyr, 2010;
Gioia et al., 2002). Scores for the total Global Executive Composite (GEC) were
calculated by summing all points according to procedures in the manual.
4.6.2 Ratings of social competence. Social competence was measured using the
Social Skills Improvement Rating System (SSIS 2008: Elliott & Gresham) and the
Socialisation Domain of the Vineland Adaptive Behavior Scales, Second Edition (VABS-
2: Sparrow, Cicchetti & Balla, 2005). The SSIS is the most widely-used measure of
social skills in children (White et al., 2007) and the VABS-2 is the most widely-used
adaptive measure in ASD research (Lopata et al., 2013).
Parent ratings of the SSIS were used to measure functional pro-social behaviours.
The SSIS parent questionnaire includes 46-items divided into seven subscales targeting
communication, cooperation, assertion, responsibility, empathy, engagement and self-
control. Examples of questions include "invites others to join in activities", "makes eye
contact when talking " and "stays calm when teased". The US national standardization
sample of the SSIS was large (n = 4,550) across 3 broad age groupings (3-5 years, 5-12
years, and 13-18 years). Validity has been demonstrated by moderate to high correlations
with other widely used instruments such as the Behavioral Assessment System (BASC–2;
Reynolds & Kamphaus, 2004) and the SSRS (Gresham & Elliott, 1990). Internal
consistency and test–retest reliability for parent ratings was strong (Elliot and Gresham,
2008; Gresham et al., 2010). Parents indicated the frequency with which their child
exhibits each social skill on a 4-point scale of never, seldom, often, and almost always. A
total score was derived from summing points from 7 subscales according to standardized
procedures.
66
Parent ratings on the Socialization domain of the VABS-2 was used as the second
measure of social competence. The VABS-2 Socialisation Domain is a commonly used
measure of social competence in clinical populations, particularly ASD (Gillespie-Lynch
et al., 2012). It measures a range of personal and interaction skills needed for everyday
adaptive social behaviour and independence in three areas: interpersonal relationships
(how child interacts with others), play (how child uses toys and leisure time) and coping
skills (how child demonstrates sensitivity to others and manages social challenges).
Examples of questions include "invites friends over", "takes turns without being asked"
and "acts when another person needs a helping hand (for example, holds door open, picks
up dropped items". The VABS-II was standardized using a representative American
sample of 3,695 children (Sparrow, Cicchetti & Balla, 2005). Concurrent and divergent
validity and test-retest and inter-rater reliability of the Socialization domain are well-
documented in the manual and independent studies (Anderson, Oti, Lord & Welch, 2009;
Perry et al., 2009). Parents indicated the frequency with which their child performs social
behaviours on a 3-point scale of never, sometimes or partially, and usually. Scores were
derived from summing points on the three subscales according to procedures in the
manual.
67
5.0 Results
5.1 Data Analysis Plan
All analyses were performed using IBM SPSS 21.0. Preliminary data screening
was performed to examine basic assumptions of parametric data. Outliers were examined
using Boxplots and assumptions of normal distribution were assessed via group-based
histograms and Q-Q Plots. Homogeneity of variance was assessed via Levene’s statistic.
There was no missing data and for all analyses p < .05 was considered statistically
significant.
One outlier was identified from the parent ratings of the ASD group and
subsequently removed from all analyses involving social competence scores or parent EF
ratings. This outlier was discarded because the SSIS and Vineland scores were over three
standard deviations higher than the mean of the ASD group and it was having significant
impact on the results of the correlation and regression analyses. However, since the
adjoining child-based SEL and EF scores for this participant were not outliers and all
analyses subsequently performed with and without this participant's EF and SEL scores
found no significant differences in outcome, the child data was retained for group
comparisons.
In order to compare performances across scales and age ranges, two scores were
calculated. First, scores were transformed to an age-corrected z-score because
standardized scores were not available for all tasks. Since children's performance on
many skill-based measures of EF and SEL normatively improves with age
(Ganesalingham et al., 2006; McKown et al, 2009; McKown et al., 2013; White et al.,
68
2010), the same raw score has a different meaning for children at different ages. Thus, to
minimize the impact of age, the scores on the SEL and EF tasks were converted to an
age-corrected standard score. To do so, each score was regressed separately on age and
then the residual score, which indicates the individual variance not explained by age, was
used to calculate z-scores based on the control group's mean and standard deviation.
These z-scores were then used to examine group differences. Second, in order to
investigate levels of clinical impairment, the percentage of children who scored one
standard deviation or more below the normative mean was calculated (McKown, 2007;
Rasmussen et al., 2013) for parent and child measures when standardized norms were
available.
The results are divided into six sections. In the first section, descriptive analyses
are provided. The five sections that follow are divided according to the main research
hypotheses. These include group comparisons on all measures; creation of SEL and EF
domains and calculation of composite scores; correlations and regressions of SEL and EF
domains with Social Competence to determine the extent to which SEL or EF predicts
social competence; and finally, further correlational analyses between SEL and EF to
evaluate the hypothesized partial mediation relationship.
5.2 Descriptive Information
Since 37% of children from the ASD group were taking stimulant medications,
analysis of variance (ANOVA) was conducted to determine whether there were any
significant within-group differences on predictor and outcome measures between those
children taking medication and those who were not. There were no significant within-
group differences between the children taking medication and those not taking
69
medications on any child measures, but there were significant differences on the SSIS,
F(1, 47) = 11.40, p < .01 but not on the Vineland, F(1, 47) = 3.12, p = .08, or the BRIEF,
F(1, 47) = 3.84, p = .06. On further analysis, within-group differences on the SSIS were
only significant for 8 to 10 year old children, F(1, 23) = 10.37, p < .01, but not the 11 to
13 year olds, F(1, 22) = 4.14, p = .06.
5.2.1 Correlation analysis. Pearson product-moment correlation coefficients
were calculated to examine the relationship between age, IQ and all predictor and
outcome measures. The analyses were conducted separately for the control and ASD
groups (see Tables 2-5).
Age. Preliminary analysis examined the relationship between age and raw scores
on each measure. For the control group, age was positively correlated with 4 of 6 SEL
and 7 of 8 EF measures but not with parental EF ratings. In terms of SEL, performance
increased with age on facial affect recognition, r(48) = .37 , p =.01, voice affect
recognition, r(48) = .37, p <.01, pragmatic language, r(48) = .59, p < .01 and problem
solving, r(48) = .396, p < .01. In terms of EF, performance improved with age on all EF
measures except for affective decision making. Specifically, there were significant
correlations between age and visual attention, r(48) = .40 , p < .01, auditory attention,
r(48) = .31 , p < .01, working memory, r(48) = .383, p < .01, planning, r(48) = -.30, p =
.04, visual inhibition r(48) = .41, p < .01, auditory inhibition time, r(48) = -.32, p = .029
and auditory inhibition accuracy r(48) = -.33, p = .02. As expected, age was positively
correlated with raw scores on both the Vineland, r(48) = .40 , p < .01 and SSIS, r(48) =
.39, p < .01, indicating that social competence increased with age. When using standard
scores, there was a significant correlation between age and the SSIS, r(48) = 0.35 p = .01,
70
but not the Vineland, r(48) = -.11, p = .45 or the BRIEF, r(48) = 0.10, p = .49. For
children in the control group, older children performed better on most measures of SEL
and EF and obtained higher parent ratings of social competence and EF.
Age was not significantly correlated with any SEL task for children with ASD.
Age was, however, significantly correlated with four EF tasks. As age increased,
performance improved on visual attention, r(49) = .40 , p < .01, working memory, r(49)
= .36, p = .01, auditory inhibition (time), r(49) = -.34, p = .02 and affective decision
making, r(49) = .28, p = .05. There were no significant correlations between age and raw
scores on the Vineland, r(48) = .03, p = .86, the SSIS, r(48) = -0.05, p = .71, or the
BRIEF, r(48) = .04, p = .80. When using standard scores, there was a negative
association between age and the Vineland, r(48) = -0.53, p < .01 but not on the SSIS,
r(48) < 0.01, p = .94 or the BRIEF, r(48) = 0.07, p = .64. These results indicate that
performance on SEL, social competence and parent-rated EF did not improve with age
for the children with ASD. In fact, the standard scores suggest that there is an inverse
relationship between age and social adaptation. The significant correlations between age
and measures of SEL and EF are problematic because it indicates that the same raw score
has a different meaning for children at different ages. As such, age-corrected z scores
were calculated as described previously and used for all subsequent analyses.
IQ. For the control group, IQ was not significantly associated with any
performance-based EF measures or parent ratings of EF, but was positively associated
with 2 SEL tasks. Higher IQ scores were associated with higher scores on the Voice
71
Table 2 Correlations among measures of Age, IQ, measures of SEL and social competence for the CONTROL group
Variables 1 2 3 4 5 6 7 8 9
1. Age .
2. IQ -.03
3. face affect .34* .02
4. voice affect .37* .31* .32*
5. pragmatic 1
.59* .19 .34* .46*
6. empathy .-.05 -.30* .07 .08 .23
7. ToM .08 -.10 .07 .04 .09 .03
8. prob solving2
.40* .20 .22 .29* .55* .21 -.06
9. Vineland -.11 -.21 .36* .09 .28 .17 .36* .45*
10.SSIS .35* -.34* .27 -.13 .20 .29* .11 .40* .61*
Note: Age-corrected z scores used for SEL and standard scores used for Vineland and SSIS 1
Pragmatic Language; 2Social Problem Solving
P<.05
72
Table 3.
Correlations among measures of Age, IQ, measures of SEL and social competence for
the ASD group
1 2 3 4 5 6 7 8 9
1. Age .
2. IQ -.05
3. facial
affect .06 .31*
4. voice
affect .20 .33* .47*
5. prag1
.17 .36* .27 .36*
6. empathy -.23 -.08 .04 -.08 .02
7. ToM .27 .37* .34* .27 .56* .10
8. prob solving
2
.20 .46* .25 .46* .61* -.06 .50*
9.Vineland -.53* .05 .31 .08 .30* .09 .19 .26
10.SSIS .01 .14 .08 .10 .16 .18 .01 .18 .62*
Note: Age-corrected z scores used for SEL and standard scores used for Vineland and SSIS
1Pragmatic Language;
2Social Problem Solving
P<.05
73
Table 4
Correlations between Age, IQ, measures of EF and social competence for the
CONTROL group
1 2 3 4 5 6 7 8 9 10 11 12
1. Age . .
2. IQ -.03
3.aud
attn1
.31* -.06 .
4.vis
attn2
.40* .19 .06
5.WM3
.38* .11 .36* .54*
6.plan -.30* .13 .13 -.04 .22 .
7. inhib
time4
-.32* -.14 -.13 -.25 -.28 .20
8.inhib
errors5 -.33* -.25 -.15 -.22 -.23 .06 .32*
9. visual
inhib6
.41* .10 .39* .66* .61* -.01 -.23 -.34*
10.dec
making7
.06 -.25 .13 -.05 -.16 -.22 .21 .13 .01
11. BRIEF .10 -.00 .14 .15 .02 .16 -.13 .22 -.06 .06
12.
Vineland .-.11 -.21 .09 -.00 .10 -.34* -.09 -.15 .15 .18 -.39*
13. SSIS .35* -.34* .09 .02 .08 -.38* -.13 -.36* .16 .17 -.36*.61*
Note: Age-corrected z scores used for EF and standard scores used for Vineland and SSIS 1Auditory attention; 2Visual Attention;
3 Working memory;
4 Auditory inhibition time;
5Auditory inhibition
errors; 6Visual inhibition;
7Affective decision making
*p< .05
74
Table 5
Correlations between Age, IQ, measures of EF and social competence for the
ASD group
1 2 3 4 5 6 7 8 9
10
11
1. Age .
2. IQ .05
3.aud
attn1
.24 .18
4.vis
attn2
.40* .30* .61*
5.WM3
.36* .32* .53* .70*
6.plan .17 -.39* -.21 -.44* -.40*
7. inhib
time4
-.34* -.41* -.24 -.32* -.32* .45*
8. inhib
errors5
-.16 -.39* -.26 -.50* -.35* .58* .38*
9. vis
inhib6
.03 -.02 .33* .51* .47 -.11 .01 -.17
10.dec
making7
.28* .00 .36* .35* .23 -.17 -.31* -.15 .29
11.
Vineland -.53* .05 .49* .31* .24 -.04 -.43* -.13 .36*
.30
12.SSIS .01 .145 .51* .20 .15 -.02 -.32* -.19 .33*
.31*
.62*
13. BRIEF .07 -.04 .37* -.16 -.22 -.16 .15 .08 -.44*
-.14
.67* 59*
Note: Age-corrected z scores used for EF and standard scores used for Vineland and SSIS 1Auditory attention; 2Visual Attention;
3 Working memory;
4 Auditory inhibition time;
5Auditory inhibition
errors; 6Visual inhibition;
7Affective decision making
*p< .05
75
Affect Recognition r(48) = .31, p =.03 and with lower scores on Empathy r(48) = -.30, p
= .04. IQ scores were also negatively correlated with SSIS, r(48) = -.34 , p = .02. As
such, children in the control group with higher intelligence performed better on voice
affect recognition but rated themselves lower on empathy and received lower scores on
the social skills measure.
For the ASD group, IQ was significantly correlated with 5 of 6 SEL tasks and 5 of
8 EF tasks. Higher IQ scores were associated with better performance on the facial affect
recognition, r(49) = .31, p = .03, voice affect recognition, r(49) = .33, p = .02, ToM, r(49)
= .37, p = .01, pragmatic language, r(49) = .36, p = .01, and problem solving, r(49) = .46,
p < 0.01. Performance also improved as IQ increased on the visual attention, r(49) = .30,
p = .04, planning, r(49) = -.39, p =.01, working memory, (49) = .32, p = .02, auditory
inhibition time, r(49) = -.41, p < .01 and accuracy, r(49) = -.391, p = .005. There were no
significant correlations between IQ scores and the Vineland, r(48) = .05, p = .76, the
SSIS, r(48) = .15, p = .33, or the BRIEF, r(48) = -.04, p = .80. As such, children with
ASD and higher intelligence performed better on most SEL and EF tasks but were not
rated higher in social competence or parent-measured EF.
Age of diagnosis. For children with ASD, the age at which they were diagnosed
was significantly correlated with one SEL and two EF tasks. The older the children were
when they received their ASD diagnosis, the better their performance on ToM, r(49) =
.32, p = .037, ,visual attention, r(49) = .390, p< .01, and planning, r(49) = -.34 , p= .02.
Age of diagnosis was not significantly correlated with the Vineland, r(48) = -.03 , p= .83,
the SSIS, r(48) = -.03 , p= .84, or the BRIEF, r(48) = .10, p= .50. As such, although
children who received an ASD diagnosis at a later age performed better on ToM, visual
76
attention and planning, they were not rated as having higher social competence or parent-
measured EF.
5.3 Hypothesis 1: Children with ASD will perform significantly different than
neurotypical children in specific domains of EF, SEL and social
competence.
In order to test the hypothesis that children with ASD would perform more poorly
on measures of SEL, EF and social competence, a series of ANOVAs were performed to
examine group differences between the tasks. The Brown-Forsythe statistic is reported
where scores violated assumptions of normality (Fagerland, 2012; Field, 2005). Since the
correlation analyses demonstrated that the relationship between age and measures of
SEL, EF and social competence differed across groups, using age as a covariate violated
the assumptions of homogeneity of regression slopes and thus the age-corrected scores
were used. See Table 6 for means and standard deviations of the SEL, EF and social
competence measures for the ASD and control groups.
5.3.1 SEL measures. There were significant differences on 4 of 6 measures.
Children with ASD performed significantly lower on voice affect recognition, F(1, 94) =
10.52, p < 0.01, ToM, F(1, 54) = 31.79, p < 0.01, pragmatic language, F(1, 75) = 50.97,
p < 0.01, and social problem solving, F(1, 66) = 41.84, p < 0.01. There were no
significant differences between the groups on facial affect recognition, F(1,94 ).=2.18, p
=.12, and empathy, F(1,94 ).=2.47, p =.20. In terms of clinical impairment, more children
with ASD performed at least one standard deviation below normative mean on all SEL
77
measures that had standardized scores. These included the DANVA facial affect
recognition (14% versus 4%), DANVA voice affect recognition (28% versus 8%), Happe
Strange Stories -ToM (22% versus 0%), TOPL pragmatic language (15% versus 2 %) and
the TOPS problem solving (37% versus 2%). Interestingly, 41% of children with ASD
and 88% of neurotypical children passed all ToM questions.
5.3.2 EF measures. In terms of performance-based EF, there were significant
differences between the groups on 6 out of 8 tasks. Children with ASD performed
significantly lower than controls on auditory attention, F(1, 82) = 4.84, p = 0.03, visual
attention, F(1, 94) = 9.39, p < 0.01, working memory, F(1, 94) =4.23, p = 0.04, auditory
inhibition time, F(1, 85) = 5.63, p = 0.02, auditory inhibition accuracy, F(1,78) = 7.08, p
= 0.01, and planning, F(1, 94) =3.83, p = 0.05. In terms of clinical impairment, more
children with ASD performed at least one standard deviation below normative mean on
all these measures. These included Planning (27% versus 15%), Auditory Attention (47%
versus 23%), Visual Attention (12% versus 4%), Visual Working Memory (25% versus
8%) and Auditory Inhibition (40% versus 20%). Although there were no significant
differences between the groups on Visual Inhibition, more children in the ASD group
scored within the clinical impairment range (20% versus 12%).
5.3.3 Parent rated EF. With respect to parental ratings of EF, scores on the
BRIEF were significantly higher for children with ASD compared to the control group,
indicating greater overall executive dysfunction, F(1, 95), = 137.08, p < .01. When
looking at the two domains of the BRIEF, children with ASD were rated as having more
difficulties with Metacognition, F(1, 95) = 100.43, p < 0.01, and Behavioural Regulation,
F(1,95) = 150.23, p < 0.01. In terms of clinical impairment, 90% of children with ASD
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performed at least one standard deviation below normative mean compared to 8 % of
children from the control group on the General Executive Composite overall score.
5.3.4 Social competence. There were significant differences between groups on
both measures of social competence. As expected, children in the control group were
rated as significantly more socially competent than children in the ASD group on both the
Vineland F(1, 72) = 221.03, p < 0.01, and the SSIS F(1, 81) = 146.94, p < 0.01. In terms
of clinical impairment, 90% of children with ASD performed at least one and a half
standard deviations below normative mean on the Vineland compared to 8 % of children
from the control group. On the SSIS, 65% of children with ASD one and a half standard
deviations below the normative mean compared to no children from the control group.
5.4 Hypothesis 2: Measures of Nonverbal Awareness, Social Meaning and Social
Understanding will represent latent SEL domains for the ASD and
control groups.
In order to test the hypothesis that the SEL measures are associated, Pearson
product-moment correlation coefficients between SEL measures were calculated for the
ASD and control groups separately (see Tables 2 and 3). Exploratory factor analysis was
then performed to examine whether the SEL measures represent three distinct domains:
Nonverbal Awareness (facial and voice affect recognition); Social Meaning (ToM,
empathy and pragmatic language); and Social Reasoning (social problem solving). For
the control group, empathy and ToM were not significantly correlated with any SEL task.
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Table 6. Descriptive Statistics of Measures of SEL, EF and Social Competence
Variable
Control M (SD) N = 48
ASD M (SD) N = 49
F-value
SOCIAL EMOTIONAL LEARNING
Facial Affect Recognition1
Range 20.81(2.22) 15 – 24
20.16(2.13) 15 – 23
2.18
Voice Affect Recognition2
Range 18.53(2.88) 13 – 23
16.33(4.16) 2 – 23
10.52**
ToM3
Range 9.77(0.66) 7 – 10
7.61(2.68) 0 – 10
31.79**
Empathy (child ratings)4
Range Empathy (parent ratings) Range
11.37(3.49) 5 – 20 12. 53(3.54) 3 – 20
10.29(3.84) 4 – 21 9.68(4.00) 2 – 21
2.47
Pragmatic Language5
Range 12.10(2.28) 6 – 16
8.27(3.33) 2 – 14
50.97**
Social Problem Solving6
Range 86.00(7.80) 65 – 100
70.90(15.40) 14 – 92
41.84**
EXECUTIVE FUNCTIONS
Auditory Attention7
Range 28.44(1.89) 22 – 30
27.61(2.76) 21 – 30
4.84*
Visual Attention8
Range 84.88(8.62) 66 – 100
79.84(11.55)
41 – 100
9.39**
Visual Working Memory9
Range 15.50(6.60) 6 – 34
13. 31(7.94)
0 -33
4.23*
Planning10
Range 26.33(13.32) 2 – 53
33.04(21.07) 4 – 115
3.83*
Visual Inhibition11
Range 10.90(9.56) 2 – 20
10.43(4.19) 3 – 20
.800
Auditory Inhibition Speed12
Range 25.23(8.18) 4 – 37
18.98(11.52) 2 – 53
5.63*
Auditory Inhibition Accuracy
13
Range
5.46(3.55) 0 – 15
7.55(5.57) 0 – 22
7.08**
Affective Decision Making14
Range 54.08(14.62) 13 – 84
51.94(14.76) 21 – 100
.821
EF PARENT RATINGS: BRIEF15
Range 48.15(7.78) 34 – 66
70.64(10.08) 40 – 86
137.08**
SOCIAL COMPETENCE
Vineland Socialisation16
Range 103.54(13.44) 78 – 138
70.64(10.88) 47 – 98
221.03**
SSIS
Range 110.81(9.98) 89 – 132
78.56(15.53) 40 – 107
146.94**
Note: Raw scores used for SEL and EF; standard scores used for Vineland, SSIS and BRIEF
80
As expected, there were positive correlations between the facial and linguistic affect
recognition tasks, r(46) =.32, p=.03. Pragmatic language was positively correlated with
facial affect recognition, r(46) =.34, p=.02, voice affect recognition, r(46) =.46, p <.01
and social problem solving, r(46) = 0.55, p < .01. Social problem solving was also
positively correlated with voice affect recognition, r(46) = .29, p = .05. For the ASD
group, empathy was also not significantly correlated with any other SEL measures.
Similar to the control group, there were positive correlations between the facial and
linguistic affect recognition tasks, r(47) = .47, p < .01. In contrast to the control group,
ToM was positively correlated with facial affect recognition, r(47) = .34, p = .02,
pragmatic language, r(47) = .56, p < .01 and social problem solving, r(47) = .50, p < .01.
Pragmatic language was positively correlated with voice affect recognition, r(47) = .36, p
= .01 and social problem solving, r(47) = .61, p < .01. Lastly, social problem solving was
also positively correlated with voice affect recognition, r(47) = .46, p < .01.
5.4.1 Principal components analysis of SEL. A principal components analysis
(PCA) with varimax rotation was conducted to identify SEL domains and to create
composite scores. Creating composite scores allowed for data reduction for subsequent
analyses. All analyses were performed separately for the control and ASD groups.
Initially, data screening was performed on the SEL variables to determine the suitability
of the data for factor detection. Several well-recognized criteria were examined,
including inspecting inter-correlations between variables and ensuring some correlations
above 0.30. Two measures of sample adequacy were consulted – the Kaiser-Meyer Olkin
(KMO) index and the anti-image diagonal correlations of sampling adequacy, which
above 0.50 is considered suitable for factor analysis and are particularly important when
81
the sample sizes are small (Tabachnick & Fidell, 2009). Bartlett’s Test of Sphericity was
consulted to determine whether the data forms an identity matrix and to further determine
suitability for structure detection. The determinant of the correlation matrix detects
multicollinearity, which should be greater than 0.00001, was also checked. Multiple
extraction techniques were then utilised to ensure the best fit of the data, as recommended
in the literature (Field, 2009). These included Kaiser’s criteria of eigenvalues > 1 and
examination of the scree plot, communalities and cumulative percent of variance
extracted. Absolute values below 0.50 were suppressed to account for the small sample
sizes (Field, 2009). Finally, the variables that did not correlate with any other measures
were excluded from further analysis (Field, 2009). As a result of the differing correlations
between variables in each group, which is common in research with clinical groups
(Dowling, Hermann, LaRue & Sager, 2010; Meredith & Teresi, 2006; Raykov,
Marcoulides & Cheng-Hsien, 2012), different variables were entered for the control and
ASD groups, as described below.
Five variables were entered into the PCA analysis for the control group. These
included facial and voice affect recognition, empathy, pragmatic language and social
problem solving. ToM was excluded from analyses because it had very low correlations
with all other variables. Although there were no significant correlations between empathy
and the other SEL variables for the control group, it was retained because it was
approaching significance with pragmatic language, r(46) = 0.23, p= 0.06 and social
problem solving, r(46) = 0.22, p= 0.07. Empathy had a diagonal anti-image correlation of
0.77 and communalities above 0.80 for all analyses, which further supports its retention.
The KMO Index was 0.699 and the Bartlett’s test of Sphericity was significant, x2 (48) =
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36.67, p < 0.01. The diagonals of the anti-image correlations ranged from 0.65 to 0.79.
Finally, the communalities ranged from 0.580 to 0.970. Initial eigenvalues indicated a 2-
factor solution that accounted for 63.57% of the variance, but the communalities were
low, with three of the five being below 0.55. A three-factor solution was preferred based
on previous theoretical support and because the eigenvalues leveled off after three
factors, which accounted for 78.70% of the variance. Extracted components ranged from
0.51 to 0.98. The first factor included pragmatic language and social problem solving and
accounted for 34.31% of the variance. The second factor included voice and facial affect
recognition, accounted for 24.11% of the variance and the third factor of empathy
accounted for 20.28% of the variance. Voice affect recognition loaded equally on the first
and second factors, but was retained for the second factor based on several robust
theoretical grounds, including that these two variables were designed to conceptually
measure different aspects of the same underlying construct and also confirmation that
these same two variables loaded on the same factor in previous research with larger
samples (Lipton & Nowicki, 2009; McKown et al., 2009; McKown et al., 2013). The
factor labels of ‘nonverbal awareness’, ‘social meaning’ and social reasoning’ proposed
by Lipton & Nowicki (2009) and McKown and colleagues (2009; 2013) were modified to
suit the current data. The three distinct underlying SEL factors for the control group used
in subsequent analyses are Social Understanding (pragmatic language and social problem
solving), Nonverbal Awareness (facial affect and voice affect recognition), and Empathy
(empathy) (see Figure 5a).
Five variables were also entered into the PCA analysis for the ASD group. These
included facial and voice affect recognition, ToM, pragmatic language and social Facial & Voice
Affect Recognition
83
problem solving. Empathy was excluded from analyses because it had very low
correlations with all other variables. The KMO Index was 0.72 and the Bartlett’s test of
Sphericity was significant, x2 (49) = 67.28, p < 0.01. The diagonals of the anti-image
correlations ranged from 0.65 to 0.79. Finally, the communalities ranged from 0.64 to
0.78. Initial eigenvalues indicated a 2-factor solution that accounted for 71.90% of the
variance. The first factor included ToM, pragmatic language, and social problem solving,
and accounted for 41.95% of the variance. The second factor included voice and facial
affect recognition and accounted for 29.95% of the variance. There were no cross
loadings and extracted components ranged from 0.78 to 0.87. The factor labels proposed
by Lipton & Nowicki (2009) and McKown and colleagues (2009; 2013) were again
modified to suit the current data. The two distinct underlying SEL factors for the ASD
group used in subsequent analyses are Social Understanding (ToM, pragmatic language
and Social Problem Solving) and Nonverbal Awareness (facial affect and voice affect
recognition) (see Figure 5b).
5.5 Hypothesis 3: The measures of Cognitive, Behavioural and Emotional regulation will
represent three latent EF domains for the ASD and control groups.
In order to test the hypothesis the EF measures are associated, Pearson product-
moment correlation coefficients were first calculated for the ASD and control groups
separately using eight EF measures: auditory attention, visual attention, planning, visual
working memory, visual inhibition, auditory inhibition reaction time, auditory inhibition
84
Figure 5a. The SEL domains for the control group
Figure 5b. The SEL domains for the ASD group
85
accuracy and affective decision making (see Tables 4 and 5). Exploratory factor analysis
was then performed to examine whether the EF measures represented three distinct
domains: Cognitive Regulation (planning, auditory attention, visual attention, visual
working memory); Behavioural Regulation (auditory inhibition accuracy, auditory
inhibition time, and visual inhibition); and Emotional Regulation (affective decision
making).
For the control group, planning and affective decision making were not
significantly correlated with any other EF measures. Although there were no significant
correlations between visual attention and auditory attention, they were both positively
correlated with the same measures. Visual working memory was positively correlated
with auditory attention, r(46) = .36, p = .01 and visual attention, r(46) = .54, p < .01.
Visual inhibition was also positively correlated with auditory attention, r(46) = .39, p =
.01 and visual attention, r(46) = .66, p < .01. Visual working memory was positively
correlated with visual inhibition, r(46) = .61, p < .01. Visual inhibition was negatively
correlated with auditory inhibition accuracy, r(46) = -34, p = .02 indicating better
performance on visual inhibition was associated with fewer errors on auditory inhibition.
Lastly, auditory inhibition errors and reaction time were positively correlated, r(48) = .32,
p = .03. For the ASD group, visual attention was significantly correlated with all EF
tasks and visual working memory was significantly correlated with all EF tasks except
affective decision making. Auditory attention was positively correlated with visual
attention, r(47) = .61, p < .01, working memory, r(47) = .53, p < .01, visual inhibition,
r(47) = .33, p = .02, and affective decision making, r(47) = .36, p = .01. Unlike the
control group, planning and affective decision making were each correlated with four EF
86
tasks. Better performance on planning was correlated with better performance on visual
attention, r(47) = -.44, p < .01, visual working memory, r(47) = -.40, p = .01, auditory
inhibition reaction time, r(47) = .45, p < .01, and auditory inhibition accuracy, r(49) =
.58, p < .01. Better performance on affective decision making was correlated with better
performance on visual attention, r(49) = .35, p = .01, auditory attention, r(47) = .36, p =
.01, visual inhibition, r(47) = .29, p = .04 and auditory inhibition reaction time, r(47) = -
.31, p = .03. Lastly, the two auditory inhibition tasks were positively correlated, r(47) =
.38, p < .01.
There were no significant correlations between the parent ratings and any
performance-based EF tasks for the control group. For the ASD group, parent EF ratings
were negatively correlated with two performance-based EF measures for the ASD group.
Higher overall ratings of executive dysfunction were associated with lower scores on
auditory attention, r(46) = -.37, p = .01 and visual inhibition, r(46) = -.44, p < .01. This
confirms the importance of conducting separate analyses for performance-based and
parent-rated EF.
5.5.1 Principal components analysis of EF. A principal components analysis
(PCA) with varimax rotation was conducted to identify EF domains and to create
composite scores. All analyses were performed separately for the control and ASD
groups. Data screening was initially performed on the EF variables to determine the
suitability of the data for factor detection using the well-recognized criteria previously
described.
For the control group, six variables were used for PCA analysis: auditory
attention, visual attention, visual working memory, visual inhibition, auditory inhibition
87
time and accuracy. Planning and affective decision making were excluded from analyses
because they had very low correlations with all other variables. The KMO Index was 0.68
and the Bartlett’s test of sphericity was significant, x2 (48) = 76.31, p < 0.001. The
diagonals of the anti-image correlations ranged from 0.50 to 0.81. Finally, the
communalities ranged from 0.65 to 0.95. Initial eigenvalues indicated a 3-factor solution
that accounted for 77.55% of the variance, Extracted components ranged from 0.76 to
0.96. The first factor included visual attention, visual inhibition and visual working
memory and accounted for 35.63% of the variance. The second factor included auditory
inhibition reaction time and accuracy and accounted for 22.65% of the variance. The
third factor included auditory attention and accounted for 19.28% of the variance. The
three EF labels originally proposed - cognitive, behavioral and affective self regulation
were not suitable for naming these factors. The three distinct underlying EF factors for
the control group included Visual Executive Control (visual working memory, attention
and inhibition), Inhibition (auditory inhibition reaction time and auditory inhibition
accuracy) and Auditory Attention (auditory attention) (see Figure 6a).
For the ASD group, the eight measures of EF were also entered into a PCA
analysis. The visual inhibition variable had a low diagonal correlation on the anti-image
matrix and thus was not retained for the analyses. With the remaining seven variables, the
KMO Index was 0.78 and the Bartlett’s test of sphericity was significant, x2 (49) =
110.68, p < 0.01. The diagonals of the anti-image correlations ranged from 0.75 to 0.82.
Finally, the communalities ranged from 0.70 to 0.86. Initial eigenvalues indicated a 3-
factor solution that accounted for 76.03% of the variance. Extracted components ranged
from 0.64 to 0.89. The first factor included visual attention, auditory attention and visual
88
working memory and accounted for 31.03% of the variance. The second factor included
planning, auditory inhibition reaction time and accuracy and accounted for 28.03% of the
variance. The third factor included affective decision making and accounted for 16.98%
of the variance. There was some cross loading with auditory inhibition reaction time as it
loaded on the second (0.64) and third factors (-0.54). It was retained for the second factor
based on higher loading score and the theoretical basis that is conceptually related to
auditory inhibition accuracy.
The three EF labels originally proposed of cognitive, behavioral and emotional
regulation were suitable for these factors. The three distinct underlying EF factors for the
ASD group included Cognitive Regulation (visual working memory, visual attention and
auditory attention), Behavioural Regulation (planning, auditory inhibition reaction time
and auditory inhibition accuracy) and Emotion Regulation (affective decision making)
(see Figure 6b).
5.5.2 Principal components analysis of parent-rated EF. Exploratory factor
analysis for both groups was performed using all 9 subscales of the BRIEF to determine
whether the three domains recently identified in the literature (Egeland & Fallmyr, 2010;
Gioia, Isquith, Retzlaff, & Espy, 2002) were also appropriate in this sample.
For the control group, the KMO Index was 0.76 and the Bartlett’s test of sphericity was
significant, x2 (48) = 199.80, p < 0.01. The diagonals of the anti-image correlations
ranged from 0.69 to 0.83. Finally, the communalities ranged from 0.45 to 0.83. Initial
eigenvalues indicated a 2-factor solution that accounted for 63.85% of the variance,
Extracted components ranged from 0.66 to 0.91. The first factor included plan/organize,
task monitor, initiate, working memory and organisation of material and accounted for
89
34.83% of the variance. The second factor included self monitor, shift, inhibit and
emotional control and accounted for 28.02% of the variance.
For the ASD group, the KMO Index was 0.81 and the Bartlett’s test of sphericity was
significant, x2 (49) = 181.54, p < 0.01. The diagonals of the anti-image correlations
ranged from 0.63 to 0.89. Finally, the communalities ranged from 0.46 to 0.78. Initial
eigenvalues indicated a 2-factor solution that accounted for 62.05% of the variance,
Extracted components ranged from 0.57 to 0.88. The first factor included self monitor,
shift, inhibit and emotional control and accounted for 34.61% of the variance. The second
factor included plan/organize, task monitor, initiate, working memory and organisation of
material and accounted for 27.44% of the variance. The three labels originally proposed
for the parent EF ratings, which included cognitive, behavioral and emotional self
regulation, were not suitable for these factors for either the control or ASD group. Rather,
the two domains of Metacognition and Behavioural Regulation provided in the BRIEF
manual were maintained for both groups. As such, the two distinct underlying parent-
rated EF factors for both groups included Metacognition (plan/organize, task monitor,
initiate, working memory and organisation of material), and Behavioural Regulation (self
monitor, shift, inhibit and emotional control) (see figure 6b). p = .01) whereas the BRIEF
Metacognition index did not (b = -.26, β =-.12 p = .46). For the ASD group, parent EF
ratings predicted 54.2% of the variance in social competence (r2 = .54). Coefficients
showed age (b = -1.52, β =-.22, p = .05) accounted for significance variance in Social
Competence but not IQ (b = .07, β =.08, p = .44). Behavioural Regulation (b = -1.6, β =-
.58, p < .01) predicted significant variance in social competence but Metacognition (b = -
.49, β =-.17, p = .21) did not for the ASD group.
90
Figure 6a. The EF domains for the control group
Figure 6b. The EF domains for the ASD group
Auditory Attention
Affective Decision
Making
91
5.5.3 Creation of composite scores. To reduce the number of variables and limit
the number of interactions tested, the SEL and EF factors identified in the previous
sections for the control and ASD groups were used to create composite scores. Multiple
indicators of a latent factor remove biasing effects of measurement error (Kenny, 2003)
and reduce probability of a Type I error (Field, 2009). The PCA of the SEL measures had
indicated that five measures were indicators of three latent SEL domains for the control
group: Social Understanding (pragmatic language and social problem solving);
Nonverbal Awareness (facial affect recognition and voice affect recognition); and
Empathy (empathy). For the ASD group, five SEL measures reflected two latent SEL
domains: Social Understanding (pragmatic language, social problem solving and ToM);
and Nonverbal Awareness (facial affect recognition and voice affect recognition). The
PCA of the EF measures had indicated that five measures were indicators of three latent
EF domains for the control group: Visual Executive Control (visual attention, visual
working memory and visual inhibition): Inhibition (auditory inhibition time and auditory
inhibition errors); and Auditory Attention (auditory attention). For the ASD group, seven
measures were indicators of three EF domains: Cognitive Regulation (visual attention,
visual working memory and auditory attention); Behavioural Regulation (auditory
inhibition time , auditory inhibition errors and planning); and Emotion Regulation
(affective decision making). The PCA analysis for the parent ratings of EF had indicated
that the 9 scales were indicators of two latent domains: Metacognition (plan/organize,
task monitor, initiate, working memory and organisation of material) and Behavioural
Regulation (self monitor, shift, inhibit and emotional control). Z-scores for SEL and EF
variables were summed according to each latent factor identified for the control and ASD
92
groups separately. The z-scores of the SSIS and Vineland were combined to provide one
social competence score.
5.6 Hypothesis 4: The proposed model of SEL and EF will account for significant
variance in social competence among neurotypical children and
children with ASD.
To test the hypothesis that better scores on the SEL and EF domains would be
related to higher ratings of social competence, Pearson product-moment correlation
coefficients between the SEL and EF domains, and Social Competence were calculated
for each group separately (see Tables 7 and 8). Preliminary analyses were conducted by
examining histograms, Q-Q plots and Boxplots to ensure that composite score data met
assumption criteria for use in correlation analyses. As mentioned earlier, one case in the
ASD group was identified as an outlier because of very high social competence ratings,
and it was removed from the ASD group.
For the control group, Social Competence was positively correlated with two SEL
domains - Social Understanding, r(46) = .44, p < .01, and Empathy, r(46) = .29, p = .05,
but not Nonverbal Awareness, r(46) = .21, p = .16. Thus, neurotypical children who
performed better on two SEL domains - Social Understanding and Empathy - were also
rated higher in Social Competence. There were no significant correlations between Social
Competence and the three EF domains - Visual Executive Control r(46) = .14, p = .36,
Inhibition, r(46) = -.16, p = .27 or Auditory Attention, r(46) = .10, p = .51. Therefore,
93
neurotypical children with better performance on any EF domains were not rated higher
in Social Competence.
For the ASD group, there were no significant correlations between Social
Competence and the two SEL domains - Nonverbal Awareness, r(46) = .20, p = .17 or
Social Understanding, r(46) = .17, p = .24. Thus, children with ASD who performed
better on either SEL domain were not rated higher in Social Competence. Social
Competence was positively correlated with two EF domains - Cognitive Regulation,
r(46) = .41, p < .01, and Emotional Regulation, r(46) = .32, p = .02, but not with
Behavioural Regulation, r(46) = -.23, p = .12. Thus, children with ASD who performed
better on Cognitive Regulation and Emotion Regulation were rated higher in Social
Competence. In terms of parent ratings, Social Competence was negatively correlated
with the BRIEF Behavioural Regulation Index, r(46) = -.50, p < .01 but not with the
BRIEF Metacognition Index, r(46) = -.18, p = .21 for the control group. Thus,
neurotypical children who were rated as having better Behavioural Regulation were also
rated as having higher Social Competence. For the ASD group, Social Competence was
negatively correlated with the BRIEF Behavioural Regulation, r(46) = -.73, p < .01 and
BRIEF Metacognition, r(46) = -.57 p < .01. Thus, children with ASD who were rated as
having better Behavioural Regulation and Metacognition were also rated as having higher
Social Competence.
5.6.1. Regression analyses. To test the hypothesis that SEL and EF
independently predict significant variance in social competence over and above the
influence of age and IQ, a series of multiple hierarchical regressions were performed for
each group separately. Parent-rated EF ratings were analysed separately based on the
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Table 7 Correlations between SEL and EF domains for the CONTROL group
1 2 3 4 5 6 7 8 9 10
11
1 SEL1
2. SEL2 .46*
3. SEL3 .25 .09
4. EF1 .31* .31* .01
5. EF2
-.23 .06 .15 -.37*
6. EF3 -.10 .24 -.06 .32* .17
7. BRIEF
Meta1 -.04 -.18 .03 .11 .01 .08
8. BRIEF Beh2
-.49* -.34* -.25 -.08 .12 .09 .50*
9.BRIEF
total -.17 -.20 -.10 .13 .-02 .16 .91* .77*
10Vineland .46* .11 .36* -.17 .07 -.06 .29* -.39* -.39*
11.SSIS .34* .09 .30* .10 -.12 .09 -.21 -.46* -.36* 61*
12. SC3 .44* -.21 .29* .14 -.16 .10 -.30* -.49* -.41* .90*
.81*
Note: SEL1= Social Understanding; SEL 2= Nonverbal Awareness; SEL 3 = Empathy EF 1 = Visual Executive Control. EF 2 = Inhibition EF 3 = Auditory Attention 1BRIEF Metacognition Index; 2BRIEF Behavioural Regulation Index; 3SC=sum of Vineland and SSIS *p< .05
95
Table 8 Correlations between SEL and EF domains and social competence for the ASD group
1 2 3 4 5 6 7 8 9 10
1SEL1
2. SEL2 .44*
3. EF 1 .49* .41
4. EF2 -.56* .40 -.49*
5. EF3
.16 .25 .37* -.26
6. BRIEF
Meta1
-.04 .15 -.11 -.07 -.14
7. BRIEF
BehInh2
-.08 .15 -.35* -.08 -.11 .61*
8. BRIEF total
-.08 -.03 -.26 .01 -.11 .87* .91*
9.Vinela
nd .11 .13 .10 -.06 -.09 -.47 -.64* -.67*
10.SSIS .14 .14 .33* -.26 .31* -.45* -.64* -.59* .62*
11. SC3
.20 .17 .41* -.23 .32* -.57* -.73* -.57* .82* .84*
SEL= Social Understanding; SEL 2= Nonverbal Awareness. EF 1 = Cognitive Regulation. EF 2 = Behavioural Regulation. EF 3 = Emotional Regulation 1BRIEF Metacognition Index; 2BRIEF Behavioural Regulation Index; 3SC=sum of Vineland and SSIS *p< .05
96
results of the correlation analyses. Age and IQ were entered in as predictors in the first
block of each regression and social competence was entered as the criterion variable for
all analyses. Data screening was performed according to Field (2005).
For the control group, Social Understanding, Nonverbal Awareness and Empathy
were entered as predictor variables in the SEL analysis. Results indicated that the overall
SEL model was significant, F(5, 47) = 3.30, p = .01 and that two predictors accounted for
28.2% of the variance in Social Competence ratings (r2 = .28). Coefficients showed that
IQ (b = -.34 β = -.34, p = .03) significantly predicted Social Competence whereas age (b
= -.95, β = -.15, p = .41) was not significant in predicting Social Competence. Social
Understanding significantly predicted Social Competence beyond the effect of age and
IQ (b = .23, β = .40, p = .04) for neurotypical children whereas Nonverbal Awareness (b
= .37, β = .06, p = .70) and Empathy (b = 1.72, β = .17, p = .28) did not predict
significant variance in Social Competence for this control group. For the EF analysis,
Visual Executive Control, Inhibition and Auditory Attention were entered as predictor
variables. Results indicate that the overall EF model was not significant, F(5, 47) = .96, p
= .46, and explained 10.2% of the variance in Social Competence ratings (r2 = .10). In
terms of the control variables, coefficients show that age (b = .68, β = .11, p = .54) and IQ
(b = -.29, β = -.29, p = .06) were not significant in predicting Social Competence. Visual
Executive Control (b = -.32, β = -.08, p = .64), Inhibition (b = -.48, β = -.08, p = .65) and
Auditory Attention (b = -.29, β = -.03, p = 86) did not predict significant variance in
Social Competence for neurotypical children.
For the ASD group, Social Understanding and Nonverbal Awareness were entered
as predictor variables in the SEL analysis. The results indicated that the overall SEL
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model was not significant, F(4, 47) = 1.5, p = .23, and accounted for 12.2% of variance in
Social Competence (r2 = .12). Coefficients show that age (b = -2.21, β = -.31, p = .05)
significantly accounted for variance, but IQ (b = -.09, β = -.11, p = .56), Social
Understanding (b = .38, β = .21, p = .26) and Nonverbal Awareness (b = .1.02, β =-.18, p
= .30) were not significant in predicting Social Competence for children with ASD.
Cognitive Regulation, Behaviour Regulation and Emotion Regulation were entered as
predictor variables in the EF analysis. The results indicated that the overall EF model was
significant, F(5, 47) =3.19, p = .02 and accounted for 28% of the variance in Social
Competence (r2 = .28). In terms of the control variables, the coefficients showed that age
(b = -.35, β = -.50, p < .01) significantly predicted Social Competence whereas IQ (b = -
.14, β =-.16, p = .33) did not. Cognitive Regulation (b = 1.2, β =.44, p = .01) predicted
significant variance in Social Competence whereas Behavioural Regulation (b = -.56, β
=-.18, p = .31) and Emotional Regulation (b = 2.76, β =.24, p = .11) did not account for
significant variance.
In terms of parent ratings, the BRIEF Behavioural Regulation and Metacognition
indices were entered into separate regression analyses for each group. Overall, parent
ratings of EF significantly predicted Social Competence for both the control group, F(4,
48) = 4.82, p < .01 and the ASD group, F(4, 48) =13.34, p < .01. For the control group,
parent EF ratings predicted 31% of the variance in Social Competence (r2 = .31).
Coefficients showed that IQ (b = -.27, β =-.27, p = .04) accounted for significant variance
in Social Competence but age (b = .02, β =<.01, p = .98) did not; the Behavioural
Regulation index predicted significant variance in Social Competence (b = -1.31, β =-.41,
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Table 9 Summary of Regression Analyses Predicting Social Competence for the CONTROL Group
Domain
R
R2
R2Change
F
F change
P
SEL
Age & IQ .30 .09 .09 2.29 2.30 .11
Model 1 .53 .28 .19 3.30 3.69 .013*
EF
Age & IQ .30 .09 .09 2.30 2.30 .11
Model 2 .32 .10 .01 .96 .147 .46
Parent Rated EF
Age & IQ
Model 3
.30
.56
.09
.31
.09
.21
2.30
4.82
2.30
6.77
.11
.00*
Note. SEL Model 1 age, IQ, Social Understanding, Nonverbal Awareness, Empathy EF Model 2 = age, IQ, Visual Executive Control, Inhibition, Auditory Attention. Parent-rated EF Model 3 = age, IQ, Metacognition, Behavioural Regulation. * p<.05
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Table 10 Regression Coefficients of the SEL and EF Composite Scores for the CONTROL Group
Domain
Beta
B
T
P
SEL
AGE
IQ
-.95
-.34
-.15
-.34
-.84
-2.2*
.41
.03
Social Understanding
Nonverbal Awareness
Empathy
2.23
.37
1.72
.40
.06
.17
2.12*
.38
1.1
.04
.70
.28
EF
Age
IQ
.68
-.29
.11
-.29
.61
-1.91
.54
.06
Visual Executive Control
Auditory Inhibition
Auditory Attention
-.32
-.48
-.29
-.08
-.08
-.03
-.48
-.46
-.18
.64
.65
.86
Parent Rated EF
Age
IQ
.02
-.27
.00
-.27
.02
-2.07
.98
.04
Metacognition -.30 -.12 -.74 .46
Behavioural Regulation -1.31 -.41 -2.60 .01
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Table 11 Summary of Regression Analyses Predicting Social Competence for the ASD Group
Domain
R
R2
R2Change
F
F change
P
SEL
Age & IQ .24 .06 .06 2.80 1.31 .28
Model 1 .35 .12 .07 1.46 1.6 .23
EF
Age & IQ .23 .06 .06 2.80 1.31 .28
Model 2 .53 .28 .22 3.19 4.25 .02
Parent Rated EF
Age & IQ
Model 3
.24
.74
.06
.54
.06
.49
1.31
12.44
1.31
22.30
.28
.00
Note. SEL Model 1 = age, IQ, Social Understanding, Nonverbal Awareness. EF Model 2 = age, IQ, Cognitive Regulation, Behavioural Regulation, Emotional Regulation Parent-rated EF Model 3 = age, IQ, Metacognition, Behavioural Regulation. * p<.05
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Table 12 Regression Coefficients of the SEL and EF Composite Scores for the ASD Group
Domain
Beta
B
t
P
SEL
AGE
IQ
-2.21
-.09
-.31
-.11
-2.01
-.59
.05
.56
Social Understanding
Nonverbal Awareness
.3
1.02
.21
.18
1.15
1.06
.26
.30
EF
Age
IQ
-3.51
-.14
-.50
-.16
-2.01*
-.72
.00
.33
Cognitive Regulation
Behaviour Regulation
Emotional Regulation
1.21
-.56
2.76
.44
-.18
.24
2.62*
-1.04
1.62
.01
.31
.11
Parent Rated EF
Age
IQ
-1.52
.07
-.22
.08
-2.03*
.78
.05
.44
Metacognition -.49 -.17 -1.28 .21
Behavioural Inhibition -1.62 -.58 -4.34* .00
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1.31, β =-.41, p = .01) whereas the BRIEF Metacognition index did not (b = -.30, β =-.12,
p = .46). For the ASD group, parent EF ratings predicted 54.2% of the variance in social
competence (r2 = .54). Coefficients showed age (b = -1.52, β =-.22, p = .05) accounted
for significance variance in Social Competence but not IQ (b = .07, β =.08, p = .44).
Behavioural Regulation (b = -1.62, β =-.58, p < .01) predicted significant variance in
social competence but Metacognition (b = -.49, β =-.17, p = .21) did not for the ASD
group.
5.7 Hypothesis 5: SEL will partially mediate the effect of EF on social competence.
Pearson product-moment correlation coefficients were calculated to test for
relationships among SEL and EF domains (see Table 7 and 8).
For the control group, two SEL domains were significantly correlated with one EF
domain. The EF domain of Visual Executive Control was significantly correlated with
SEL domains Social Understanding, r(48) = .31, p = .03 and Nonverbal Awareness r(48)
= .31, p = .03. In terms of parent ratings, Behavioural Regulation was negatively
correlated with both Social Understanding r(48) = -.49, p < .01 and Nonverbal
Awareness, r(48) = -.34, p = .02. As such, children in the control group who performed
well on Visual Executive Control also performed well on Social Understanding and
Nonverbal Awareness. Similarly, children in the control group who were rated as having
less executive dysfunction on the BRIEF Behavioural Regulation domain performed
better on both SEL domains of Social Understanding and Nonverbal Awareness.
Affective decision making was significantly correlated with one SEL domain - Empathy
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(r(48) = .31, p = .04) and affective decision making had even higher correlation with
parent ratings of empathy (r(48) = .61, p < .01).
For children with ASD, both SEL domains were significantly correlated with two
EF domains. Social Understanding was significantly correlated with Cognitive
Regulation, r(48) = .49, p < .01 and Behavioural Regulation, r(48) = -.56, p < .01.
Nonverbal Awareness was also significantly correlated with Cognitive Regulation, r(48)
= .39, p < .01 and Behavioural Regulation, r(48) = -.41, p < .01. As such, children with
ASD who performed well on Cognitive Regulation and Behavioural Regulation also
performed well on Social Understanding and Nonverbal Awareness. However, children's
performance on either SEL domain was not associated with parent ratings of EF.
The partial mediation hypothesis was not tested because the basic conditions
required to determine the presence of a partial mediation relationship were not met for
either group. For a partial mediation relationship to exist, EF and SEL must be
significantly correlated with each other and with Social Competence (Baron & Kenny,
1986; Holmbeck, 1997). Although there are significant correlations between SEL and EF
domains in both groups, SEL and EF were not significantly correlated with Social
Competence. In the control group, SEL was significantly correlated with Social
Competence but not EF. In the ASD group, EF was significantly correlated with Social
Competence whereas SEL was not.
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6.0 Discussion
The development of social competence is critical to overall mental health and is
associated with a range of positive outcomes throughout the lifespan. The results of this
study add significantly to our understanding of social competence in children with high
functioning ASD in a number of important ways. First, the results highlight the
variability in social competence, SEL, and EF in children with ASD. Second, the results
emphasize the relationships between multiple antecedents of social competence in terms
of SEL and EF domains in both children with ASD and neurotypical children. Third,
the results of this study demonstrate the unique contributions of performance-based and
parent-rated measures of EF in understanding social competence. Finally, this study
identifies EF as an important predictor of social competence for children with ASD.
6.1 Social Competence
Based on the previous literature, it was hypothesized that children with ASD
would score significantly lower than neurotypical controls on measures of social
competence. The results indicated that children with ASD were rated significantly lower
on both the Vineland-II Socialization domain and the SSIS, with their mean scores on
these measures consistent with other studies (Hus et al., 2013; Klin et al.2007; Lee &
Park, 2007; Lerner et al., 2011; Liss et al., 2001; Lopata et al., 2013; Matchullis, 2012;
Schohl et al., 2013; Weiss et al., 2013). The ASD group, on average, were rated within
the Low to Moderately Low range on the Vineland and within the Below Average range
on the SSIS. Moreover, one-half of the children with ASD scored at least two standard
deviations below the normative mean on the Vineland Socialisation domain and one third
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scored at least two standard deviations below the normative mean on SSIS. These
findings are consistent with significant clinical impairment in these social skills areas in
the ASD group. It also highlights the magnitude of the deficits in daily adaptive
socialisation and social skills despite intact IQ for many children with ASD. In contrast,
the control group, on average, scored in the Adequate range on the Vineland and in the
Average range on the SSIS; no children in the control group scored two standard
deviations below the normative mean on either measure. These findings are consistent
with no significant clinical impairment being identified in the control group.
The children with ASD also had a wide range of scores on the social competence
measures. Individual scores on the Vineland ranged from the Low to the Adequate range
on the Vineland and from the Well Below Average to the Average range on the SSIS.
While this range of social competence in children with ASD has not received much
attention in the literature, it is noteworthy. This finding suggests that although a diagnosis
of ASD entails shared core diagnostic features of social dysfunction, it merely
characterizes the diagnostic parameters of the disorder, and does not convey an individual
child's range of social functioning. This is important clinically, because many
intervention studies target aspects of social deficits or problem behaviours within this
population, without first assessing children's’ social functioning ability. It may thus be
more helpful to assess children's’ specific social functioning as part of intervention
planning, as opposed to targeting interventions generally based on the diagnosis of ASD
alone. Moreover, as demonstrated in this study, assessing the child’s social adaptation
(e.g., using the Vineland) and social skills (e.g., using the SSIS) allows for a more
comprehensive examination of social competence in children with ASD. This
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information can then be used to inform individual interventions or measure changes in
social functioning throughout childhood. This is particularly important given the
evidence that the gap in social functioning between children with ASD and their peers
typically widens with age.
6.2 Demographic factors and social competence
This study explored whether other child-demographic factors - age, IQ,
medication use, and age at diagnosis - were associated with social competence. Standard
scores and raw scores were used to explore the relationship between age and social
competence. Raw scores tell whether children perform more skills with age whereas
standard scores provide scores in relation to other same-aged children from the
standardization sample, thus accounting for typical increases in skills. Consistent with the
literature, the results of this study demonstrated that social competence decreased with
age for children with ASD, (Klin et al., 2007; Lee & Park, 2007; Perry et al., 2009;
Szatmari et al., 2003). For this group, decreasing standard scores on the Vineland
Socialisation Domain with age likely reflected the widening gap throughout elementary
years between children with ASD and neurotypical peers (Klin et al., 2007), rather than a
loss of skill with age, as indicated by the positive, albeit non-statistically significant,
correlation between age and raw scores. For the control group, the significant positive
correlation between age and raw scores on the Vineland suggests that neurotypical
children gain considerable social abilities as they age. However, there was no significant
relationship between age and standard scores on the Vineland, indicating that age did not
account for significant variance in social adaptive functioning for the control group and
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that increasing raw scores likely reflected typical developmental gains in social
behaviours.
In terms of the SSIS, there was no association between age and social skills using
either raw or standard scores for children with ASD. For the control group, both raw and
standard scores on the SSIS demonstrated that social skills increased significantly with
age. The SSIS does not provides standard scores for each year of age, as does the
Vineland, and the same standard scores are provided for all children across the age range
in this study. Given this, the Vineland may be a better measure of social competence than
the SSIS for researchers and clinicians specifically interested in developmental changes
in social skills across this age group.
Despite a wide range of IQ scores from Low Average to Superior, there was no
significant correlation found between IQ and social competence for children with ASD in
this study. At first glance these results may seem contradictory to the previous robust
finding that intelligence is associated with social outcomes in this population (Baird,
2014; Howlin, Goode, Hutton & Rutter, 2004; Howlin & Moss, 2012; Volkmar et al.,
2005). However, this association may only hold true when researchers include children
with a range of intellectual ability. Studies that restricted their participants to children
with IQs over 85 have not found intelligence to be associated with social functioning on
the Vineland Socialisation domain (Anderson et al., 2009; Howlin et al., 2004;
Kenworthy et al., 2010; Klin et al., 2007; Liss et al., 2001; Lopata et al., 2013; Szatmari
et al., 2000) or parent rated social skills (Vickerstaff et al., 2007). Furthermore,
longitudinal studies have also failed to find a relationship between intelligence in
childhood and social functioning in adulthood for those ASD and average or above
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average IQ (Gillepsie-Lynch, 2012; Howlin et al., 2004; 2014). The results of this study
thus support this notion that social competence is independent of intelligence for children
with ASD and intact IQ. This suggests that IQ may not have any added benefit to social
competence once a certain level is reached and that the gap in social competence in
children with ASD relative to their peers is not impacted by intelligence.
For the control group, IQ was significantly correlated with social skills as
measured by the SSIS but not the Vineland, with SSIS scores decreasing with higher IQ.
These results are consistent with other studies using the SSIS or its predecessor - the SRS
(McKown et al., 2007) and are also found in the standardization sample of the SSIS. The
reason for this apparent trend is not well-established. It may be that parents of more
intelligent children have higher social expectations or underestimate positive social
behaviours (Lupowski, 1989). Nevertheless, as mentioned previously, researchers may
want to consider using the Vineland instead of the SSIS as a measure of social
competence as the ratings on the Vineland Socialisation domain do not appear to be
associated with IQ in neurotypical children.
IQ was not used as a covariate in this study for several reasons. First, it had
different associations across groups, and thus does not fulfill methodological requirement
as a covariate. Second, IQ was not associated with either measures of social competence
for the ASD group and was not a significant contributor to social competence in the
regression analyses. Furthermore, researchers advise against using IQ as covariate in
studies involving neurodevelopmental populations, and particularly when other
neurocognitive factors, such as EF, are being examined (Dennis et al., 2009; Miller &
Chapman, 2001). Dennis et al. (2009) argue that IQ is always confounded by and
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inseparable from the ASD condition, and that controlling for IQ removes variability in
the outcome measure that is directly related to the underlying skill or component being
measured.
Consistent with prior studies, 37% of children in the ASD group in this study
were taking psychostimulant medications. Such medications are typically prescribed to
reduce symptoms of hyperactivity, impulsivity and attention (Nickels et al., 2008). The
percentage of children with ASD taking psychostimulant medication in this study is
similar to other studies, which have reported rates between 31% and 52% (Nickels et al.,
2008; Pearson et al., 2012; Tureck et al., 2013). The high prevalence of medication use in
this group may reflect the notion that inattention and impulsivity are a common feature of
the disorder (Frazier et al., 2001; Sinzig et al., 2009; van der Meer et al., 2012). In
general, medication use was not significantly correlated with performance on any SEL or
EF task, or on any parent ratings of EF and social competence on the Vineland. However,
children with ASD on medication were rated lower on the SSIS social skills by their
parents when compared to children with ASD not taking medication. Prior research
looking at the effects of medication on social skills for children with ASD has been
inconsistent. Some studies demonstrated that children with ASD on psychostimulant
medications had poorer social skills (Turygin, Matson & Tureck, 2013; Yerys et al.,
2009) whereas others found increased social skills (Handen et al., 2010; Jahromi et al.,
2009; Pearson et al., 2013; RUPP, 2005). One explanation for these discrepancies is that
measures of social functioning vary across the studies, and that different measures are
likely tapping different aspects of social functioning. For example, the SSIS focuses more
on aspects of compliance and cooperation than the Vineland, and these abilities are often
110
decreased in children who have difficulties with attention and impulsivity (Barkley &
Benton, 2013). This is consistent with recent findings that children with ASD are often
prescribed stimulant medications to manage externalising behaviours (Tureck, Matson,
Turygin & Macmillan, 2013).
Consistent with previous studies, children with ASD in this study were diagnosed,
on average, at age 6 (range: 3 to 10 years) (Daniels & Mandell, 2012; Maenner et al.,
2013; Mandell, Novak & Zubritsky, 2005). As some researchers suggest that children
who receive ASD diagnoses at earlier ages suffer from more impairment than children
diagnosed later, (Daniels & Mandell, 2012; Maenner, 2013), age of diagnosis was
included in this study as a child-based demographic factor. To the researcher’s
knowledge, this is the first study to explore the relationship between age at diagnosis and
multiple measures of social abilities in high functioning children with ASD. Age at
diagnosis was not significantly correlated with social competence for children with ASD
in this study, a finding inconsistent with previous studies (e.g., Daniels & Mandell, 2012;
Maenner, 2013). While the reasons for this are unclear, it may be that age of diagnosis
matters more for children with ASD with lower IQ and less for children with ASD with
average or above average IQ. To further assess this, researchers could study the
relationship between age at diagnosis and social competence in children with ASD across
a broader range of IQ. Alternatively, this finding may reflect the focus on social ability
rather than deficits in this study. In the previously cited studies, age at diagnosis was
related to severity level, as measured by number of diagnostic criteria met in all three
diagnostic categories (social interactions, communication, and restricted
interests/repetitive behaviours). While it may be that children diagnosed earlier have
111
more clinical symptoms, it may also be that those diagnosed at a later age have specific
difficulties which are equally as disadvantageous to social functioning. For example,
although children who are diagnosed at a later age tend to have fewer symptomatic
deficits, they are more likely to have specific deficits in conversational ability,
idiosyncratic speech, and relating to peers (Daniels & Mandell, 2012; Maenner, 2013),
which may clearly have a significant impact on social competence. The results of this
study thus demonstrate the importance of assessing social competence at the level of the
individual when designing interventions for children with ASD.
In summary, for the ASD group, age at diagnosis and IQ were not significantly
correlated with social competence; medication use correlated significantly with one
measure of social skills. In the control group, the results varied with the measure of social
functioning. The latter results suggest that neurotypical children's’ social adaptive
behaviours, as measured by the Vineland - which includes how well they adapt to and
cope with different demands and situations, how well they relate to others and manage in
play or extracurricular activities – was not associated with age or IQ. Neurotypical
children's social skills as measured by the SSIS – which includes behaviours related to
cooperation, responsibility, communication, engagement with others and self control –
increased with age and decreased with higher IQ.
6.3 Social Emotional Learning
Children with ASD performed significantly below the children in the control
group on most measures of SEL. Children with ASD scored lower on measures assessing
the ability to recognize emotional tone from voice, understand the perspectives of others,
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interpret social cues in communication, and find solutions to common social conflicts and
situations.
The significant differences in performance between the two groups on the four
SEL measures is consistent with the plethora of studies that have demonstrated that
children with ASD, as a group, perform worse than typically developing peers on
measures of ToM (Nilsen & Fecica, 2011; Wellman & Liu 2004) and pragmatic language
(de Villiers, Szatmari & Yang, 2014; Lam, 2014). Although the group differences
observed in this study on the ToM task seem to support the notion of a general deficit in
the ability of children with ASD to take others' perspective, these results should be
interpreted with caution since almost one-half of the children (41%) in the ASD group
successfully answered all ToM questions. While the scores may be inflated by the
relatively low demands of the Strange Stories task used in this study, high success rates
have also been reported in other studies in this population using multiple ToM tasks of
varying complexity (Begeer, Malle, Nieuwland & Keysar, 2010; Peterson et al., 2009).
Thus, the general usefulness of including targeted ToM training in interventions for
children with ASD (e.g., Stichter et al., 2012) might be questioned if a large percentage
of children do not actually demonstrate deficits in this area (Rao et al. 2008; Reichow and
Volkmar 2010). However, given the range of success of children with ASD on the ToM
task in this study, particularly as some children with ASD were not able to answer any
questions correctly, clinicians may want to consider administering measures, such as the
Strange Stories task, prior to planning individualised interventions. This would allow
clinicians to determine which children would be more likely to benefit from targeted
ToM training. In terms of problem solving, the findings are consistent with the literature
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that children with ASD perform more poorly than neurotypical children (Demopoulos et
al., 2013; Meyer et al., 2006; Ziv, Hadad & Khateeb, 2013). These findings were robust
across age, stimuli used, and means of evaluation. For example, children with ASD
performed more poorly than neurotypical peers when responding to questions based on
story vignettes, pictures or videos and regardless of whether number of solutions,
effectiveness of responses or style of response (e.g. passive or assertive) was used as the
target score.
The finding that children in the ASD group in this study were significantly worse
at recognizing affect in voices than the control group is important for two reasons. First,
there is much less research in this area as most studies focus on facial affect recognition
in the ASD population. These results add support to the growing literature that children
with ASD have deficits in voice affect recognition (Lindner &Rosen, 2006; Oerlemans et
al., 2013; Peppe, McCann, Gibbon, O'Hare & Rutherford, 2007). These results are
inconsistent with a few studies that did not find differences between children with ASD
and neurotypical children (Mazefsky & Oswald, 2007; Paul et al., 2005). However, in
one of these studies (Paul et al., 2005) both groups obtained near-ceiling scores,
suggesting that the task used of matching voices to only two emotions might have been
too simple to differentiate between the groups. In contrast, using the more complex
DANVA task in this study may have led to better detection of differences between
children with ASD and controls. Second, by using parallel tasks of both facial and voice
affect recognition, this study showed that children with ASD were able to adequately
identify varying levels of the four basic emotions (happiness, sadness, anger and fear) in
faces but not in voices. This highlights the importance of examining various modalities
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when assessing emotion recognition in children with ASD. Future research should
consider examining voice affect recognition in addition to other less explored areas, such
as recognizing affect in gait and posture (Doody & Bull, 2013). It may be that there are
specific rather than global deficits in affect recognition in ASD which are worthy of
further study.
Children with ASD in this study performed similarly to the control group on
measures of facial affect recognition and empathy. These findings support the notion that
children with ASD can accurately identify emotions through facial expressions. Despite
ample research dedicated to this topic, there are inconsistencies throughout the literature.
Some studies found significant deficits in facial affect recognition (Lindner & Rosen,
2006) whereas others have not (Robel et al. 2004). Several recent reviews have suggested
that the group differences found in the literature may be related to the particular emotions
or modalities used in the studies rather than a global deficit in affect recognition for
children with ASD (Harms et al., 2013; Uljarevic & Hamilton, 2012). Findings from this
study support this idea, in that children in the ASD group were able to identify the four
basic emotions as well as the control group. As such, children with ASD do not appear to
have a global deficit in facial affect recognition. Future studies may want to include more
facial expressions, such as jealousy and pride, to explore whether facial affect recognition
is still intact with more complex emotions. In this study, the DANVA was chosen as the
measure of facial affect because it allows for of the identification of varying levels of
emotional intensity. However, the DANVA also provides children with four choices of
possible emotions from which to identify the facial affect. It may be that this structure
helped children be more successful on this task. As such, this ability to identify emotions
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may not be able to translate to more natural situations, where this choice-component is
not typically provided. From a clinical perspective, since children with ASD are able to
recognize facial emotions when provided the vocabulary, it may be important to offer this
same support in other settings such as school. For example, when assisting with a social
situation or teaching children with ASD, it may be more effective to ask 'Is Jimmy
feeling sad or angry' rather than 'how is Jimmy feeling'. The use of static pictures,
however, has been criticized for a lack of ecological validity in studies of facial affect
recognition, but this criticism may not apply to the ASD population. For example, while
recent studies demonstrated that neurotypical children change their visual scanning
patterns based on the nature of the stimuli, children with ASD maintained the same
viewing pattern regardless of whether looking at static pictures or being involved in face
to face interactions (Horlin et al., 2013). In addition, children with ASD rely on specific
facial features to identify emotions whereas neurotypical children rely on more global,
configural-based strategies (e.g., Behrmann et al., 2006). The inclusion of eye tracking
methods in future studies may help to delineate further the factors associated with facial
affect recognition in children with ASD.
Interestingly, the ASD group scored similarly to the control group on the empathy
measure in this study. The general consensus in the literature is that individuals with
ASD lack empathy (Lai, Lombardo, Chakrabarti, Baron-Cohen, 2013; Wing, Gould &
Gillberg, 2011). For example, the DSM-IV specifically includes a lack of awareness or
failure to offer comfort when others are hurt or upset as diagnostic criteria for ASD
(APA, 2004). However, there are few studies dedicated to this topic and of those, the
results are inconsistent (Dziobek et al., 2007; Schulte-Rüther et al., 2013). The mixed
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results appear to be related to two factors. First, they may be due to the inclusion of
children with a range of IQ, since children with lower IQ tend to demonstrate more
impairment in empathy (Bacon, Fein, Morris, Waterhouse & Allen, 1998; Scambler et al.,
2007). Second, it may be related to the idea that different measures are tapping different
aspects of empathy. For example, there is reason to believe that the deficits for children
with ASD may lie in one type of empathy-cognitive empathy but not in another type of
empathy- affective empathy (Dziodek et al., 2008; Jones et al., 2010; Rogers et al., 2007;
Schwenck et al., 2012). Whereas most research with this population focuses on cognitive
empathy – the ability to infer someone else's feelings, the current study specifically
examined affective empathy – the congruent response to someone else's emotional
distress. As ToM tasks are often purported to measure components of cognitive empathy,
an affective empathy measure was chosen for this study to reduce overlap between the
ToM and empathy measure. Results from this study support the recent conceptualisations
that children with ASD have specific rather than global deficits in empathy, where they
are stronger on tasks of affective empathy than cognitive empathy (Scheeren et al., 2013).
In sum, compared to the control group, children with ASD generated less effective
solutions to everyday problems, had more difficulty taking the perspective of others and
interpreting social cues, and were less adept at both recognizing emotions by tone of
voice and appraising the effectiveness of a response based on social context. However,
they were equally skilled at recognizing basic facial emotions and congruently
responding to others' distress or emotions.
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6.4 Executive Functions
This study contributed to the literature by using both performance-based and
parent measures of EF to provide a more comprehensive examination of executive
functioning in children with ASD. The ASD group scored more poorly than the control
group on all but two performance-based measures of EF. More specifically, children with
ASD performed significantly worse on visual attention, auditory attention, visual working
memory, planning, and auditory inhibition. While prior literature has demonstrated mixed
results, comparisons are difficult due the wide range of tasks and stimuli used to tap
similar underlying EF skills (Pellicano, 2012). In addition, most previous studies focus on
isolated EF tasks. Nevertheless, the results of this study are consistent with others which
have simultaneously examined a range of EF in this same age range (Corbett et al., 2009;
Narcizi et al., 2013; Semrud-Clikeman et al., 2010; van Rijn et al., 2013). Moreover, the
finding of significant differences between groups when the direct assessment of EF was
conducted under optimal conditions (i.e. limited distractions; breaks when needed; one-
on-one with an adult) is clearly suggestive of significant deficits in EF in ASD.
The use of both auditory and visual modalities in this study allows for a more
comprehensive look at attention and inhibition in children with ASD. Children with ASD
performed more poorly on both tasks of sustained attention. Although diminished
attention to social stimuli is well-documented for children with ASD (Riby & Hancock,
2008), the present results suggest that difficulties with sustained attention are also
evident with non-social stimuli presented in both visual and oral formats. In terms of
inhibition, the ASD group performed worse than the control group on auditory inhibition
but not visual inhibition, findings consistent with another recent study using the same
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task with boys with ASD (Narzisi et al., 2013). It is possible that the ASD group
performed well on the visual inhibition task because it was computer-administered as
researchers have reported that non-social administration of tasks attenuate deficits found
on person-administered tasks (Ozonoff & Strayer, 2001; Russell, Hala & Hill, 2003).
However, since there were significant group differences on the two other computer-
administered tasks in this study, this factor likely did not account for the findings. The
better performance on visual inhibition is more likely related to the complexity of the
tasks. The visual inhibition task is simpler in that it requires only one response
(withholding a dominant response).whereas the auditory inhibition task is a dual
processing task that requires two responses (withholding a dominant verbal response
while saying the opposite word). These results are consistent with recent studies that have
reported differential performance across task complexity for children with ASD
(Sanderson & Allen, 2013) and provide support for selective inhibitory deficits in
children with ASD.
This study included a measure of affective decision making, an area of
functioning commonly referred to as 'hot' EF. On this task, complexity was decreased by
using two decks instead of four (Crone et al., 2005) to reduce possible effects of random
responding often reported in young typically developing children (Duijvenvoorden,
Jansen, Bredman, &. Huizenga, 2012; Lambek et al., 2010; Skogli et al., 2013). Marbles
were used to provide a visual representation of the wins and losses to reduce demands on
working memory (Crone et al., 2005). Although few studies have examined affective
decision making in children with ASD, the lack of differences found between the ASD
and control group on this task is consistent with the sparse literature with both children
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(Faja, 2013; South et al., 2006, 2011) and adolescents (Johnson et al., 2006; Yechiam et
al., 2010). However, these results should not necessarily be considered evidence of intact
affective decision making in this population for several reasons. The whole premise of
this task is that children who have better affective decision making are able to
strategically adjust their decisions based on previous wins and losses in highly motivating
situation that have an element of risk. However, other studies have demonstrated that
children and teens with ASD are able to obtain similar overall scores as typical peers
despite using a frequent switching strategy not related to outcome or feedback (Johnson
et al., 2006; South et al., 2008; Yechiam et al., 2010) and having stronger autonomic
responses to feedback (Faja et al., 2013). Additionally, the role of motivation is also a
critical aspect of this task as it is measuring the ability to make decisions when there is
some sort of personal gain. It has been postulated that the instructions to make choices for
a donkey rather than for themselves might not be motivating enough (Skogli et al., 2013).
However, to increase motivation in this study, participants were told they would receive a
reward if they were successful at this task (in fact they all received a prize after this task).
Behavioural observations of high frustration and repeated comments made by children
with ASD when losses occurred suggest that this element of deception provided a strong
motivational component. Therefore, given the strong negative reactions the researcher
observed in participants throughout this task and the previous findings that adolescents
with ASD had similar overall scores to their peers despite not adjusting their responses
based on the feedback, it is possible that equal performance between groups in this study
was not actually indicative of their ability to make better decisions in emotionally-
charged situations. Analysing strategy use while measuring physiological reactions
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during an affective decision making task may afford more valuable information regarding
the impact of emotion on decision making and shed some light on the nature of 'hot' EF in
this population.
Parent-ratings of EF can provide useful information regarding the application of
EF under more emotion-laden circumstances and were examined in this study. The
results indicated that parents of children with ASD reported significantly more deficits
compared to the control group on the BRIEF, which is consistent with the literature
(Kalbfleisch &Loughan, 2012; Kenworthy et al., 2008; 2009; 2010; Rosenthal et al.,
2013; Troyb et al., 2013). By virtue of reliance on typical day to day behaviours, the
BRIEF targets multiple abilities and thus has been criticized for its inability to isolate
discrete EF processes while also possibly measuring non-EF behaviours (Isquith et al.,
2013; Kenworthy et al., 2009). Although the results of this study may thus not provide
information about specific EF impairments, it does demonstrate the striking deficits in the
application of EF in daily tasks for children with ASD. Studies of EF in children should
consider using parent ratings of EF along with performance-based measures as they are
likely tapping two different yet important aspects of EF. Performance-based measures of
EF typically capture optimal performance and provide information regarding efficiency
of various self-regulatory processes whereas parent-ratings provide more information
regarding the engagement and application of these processes in day-to-day and less
structured situations (Toplak et al., 2013).
In summary, by using both performance-based and parent ratings of EF, this study
found that children with ASD had deficits in specific aspects of EF as well as significant
difficulties regulating their own behaviours in day to day activities and achieving age-
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expected goals in daily tasks. Specifically, the ASD group was significantly worse than
the control group at sustaining attention to verbal and auditory information, strategic
planning, holding visual information in mind for short periods, and simultaneously
inhibiting and replacing dominant responses. Children with ASD performed well on a
single-level inhibition task and an affective decision-making task, although further study
is required to examine whether these findings reflect intact abilities in these areas. Parent
ratings of EF demonstrated a breadth of deficits across all areas of daily functioning. The
results also indicated considerable variability on both SEL and EF measures in the group
of children with ASD. Although the ASD group performed significantly poorer on most
measures of SEL and EF and had a higher percentage of children within the clinically
impaired range, it is noteworthy that some children with ASD performed as well as the
control group on many SEL and EF measures. For example, the maximum scores were
similar for the ASD and control groups on all SEL measures, except pragmatic language
and social problem solving. In terms of EF, the maximum scores were similar in both
groups on all measures, except planning and auditory inhibition. Since different children
obtained minimum and maximum scores on different tasks, the higher scores in the ASD
group are not due to the same subgroup of children performing better across all tasks.
6.5 Model of Social Competence
It was hypothesized that EF and SEL domains would be significantly correlated
with each other and with social competence for children with ASD and the control group.
Furthermore, it was hypothesized that both SEL and EF would predict social competence
and that the relationship between EF and social competence would be partially mediated
by SEL. Separate exploratory factor analysis was conducted to examine the relationship
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amongst SEL and EF tasks to determine whether proposed domains could be used in an
overall model of social competence. Since performance-based and parent-rated EF are
arguably distinct measures of EF with weak inter-correlations, they were included in
separate models (Toplak et al., 2013).
6.5.1 Factor analysis of SEL domains. An existing model of social competence
was initially used as a basis for examining SEL skills (McKown et al., 2009; 2013.). The
McKown model proposes three SEL domains: Nonverbal Awareness, Social Meaning,
and Social Reasoning. In this model, Nonverbal Awareness is assessed using tasks that
measure affect recognition, Social Meaning is assessed using tasks that measure
understanding and interpretation of others’ communication, and Social Reasoning is
assessed using measures of social problem solving. McKown et al. specifically used
measures of pragmatic language, ToM, empathy, and the Test of Problem Solving with
their community sample, and measures of pragmatic language, social language
comprehension, and social vignettes with their clinical sample. In a recent study,
McKown et al. (2013) provided data to support a two-factor model of SEL. In this model,
facial, voice, gait and posture affect recognition made up the Nonverbal Awareness
domain, while measures of pragmatic language, social problem solving, and ToM make
up the Social Understanding domain.
The results of this study support McKown’s two-factor model of SEL for the ASD
group. The results of factor analysis for the ASD group produced two factors: Nonverbal
Awareness – the ability to infer others' emotions based on nonverbal cues; Social
Understanding – the ability to identify, monitor and appraise the effectiveness of
responses to social problems based on contextual cues social and the understanding of
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others' intentions. The results indicated that for children with ASD, the abilities to
interpret social information and to effectively resolve social problems are intricately
linked and should be considered as one domain of SEL. Although the social problem
solving measure targets the ability to reason about social situations and conflict
situations, it also likely involves the interpretation of the intentions underlying others’
words and actions, and thus having these tasks under one domain instead of two is likely
more reflective of the joint processes involved in understanding social information.
Additionally, results suggests that these two SEL domains are also robust for children
with ASD and that they can also be reliably measured using tasks commonly found in
clinical settings.
For the control group, the results of the factor analysis produce a three-factor
model: Nonverbal Awareness – comprised of facial and voice affect recognition; Social
Understanding - comprised of pragmatic language and social problem solving; Empathy -
comprised of the affective empathy task. Consistent with the McKown model, pragmatic
language and social problem solving were retained within the same domain for the
control group. Contrary to the McKown model, ToM did not correlate well with the other
SEL tasks and therefore was not included in any domain. It is unclear why the ToM task
did not correlate well with the other SEL tasks for the control group. It could be that the
task was simplified too much by using only five vignettes from Happe's Strange Stories,
in comparison to the twelve used in the McKown studies (2007; 2009; 2013), as
evidenced by the large proportion of children in the control group (86%) who obtained
perfect scores on this task. Nevertheless, the pragmatic language task also required the
ability to understand others' intentions. McKown’s two-factor model including Social
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Awareness and Social Understanding was also initially a good fit for the control group in
this study, but the decision was made to retain a third factor. A third domain comprising
the empathy task was retained for the control group because the model with the Empathy
domain was superior to the model without, and because of the importance of empathy in
most conceptualisations of social competence. It is important to note that the Empathy
factor was retained with some reservations, however, given the low correlations between
Empathy and the other two factor domains. In their latest analysis, McKown et al. (2013)
did not indicate why they excluded empathy as a critical contributor to children's ability
to interpret the meaning of social information, but it is possible that empathy would be
better categorized as part of another important contributor to social competence.
Alternatively, it is possible that the self-report questionnaire used in this study assessed
the child's beliefs and expectations about the experience of empathy, which likely reflects
societal norms and social desirability (Michalskaa, Kinzler & Decety, 2013). However,
given the high correlations with parent ratings of their child's empathy, this is unlikely.
In summary, this study demonstrated that two domains – Nonverbal Awareness
and Social Understanding – are significant contributors to SEL for both children with
ASD and neurotypical children. These two dimensions have repeatedly demonstrated
their importance to SEL and should be included when assessing children's social
comprehension skills. Empathy also appears potentially important, particularly with
neurotypical children.
6.5.2 Factor analysis of EF domains. In this study, EF is conceptualised as a
multidimensional construct that includes multiple lower order skills related to self-
regulatory processes (McCloskey, 2011; Zhou et al., 2012). An integrated model of EF -
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as the broader domain of subcomponents and processes that exert control over a person's
cognitive, behavioural and emotional regulation - was chosen because of recent evidence
demonstrating the importance of considering the separate but integral contributions of
cognitive, inhibitory and emotional aspects of EF to a child's social functioning (Barkley,
1997; Blair, Zelazo & Greenberg, 2005; Hongwanishkul et al., 2005; Jahromi et al, 2008;
McCloskey, 2011). Such an integrated approach to EF expands upon the McKown model
of social competence and includes a wider conceptualisation of self-regulation, subsumed
under the realm of EF.
The hypothesized EF model proposed that Cognitive Regulation (attention,
working memory, and planning), Behavioural Regulation (inhibition), and Emotional
Regulation (affective decision making) were fundamental components of EF. The results
of the factor analysis for the ASD group produced these three domains, with some
modifications: Cognitive Regulation – comprised of visual attention, auditory attention
and visual working memory; Behavioural Regulation - comprised of auditory inhibition
time, auditory inhibition errors and planning; Emotional Regulation – comprised of
affective decision making. There are several possible explanations for why the measures
from the Behavioural Regulation domain were not associated as expected. First, the
visual inhibition task was dropped as it did not correlate well with any other measures. As
mentioned previously, this task, which only required withholding a dominant response,
was a more simple inhibition task and was more associated with the visual attention task,
which also used the same computerized stimuli. It may be that this more simple inhibition
task relied more on visual attention than inhibiting a response because it did not require
the second step of replacing with a non-dominant response. Second, planning was
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included in this domain as it correlated most with tasks of auditory inhibition. The
planning measure used in this study - Tower of London - is generally considered a more
complex EF task and is robustly associated with inhibition in adults (Gonzalez et al.,
2010; Miyake et al., 2000). However, the relationships between these EF tasks are
inconclusive for children, with some studies reporting significant correlations (Lehto et
al., 2003) but not others (Bull, Espy & Senn
, 2004). Using the same measures as in this
study, Lehto and colleagues (2003) found a significant association between planning and
auditory inhibition while also failing to find a significant association between planning
and auditory attention. Results suggest that children's performance on the planning task
may have been related to their ability to delay their initial response (i.e. inhibition) in
order to plan their moves (Albert & Steinberg, 2011). Finally, the identification of
affective decision making as its own distinct but related domain of EF is consistent with
the sparse literature examining affective decision making in children with ASD (South
and colleagues, 2011).
In summary, three major domains of EF were distinguished in ASD: (1) those
classically related to higher order cognition or cognitive control; (2) those related to
inhibiting behaviour; and (3) those related to the coordination and control of emotional
behaviour, supporting the notion that Cognitive, Behavioural and Emotional Regulation
are different dimensions of EF. The findings further support the popular position that EF
should be considered simultaneously uniform and diverse rather than as a single entity
(Lehto et al., 2003; Miyake et al., 2000). The findings that performance on seven EF
tasks was best accounted for by three separate domains represents diversity. The finding
that there were significant correlations among the three EF domains represents unity.
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Importantly, the findings indicated that the study of these three EF domains can be
applied to children with ASD. In addition, the examination of EF through the domains of
Cognitive, Behavioural and Emotional Regulation may allow for integration of other
important areas related to children's self-regulation, such as temperament, emotional
intelligence, and effortful control (Berkman et al., 2012; Hofmann et al., 2012; Jahromi et
al., 2013; Rueda et al., 2005; Wasserman & Wasserman, 2013; Zhou et al., 2012).
Importantly, the separate domains of EF may allow for more accessible intervention
targets and research is clearly needed to determine whether these domains respond to
targeted treatment in ASD.
The three hypothesized EF domains did not emerge from factor analysis in the
control group. Instead, a three-factor model consisting of Visual Executive Control –
made up of visual attention, visual working memory, and visual inhibition; Inhibition -
made up of auditory inhibition time and errors; and Auditory Attention – made up
auditory attention, was retained for the control group. The factor domains likely emerged
differently for the control group for several reasons. First, visual attention and auditory
attention were not significantly correlated. The findings that almost one-half the control
group (47%) obtained perfect scores on the auditory attention task compared to none on
the visual attention task also suggest that they were tapping different aspects of attention.
Since the TEC battery is relatively new, there are no normative studies which have
compared performance on visual attention to alternate measures of auditory attention. For
the control group, the visual attention task was only significantly correlated with the other
two TEC measures using the same visual stimuli. The identification of a distinct Visual
Executive Control domain for the control group is, however, consistent with the results
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from the analyses of the standardization sample for the TEC. These three tasks all loaded
on the same factor in both the 5 to 7 year old and the 8 to 18 year old normative samples,
as they all appear to tap an element of selective visual attention.
Second, the planning task did not correlate significantly with any other EF
measures for the control group and thus was not retained in any domain. While it was
expected that planning would correlate highly with working memory because of the
evidence of an association between the ability to hold information in mind (working
memory) while thinking of different responses while planning (Ardila, 2013), there are
mixed results regarding the processes related to planning tasks in neurotypical children.
Albert & Steinberg (2011) examined age-related differences in strategy use on the Tower
of London in a large sample of 10 to 30 year olds and found that working memory and
inhibition were only associated with performance on the planning task in late adolescence
and adulthood. Furthermore, they suggested that the Tower of London task used in this
study may not be a good measure of effortful planning for neurotypical children because
the solutions for the more simple tasks require little planning and more complex tasks are
commonly approached using a trial and error strategy. To further clarify the nature of
effortful planning in both groups, studies comparing the strategies that children with ASD
and neurotypical children use during the planning task are needed. Whereas it has been
reported in the literature that total move scores is the most indicative of planning
(Culbertson & Zillner, 2001), this may be not an accurate representation of strategic
planning in children. Specifically, measuring how much time before making the first
move and how long it takes them to complete each trial would shed some light on this
issue. Furthermore, incorporating measures of initiation and execution time in addition to
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total correct score – as is commonly done in the adult literature (Gonzalez et al., 2010) -
will be very important to sorting out whether children with ASD rely on different
processes for successful completion of this task.
Lastly, the affective decision making task did not load on any factor for the
control group. The measure of affective decision making was included in the study to
specifically explore its role in social competence of children with ASD, as this literature
is sparse. However, the lack of significant correlations between affective decision making
and other EF measures for the control group is consistent with findings in the literature
that have also demonstrated clear distinctions between 'hot' and 'cool' EF tasks in
neurotypical children (Barraclough, Conroy, & Lee, 2004; Crone & vanderMolen, 2004;
Crone et al., 2005; Hooper, Luciana, Conklin & Yarger, 2004; Skogli et al., 2013; van
Liejenhorst, Crone & Bunge, 2006). It is noteworthy that children with ASD performed
as well as the control group on this task but that performance was only related to other
aspects of EF for the ASD group. This again suggests that children with ASD may rely
on different strategies to obtain similar overall scores. For example, it is possible that
children with ASD rely more on sustained attention and inhibition for completion of this
task. This would be consistent with recent findings that affective decision making was
significantly correlated with the parent rated behavioural inhibition scale for the ASD
group but not the control group (South et al., 2011). As mentioned previously, future
research should investigate strategy use and its relationship to other EF tasks in children
with ASD.
It is important to note that the generally low correlations between different EF
tasks in the current study for both the control and ASD groups are consistent with the
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literature. Low and statistically nonsignificant intercorrelations among EF tasks have
repeatedly been reported in children (Huizinga et al., 2006 Lehto et al., 2003),
adolescents (Lehto et al., 1996) and adults (Miyake et al., 2000). In their study of 108
neurotypical children, Lehto and colleagues (2003) reported low intercorrelations among
14 EF measures. These low correlations likely contribute to the myriad of discrepant or
inconclusive findings within the EF literature (Wasserman & Wasserman, 2013).
Furthermore, it is also fairly common to find differential group correlations when
comparing children with ASD to neurotypical peers (Demopoulus et al., 2013; Dyck et
al., 2006; Gepner et al., 2001; Oerlemans, 2013; Peppe et al., 2007), yet this is rarely the
focus of discussion. Unfortunately, most studies focus on group differences and do not
report correlations between measures (Barron-Linnankoski et al., 2014; Reinvall et al.,
2013; Semrud-Clikeman , Goldenring Fine & Bledsoe, 2013; van Rijn et al., 2013), or
else report combined group correlations (Demopoulus et al., 2013) or only those for the
ASD group (Faja et al., 2013).
6.5.3 Parent rated EF. As expected, the parent ratings of EF were not
significantly correlated with performance-based measures of EF for the control group
(Toplak et al., 2013) and only significantly correlated with two out of eight performance-
based EF measures (auditory attention and visual inhibition) for the ASD group. These
results emphasize the importance of using parent-ratings of EF as complementary rather
than equivalent measures of EF in research and clinical practice. Unfortunately, this has
not been common in the literature, with several studies relying solely on the BRIEF
(Kalbfleisch & Loughan; Rosenthal et al., 2013; Stichter et al., 2012), and others
referring to BRIEF scores as equivalent to the broad construct of EF (e.g., Jahromi, Bryce
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& Swanson, 2013; Kloosterman, Kelley, Parker & Craig, 2014; Scheeren, Koot &
Begeer, 2012). Interestingly, Gomez-Guerrero et al. (2011) found significant correlations
between BRIEF parent ratings and the TEC visual inhibition task in a group of eight to
twelve year old children with ADHD. Moreover, the finding that the TEC visual
inhibition task was significantly correlated with all BRIEF composite scores despite no
group differences on this measure, has several implications. It may be that the TEC visual
inhibition task is sensitive to the application of EF in daily tasks. However, it is also
possible that the basic abilities of sustaining visual attention and withholding a dominant
response are related to the overall regulation of behaviour in everyday context for
children.
A three-factor model of parent rating of EF was hypothesized, based on previous
clinical developmental research (Gioia et al., 2002). The results of the factor analysis
produced an identical solution of two domains for both groups: Metacognition and
Behavioural Regulation. The relationship between the three domains of the performance-
based EF and the two domains of the parent-rated EF was subsequently examined. For
the ASD group, the Behavioural Regulation domain was significantly correlated with the
performance-based Cognitive Regulation domain. This is an interesting finding as it
indicates that for children with ASD, performance on higher-order cognitive EF tasks is
related to everyday use of inhibitory control, flexibility, and modulation of emotions,
which has specific clinical implications for intervention. For the control group, there were
no significant correlations between performance-based and parent-rated EF domains.
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6.6 Relationship between SEL and EF Domains
The relationship between factor analysis derived SEL domains of Nonverbal
Awareness and Social Understanding and the EF domains of Cognitive Regulation,
Behaviour Regulation, and Emotion Regulation were explored for children with ASD.
The results demonstrated some significant relationships between SEL and EF domains in
the ASD group, findings consistent with the previous literature (Ahmed & Miller, 2011;
Oerlemans et al., 2013). Specifically, two SEL domains – Social Understanding and
Nonverbal Awareness – were significantly correlated with two EF domains - Cognitive
Regulation and Behavioural Regulation. For the children with ASD in this study, the
ability to sustain basic attention, keep things in mind while doing another task, and inhibit
primary responses in favour of a dominant one are related to how well they can recognize
affect in others, how effectively they can problem solve social situations, interpret social
cues, and take the perspective of others. The relationship between affective decision
making and SEL tasks has not been previously been explored in children with ASD.
While it was hypothesized that the Emotion Regulation domain would be significantly
related to SEL since 'hot' EF is purported to extend the EF construct to everyday social
decision making, which is typically conducted in the presence of motivational or
emotional influences (Zelazo & Carlson, 2012), this was not found. This lack of
relationship between Emotion Regulation and SEL domains of Social Understanding and
Nonverbal Awareness could be related to the strategies used to complete SEL tasks. It
may be that children with ASD approach social comprehension tasks in a more logical
and rational manner which are free from or absent of emotional or motivational
influences. This is clearly an area for further study.
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For the control group, the relationships between the factor analysis derived SEL
domains of Nonverbal Awareness, Social Understanding, and Empathy and the EF
domains of Visual Executive Control, Inhibition, and Auditory Attention were also
explored. This is the first study to explore the relationship between a wide range of SEL
and EF tasks in neurotypical children. Visual Executive Control (visual attention; visual
working memory; visual inhibition) was significantly correlated with Social
Understanding (pragmatic language; social problem solving) and Nonverbal Awareness
(facial and voice affect recognition). The results suggest that for neurotypical children,
the ability to sustain visual attention, keep images in mind and inhibit primary responses
are related to how well they can recognize affect in others, effectively problem solve
social situations, and interpret social cues. It is not clear why the Inhibition or Auditory
Attention domains did not correlate with any of the SEL domains. In contrast to children
with ASD, it is possible that socially competent neurotypical children do not engage or
rely on these skills to succeed in social comprehension tasks. These differences between
groups have been reported in the literature. In one study of 267 children and adolescents,
facial and voice affect recognition was significantly correlated with inhibition and verbal
working memory for the ASD but not the control group (Oerlemans et al., 2013).
The relationship between the factor analysis derived SEL and parent rated BRIEF
domains was also examined for both groups. For children with ASD, there were no
significant associations between either Metacognition or Behavioural Regulation and any
SEL domain. For the control group, the parent rated Behavioural Regulation domain was
significantly correlated with the SEL domains of Social Understanding and Nonverbal
Awareness. The contrast between the two groups is interesting. These results suggest that
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for neurotypical children, but not children with ASD, the ability to inhibit behaviour and
modulate emotions flexibly in everyday settings appears related to their understanding of
social situations and ability to recognize emotions.
6.7. SEL and EF in Relation to Social Competence
The results of this study supported the hypothesis that SEL and EF domains
would be significantly correlated in the ASD group. The further hypothesis that SEL and
EF domains would both be significantly correlated with social competence was only
partially supported. Specifically, two EF domains were related to Social Competence, but
there were no significant associations between any SEL domains and Social Competence
for the ASD group. The lack of association between SEL and Social Competence was
unexpected, particularly given the numerous publications outlining the specific deficits of
the ASD population in relation to SEL skills (Chasson & Jarossiewicz, 2014) and the
presumed importance of SEL skills in children's ability to navigate the social world that
is emphasized throughout the ASD literature. Furthermore, the lack of association
between SEL and social competence in children with ASD was surprising given the
robust outcomes in the McKown studies that found that all three SEL domains –
Nonverbal Awareness, Social Meaning, and Social Reasoning –predicted significant
variance in social competence across community and clinical samples. The measures
used in the McKown studies were comparable or identical to those used in the current
study and the age range of the participants in the McKown studies, albeit a bit wider was
similar to that of the current study. There are, however, some important differences. First
of all, McKown et al. had much larger sample sizes, including 186 community-recruited
neurotypical children and 119 clinic-referred children. Second, their clinical sample
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included children with various DSM-IV diagnoses, such as ADHD, ASD and mood and
anxiety disorders, as well as some who did not meet formal criteria for any clinical
diagnosis. As such, their clinical group did not demonstrate overall deficits in SEL as the
children in the current study; furthermore, the clinical group in the McKown studies
demonstrated higher mean scores than their own control group on ToM and pragmatic
language, and equivalent scores on all three measures of social problem solving. As such,
clinical implications are potentially less meaningful when children with different
diagnoses are grouped together. Therefore, since McKown et al. (2007; 2009) combined
children with ASD with other groups of children who did and did not have other DSM
diagnoses, their results may not accurately reflect clinical populations. Third, the
McKown studies (2009; 2013) used Structural Equation Modeling (SEM), a sophisticated
data analysis technique with potentially greater power to detect relationships that may be
missed, even in the same data set, using bivariate correlation analyses (Wilcox, 2001).
This is evident in McKown's own studies where nonverbal awareness was found to be
significantly predictive of social competence only after the data were re-analysed using
SEM. The sample size in the current study was simply too small for a more sophisticated
data analysis.
Although McKown et al. demonstrated the predictive utility of SEL in relation to
social competence in a diverse clinical sample, the relationship may not apply in the same
manner with a specific ASD sample. It is possible that the lack of relationship between
SEL and social competence emphasizes the distinction between 'knowing' which involves
the ability to provide correct answers on social comprehension tasks and 'doing' which
involves the application of these skills in more complex social situations. Since no
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information was gathered regarding the children's past or present interventions, it is also
possible that direct teaching of these skills, as is often done in both social skills groups
and individual treatment, prepared children to answer social comprehension questions,
thus providing children with the correct knowledge to correctly answer questions
regarding various social scenarios, but not the skills to apply the knowledge in social
situations. Alternatively, it may be that the children who were more successful on the
SEL tasks were using a more cognitive-based strategy, which helped them perform better
on the task but which may not necessarily translate to more real-world scenarios and
situations, as tapped by the social competence measures. Therefore, future research may
want to use role-play scenarios and more thoroughly evaluate what processes are used to
arrive at the answer. Finally, although SEL is not associated with the aspects of social
competence measured in this study, it may be related to other aspects of social
functioning. For example, it is possible that SEL is associated with problem behaviours.
Although this was not measured in this study, SEL domains predicted problem
behaviours in the first McKown (2007) study.
Two domains of EF - Cognitive Regulation and Emotion Regulation - were
significantly correlated with Social Competence in the ASD group. This finding suggests
children with ASD who have better selective and sustained attention, working memory,
and affective decision making are also more socially competent. There has been some
research examining the relationship between inhibition and social functioning in children
with ASD and most has measured deficits rather than competence. Some studies failed to
find a relationship between inhibition and social impairments (Bishopp & Norbury, 2005)
or social skills (Kentworthy et al., 2009) whereas other have reported a significant
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association between inhibition and social impairments (Joseph & Tager-Flusberg, 2004).
Certainly, the association between inhibition and behaviour problems has received the
most attention with neurotypical children and those with ADHD and there is some
consensus that poor inhibition is associated with behaviour problems for these
populations (Nigg et al., 1998; Tilman, Brocki, Sorensen & Lundervold, 2013; White,
Jarrett & Ollendick, 2013). The results of this study add to the literature by demonstrating
that although children with ASD performed significantly worse than the control group on
measures of Behavioural Regulation, this deficit was not associated with Social
Competence. Further evaluation with alternate measures of behavioural inhibition should
be conducted to explore whether other aspects of Behavioural Regulation may be
associated with Social Competence. For example, using delay of gratification tasks, tasks
of behavioral persistence (e.g. puzzle box; Zhou et al., 2012), the Touch Your Toes task
(Ponitz et al., 2008), and/or the slow-down motor task (Lisonbee, Pendry, Mize &
Gwynn, 2010).
In contrast, for the control group, two SEL domains – Social Understanding and
Empathy - were significantly correlated with Social Competence. These findings suggest
that having congruent emotional responses to others' distress, the ability to understand
and apply the social meaning of language, and problem solve common social situations
were related to children's social skills and social adaptive behaviours in the control group.
As mentioned previously, the lack of significant correlations between Nonverbal
Awareness and Social Competence is consistent with McKown (2007) and others that
examined the four basic emotions (Adolf, Sears & Piven, 2001; Castelli, 2005; Gross,
2004; Harms et al., 2010). Furthermore, the Nonverbal Awareness domain from the
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McKown et al. (2009) community sample included measures of gait and posture, as well
as facial and voice affect recognition. It may be that these additional tasks are more
highly associated with social competence. On the other hand, EF did not relate to social
competence of children in the control group. Few studies have evaluated the association
between EF and social functioning in neurotypical children in this age range, and of those
that did, most focused on problematic behaviours rather than social competence (Huyder
et al., 2013; Thorell et al., 2004; Vuontele et al., 2013). For example, Ciairano and
colleagues found that inhibition predicted problematic but not cooperative behaviours in
7 to 12 year old neurotypical children. While it may be that better EF performance does
not relate to prosocial behaviours and that worse EF performance is related to
problematic behaviours, simultaneous investigations of pro-social and problematic
behaviours are needed to clarify this relationship for neurotypical children. In addition,
since the EF task in this study were specifically chosen because of expected sensitivity to
deficits in children with ASD, there are many other components of EF which were not
evaluated. The inclusion of other components of EF, such as processing speed, flexibility,
and/or updating and shifting, may render different results. Furthermore, the tripartite EF
model used in this study was chosen for its potential to inform specific ASD
interventions. That is, more simple tasks were chosen in an attempt to minimize shared
variance and to isolate specific underlying skills that could potentially be targets for
intervention. Inclusion of more complex EF tasks, such as the Wisconsin Card Sorting
Task or Rey-Osterrieth Complex Figure Test, may have been more sensitive to variations
in social competence for neurotypical children.
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Parent ratings of the Metacognition and Behavioural Regulation domains of EF
were significantly correlated with Social Competence within both groups. It is important
to note that these results may have been influenced by shared-rater bias since parents
concurrently completed the BRIEF and social competence measures. The potential exists
for parents to provide answers on both the BRIEF and Social Competence questionnaires
based on the same behaviour because all three measures gather information based on a
child’s behaviour in real life settings.
6.8 Predictors of Social Competence
An important aim of this study was to examine whether EF and SEL predicted
social competence. The hypothesis that EF and SEL would predict Social Competence in
children with ASD and children in the control group received partial support (see Figures
7-10). For the control group, the SEL model significantly predicted 28% of the variance
in Social Competence. One domain, Social Understanding, and IQ were significant
predictors of social competence, whereas age, Nonverbal Awareness, and Empathy did
not significantly contribute. In a separate regression analysis, EF domains did not
contribute significantly to Social Competence. These findings suggest that targeting SEL
skills, particularly teaching social problem solving, rules of conversation, and the
importance of adjusting language based on context should be considered in social skills
interventions. In contrast, the performance-based EF model significantly predicted 28%
of the variance in Social Competence in the ASD group, with the Cognitive Regulation
domain and age being significant predictors. These contrasting results are theoretically
and clinically important: SEL domains, which were impaired in the ASD group, did not
predict Social Competence, but EF did. Most notably, this study demonstrated that for
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children with ASD, Cognitive Regulation processes make an important contribution to
Social Competence, providing an important basis for targeted clinical interventions.
These results are consistent with other studies that have found that EF is involved in
social abilities (Diamantopoulou et al., 2007; Rinsky & Hinshaw, 2011; Wahlstedt et al.,
2008). The significant contribution of age demonstrated that increasing age predicted
lower social competence for children with ASD.
Analyses with parent ratings of EF were conducted separately because of the
weak correlations with performance-based EF found in this study and the idea that parent
ratings and performance-based EF measures evaluate different facets of EF. Parent
ratings of EF predicted significant variance in Social Competence for both the ASD and
control group. One domain of the BRIEF - Behavioural Regulation - along with IQ,
predicted 31% of variance for the control group. The BRIEF Behavioural Regulation
domain – along with age, predicted 54% of variance in Social Competence for children
with ASD. The BRIEF Metacognition domain did not contribute to significant variance
to Social Competence for either group. These results are not surprising since the
Behavioral Regulation domain captures children's ability to maintain appropriate
regulatory control of their behavior and emotional responses, and some questions on the
measure are specifically related to aspects of social relatedness. Nevertheless, there are
important implications for these findings. The BRIEF Metacognition domain, which was
impaired in the ASD group, did not significantly predict Social Competence in this group
whereas the Behavioural Regulation domain did. These results support the premise that
parent ratings of EF can provide valuable information regarding children's Social
Competence. Furthermore, it suggests that those working in clinical settings should pay
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Figure 7. The relationship between social competence, SEL
and EF for children with ASD
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Figure 8. The relationship between social competence, SEL
and parent-rated EF for children with ASD.
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Figure 9. The relationship between social competence, SEL
and EF for the control group
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Figure 10. The relationship between social competence, SEL
and parent-rated EF for the control group
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particular attention to the parent ratings on the Behaviour Regulation domain as it may be
a good gauge of how children are managing socially. Additionally, clinicians should not
dismiss or discount adequate parent ratings on the Metacognition domain as they may not
be representative of the child's Social Competence.
Previous studies have primarily emphasized performance-based EF 'profiles' of
children with ASD (e.g. Barron et al., 2014; Troyb et al., 2013) as a means of assessing
clinical impairment. The results of this study extend this approach and highlight the
important relationship between EF and social competence. Future studies should explore
how this relationship is manifested in order to further understand the processes involved
with the social competence of children with ASD. The finding that the parent rated
Behavioural Regulation domain was significantly correlated with performance-based
measures of Cognitive Regulation and that both were significantly predictive of Social
Competence is important as it provides important information about how EF impacts
Social Competence. It has been proposed that behavioural regulation is a precursor to
cognitive regulation (Ardila, 2013; Gioia et al., 2000; MacKenzie, 2013), but further
research is needed to explore the nature of this relationship. Since the results
demonstrated that EF does not impact social competence through SEL skills (as initially
hypothesized), researchers might explore other ways in which EF impacts social
competence. The relationship between performance-based Cognitive Regulation and
parent-rated Behavioural Regulation also has important clinical implications and provides
support for interventions that specifically target both areas. The Self-Regulation Program
for Awareness and Resilience in Kids (SPARK: MacKenzie, 2013) is noteworthy because
it uses a progressive approach that specifically targets behavioural regulation before
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targeting cognitive regulation through a variety of explicit and practical strategies. The
age of the child should also be considered when implementing intervention strategies.
There is some evidence that cognitive regulation strategies benefit older children more
than younger ones (Riccio & Gomes, 2013). Determining whether different EF
interventions are more effective at different stages in development will be instrumental to
clinical practice and to addressing the increasing gap in social competence between
children with ASD and neurotypical peers throughout childhood.
The results of the current study thus suggest that both performance-based and
parent-ratings of EF should be included as integral components of assessment and
intervention with children with ASD. Although leading researchers in the field of ASD
have recently recommended systematic implementation of EF interventions for school
aged children with ASD (Szatmari, Charman & Constantino, 2012), more research is
needed to determine the efficacy and effectiveness of this intervention. Targeted
interventions are needed to explore whether gains in Cognitive and Behavioural
Regulation may in fact generalize to everyday behaviours and improve social
competence. In three recent reviews of EF interventions for children, diverse targeted and
multi-modal approaches are identified that have been shown to improve children's EF
(Cicerone, Levin, Malec, Stiss & Whyte, 2006; Diamond, 2012; Riccio & Gomes, 2013).
These include computerized training, biofeedback, verbal mediation, external cueing,
environmental restructuring and mindfulness meditation. The scaffolding of external
strategies is also recommended because the goal is not to train task-specific performance,
but rather the training and internalisation of regulatory cognitive processes (McCloskey,
2011). There is some evidence that one aspect of Cognitive Regulation – working
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memory - can be improved with targeted training with long term gains in ability for both
neurotypical children (Diamond & Lee, 2011, 2012) and those with HFASD (de Vries,
Prins, Schmand, & Guerts, 2012). However, even though computerized working memory
training has been effective in improving working memory, the transfer to other areas or
skills has been narrow and thus programs that target more components of EF are
recommended (Diamond, 2012). Multi-modal interventions that target both behavioral
and cognitive regulation, have been found to have positive effects on inhibitory control,
working memory, and cognitive flexibility in typically developing children (Diamond &
Lee, 2011), but research is needed to determine whether these improvements in EF could
also influence social competence. Although the efficacy of EF as a treatment to increase
social competence has not been established, the results of this study provide clear support
of this idea. Furthermore, a review of recent studies concluded that it is children with the
poorest EF who gain the most from EF interventions (Diamond & Lee, 2011).
Interestingly, recent findings also suggest that children with ASD who had higher
baseline social skills are the ones who benefited the most from group social skills
interventions (Chang et al., 2013).
SEL skills are generally considered important in the normal development of social
competence. The results of this study indicated that SEL domains were not significantly
predictive of Social Competence in the ASD group, but nonetheless SEL might still be an
important factor to consider. It is interesting to speculate that incorporating targeted EF
training in more traditional social skills interventions may increase effectiveness. For
example, the SPARK intervention mentioned earlier specifically targets cognitive,
behavioural and emotional regulation for children with ASD (MacKenzie, 2013). The
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specific inclusion of visual attention training in SPARK is supported by the results of the
current study, particularly given the speculation that it may be a critical skill for children
with ASD. Cognitive Behavior Intervention (CBI; Bauminger et al., 2002) and Social
Competence Intervention (SCI: Stichter et al., 2010; 2012) have also shown promising
results in reducing problematic behaviours and social deficits by specifically targeting
metacognitive strategies, self-monitoring, and self-regulation in addition to teaching
traditional social skills of emotion awareness and perspective taking. Moreover, it was
found that children with higher self-regulation benefited most from social skills treatment
(Chang et al., 2013), again providing support for the importance of targeting EF skills
prior to or in combination with social interventions.
The finding that both performance-based and parent-ratings of EF predicted
significant variance in the social competence of children with ASD is a novel and
exciting result of this research. However, there is still much variance to be explained in
the social competence of children with ASD. For example, this study was limited to two
areas associated with social competence – SEL and EF – and, as such, is not an
exhaustive examination of potential antecedents of social competence. In trying to adapt
the established McKown model of social competence to specifically incorporate the role
of EF, other potential influential factors were excluded. Temperament, parenting,
siblings, mental health, motor skills, and emotional intelligence are all potential
influential factors but were not examined in this study. The importance of considering
other factors is demonstrated in several recent studies. For example, emotional
intelligence predicted social interactions in a small sample of 16 to 21 year olds with
Asperger Syndrome (Montgomery, Stoesz & McCrimmon, 2012), effortful control was
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inversely related to social deficits (Konstantareas & Stewart, 2006; Jahromi et al., 2013)
and associated with prosocial peer engagement in children with ASD (Jahromi et al.,
2013), and negative affect temperament has also been negatively associated with social
adaptive behaviour in ASD (Millea, Shea & Diehl, 2013).
6.9 Study Limitations
This study is limited by several factors. Children with intellectual disability were
excluded and, as such, the results are not generalizable to the entire ASD population.
However, the functioning level specified in this study (IQ > 80) was consistent with
criteria used by other studies of EF in high-functioning children with ASD (e.g.,
Kenworthy et al., 2005; Landa & Goldberg, 2005; Troyb et al., 2013) and still allowed
for a wide range of IQ scores. Girls with ASD were also excluded from this study. Given
the 4.6 to 1 ratio of boys to girls (CDC, 2012), the sample in this study does nonetheless
represent the majority of children with ASD. The decision to exclude girls in this study
was made based on the consistent gender differences reported in the variables of interest
in this study (SEL, EF, social competence) (Boghi et al., 2006; Gentzler, Kerns &
Keener, 2010; Huizinga et al., 2011; Lemon, Gargaro, Enticott & Rinehart, 2001;
Willoughby et al., 2009).
The small sample size limited the strength of the analysis and the ability to use
more robust statistical analyses, such as SEM. The number of participants in this study,
however, is typical of studies examining EF in clinical populations. Attempts were made
to obtain more participants by recruiting children with ASD from two additional health
regions and by attending all available ASD parent meetings within the geographic area.
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Previous clinical diagnostic reports were also used to confirm the diagnosis of ASD
rather than secondary confirmation using the Autism Diagnostic Interview—Revised
(ADI-R; Lord et al. 1994) or Autism Diagnostic Observation System (ADOS; Lord et al.
1999), as is done in some studies. Importantly, other studies that did secondarily confirm
a diagnosis of ASD using the ADOS or ADI-R demonstrated similar results in their
Vineland Socialisation scores as in the present study, indicating that the current sample
was characteristic of the larger samples in these studies (Liss et al., 2001; Topaka et al,
2013). Notably, whereas many studies also used the results from the ADOS or ADI-R as
an outcome measure, the current study used a separate outcome measure that focused on
ability rather than disability. In addition, the data was collected at a single time and
performance on SEL or EF tasks may have thus been influenced by external factors on
the day of assessment. Although the tasks were administered in the same order for both
groups, many children with ASD needed more frequent breaks and thus the impact of
frequent breaks and hence longer sessions on task performance is unknown. However,
observations made by the researcher suggested that the frequent breaks improved rather
than hindered performance.
The use of only parent ratings of social competence may have provided a limited
or biased view of children's social competence. Although multi-method, cross-informant
evaluation of social competence is ideal because it reduces the effects of measurement
factors, particularly parent-related factors, on ratings of problem behaviours (Bennett et
al., 2012), there is substantial research that supports the use of a single informant
approach for social skills and adaptive behaviour. Studies have consistently found no
significant differences between parent and teacher ratings on social skills (Gresham,
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Elliot, Cook, Vance & Kettler, 2010; Murray, Ruble, Willis & Molloy, 2009; Vickerstaff
et al., 2007) and on the Vineland (Gagnon, Nagle & Nickerson, 2007; Szatmari, Archer,
Fisman & Streiner, 1994; Voekler, Shore, Hakim-Larson & Bruner, 1997) for children
with ASD. This is in contrast to the low inter-rater agreement generally found among
informants for problem behaviours and psychopathology (Kanne, Abbacchi, &
Constantino, 2009; Salbach-Andrae, Lenz & Lehmkuhl, 2009). High parent-teacher
agreement on social skills ratings suggests that social behaviours are more consistent
across settings for children with ASD. Additionally, the measurement of social
competence via parent ratings has several advantages. Rating scales assess a broad range
of behavior often not observed during direct observation (McConaughy & Ritter, 2005).
Furthermore, available normative data provide a standard for comparing behaviour with a
typically large and representative sample (Gresham & Elliott, 2008; McConaughy &
Ritter, 2005).
6.10 Conclusions and Future Research
This study aimed to clarify the role of SEL and EF in the social competence of
children with ASD. Previous studies in the literature have failed to incorporate a range of
EF components, have not comprehensively explored the relationship between EF and
SEL in relation to social competence, and focused on measures of impairment or
disability rather than social competence. This is the first study to concurrently evaluate
important SEL components, performance-based and parent-rated domains of EF in
children with ASD and a neurotypical control group. In addition, this study added
significantly to the ASD literature by examining the relationship between SEL and EF
domains specifically in relation to social competence.
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Results of this study demonstrated that better performance on two SEL domains
(Social Understanding and Empathy) and one parent rated EF domain (Behavioural
Regulation) were significantly correlated with higher Social Competence for children in
the control group. Social Competence was not associated with any performance-based EF
domains in the control group. IQ, the SEL domain of Social Understanding and the
parent-rated EF domain of Behavioural Regulation independently predicted significant
variance in Social Competence for the control group. Performance-based EF did not
predict significant variance in Social Competence for the control group. For children with
ASD, better performance in performance-based Cognitive Regulation and the parent
rated EF domains of Metacognition and Behavioural Regulation were significantly
correlated with higher Social Competence. Social Competence was not significantly
correlated with any SEL factors in the ASD group. Finally, performance-based Cognitive
Regulation, parent rated Behavioural Regulation, and age independently predicted
significant variance in Social Competence for the ASD group. IQ and SEL did not predict
significant variance in Social Competence for children with ASD.
The importance of EF to the social competence of children with ASD was
demonstrated through both performance-based measures and parent ratings.
Performance-based and parent ratings of EF also highlighted the importance of
considering the separate but integral contributions of cognitive, behavioural and
emotional regulation to children's social functioning. Notable results of the current study
are that (1) performance-based cognitive EF domains were related significantly to
everyday behavioral regulation, based on parent ratings; (2) performance-based and
parent ratings of EF significantly predicted social competence; and (3) SEL did not
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contribute significant variance in social competence for the ASD group. This is
particularly interesting in light of the contrasting findings for the control group where the
SEL domain of Social Understanding significantly predicted social competence, whereas
no EF domain did. The results also demonstrated that deficits in social competence tend
to increase with age for children with ASD by virtue of a widening gap with that of their
neurotypical peers. These results have important implications for clinical assessment and
intervention in ASD: (1). both performance-based and parent-ratings of EF should be
included as integral components of assessment and intervention; (2) targeted EF
interventions may be an effective means of increasing social competence in children with
ASD, and ultimately improving long-term mental health and behavioural outcomes.
Finally, although SEL domains were not significantly correlated with social competence
in the ASD group in this study, such factors should still be considered an important area
to assess in children with ASD. As it is likely that SEL domains are important to other
areas of social functioning, evaluation of these skills should also be used to help guide
individualised treatment.
Further research is needed to further explore the relationship between EF and
social competence in children with ASD. The finding that Cognitive Regulation accounts
for significant variance in social competence of children with ASD needs to be examined
in studies with a larger sample that can make use of more sophisticated analyses, such as
SEM. Other aspects of self-regulation should also be explored in further studies to
additionally clarify the relationship between EF and social competence. The importance
of assessing multiple integrated components may also allow for even more targeted
intervention strategies (Wasserman & Wasserman, 2013) and is in line with recent
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literature strongly encouraging a more integrated conceptualisation of EF and self
regulation (Ardila, 2013; Isquith, Roth & Gioai, 2013; Koziol, 2013). Additionally, more
focus on the developmental emergence and trajectories of Cognitive, Behavioural, and
Emotional regulation is required. Longitudinal studies could help identify whether there
are disparate developmental trajectories or maturational delays for children with ASD
and inform at which age targeted EF interventions may be most beneficial. This would
inform interventions to address EF impairments in early childhood in order to increase
social competence. Although current findings demonstrate the important role of EF in
social competence, more information is needed on how EF impacts social competence
and on what factors indeed influence EF. For example, sociocultural factors, such as
parent scaffolding and parent-child interactions, predict earlier EF development and
better performance on EF measures in neurotypical children (Pellicano, 2012). Exploring
factors that contribute to EF performance in children with ASD may provide another
avenue for intervention, possibly even at a younger age.
Many researchers have alluded to a distinct EF profile in children with ASD
(Happe et al., 2006; Reinvall et al., 2013), but to date the patterns of strengths and
difficulties has not been solidified because of inconsistent results in the literature. It is
unclear whether this is by virtue of the wide array of individual tasks purporting to
measure the same aspects of EF, which are often accompanied by varying complexities
and modalities. Alternatively, it could be that the nature of the heterogeneity within ASD
eludes identifying the types of specific EF profiles that have been demonstrated in
children with ADHD (Holmes et al., 2010) and FASD (Rasmussen et al., 2013). A
current meta-analysis of studies examining EF in high-functioning children with ASD
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may help clarify this issue, although the varying measures and complexity levels would
make this a challenging task. Furthermore, children with ASD may use different
strategies that rely on different underlying cognitive processes to complete SEL and EF
tasks. A comprehensive review of correlation studies may prove indispensable to shed
some light on this issue and further explore whether EF tasks are truly differentially
affected in children with ASD. Incorporating strategy measures, specifically examining
process-related elements of various tasks and physiological measures are critical for
future EF research. The accumulating evidence that children with ASD use different
strategies to successfully solve SEL and EF tasks, as well as the different correlations
among age, IQ, SEL, and EF measures also highlight the need for future studies to focus
on the heterogeneity within the ASD population. Exploring why some children are
successful rather than focusing on what is associated with their deficits or impairments
could also provide valuable clinical information. This may help to narrow the striking
disparity between their cognitive abilities and social competence.
Finally, the results of the current study highlight the need to go beyond
comparisons with neurotypical children and examine whether and why EF tasks are
differentially affected in ASD. Despite overall group deficits in SEL, EF and social
competence, the ASD group had scores ranging from the average to impaired range on
most measures. This heterogeneity in SEL and EF is reflective of the reported
behavioural heterogeneity in the ASD population. Given this intra-group heterogeneity, it
is time to go beyond group comparisons and focus on which aspects specifically
influence successful performance within this group, in order to reduce long-term negative
impacts, and improve overall quality of life for children with ASD and their families.
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APPENDIX A: Ethical Approval
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APPENDIX B: Recruitment Poster
Project Title: The Role of Executive Function and Social Emotional Leaning Skills in Social Competence of Boys with and without High-Functioning Autism Spectrum Disorder
Overview and Description:
8-12 year old boys with and without high functioning autism spectrum disorder (Asperger's, PDD NOS, high functioning autism) and their parent(s) are being invited to take part in a study of social competence, which is how well we get along with others. Social competence is important because it is associated with academic success and overall mental health. Learning more about which factors best predict social competence may later help us provide useful treatments for children who have social difficulties. This study is designed to examine which factors are most important in getting along with others.
What's involved in the study?
Before you agree to participate, we would like to give you some information about what's involved with this study. Parents will be asked to complete 3 questionnaires – 2 about your child's daily social skills and 1 about Executive Functions (i.e., attention, planning, memory). Children will be asked to complete 10 short tasks. There is no reading, writing or mathematics involved in any of the tasks. Some are computerized activities where the child has to remember objects he saw on the screen, pay attention to instructions and solve problems. Others involve looking at pictures and answering questions about what the characters feel and think. This will take approximately 90 minutes. Breaks and snacks will be provided. To recognize your child's participation, he will be provided with a Cineplex gift certificate for a movie, popcorn and drink.
Please call or email me if you want more information or are interested in participating.
Nathalie Berard Primary Researcher Department of Psychology, University of Regina Supervisors: Lynn Loutzenhiser, Ph.D., University of Regina (585-4078)
Dennis Alfano, Ph.D., University of Regina (585-4220)
The Research Ethics Board of the University of Regina has approved of this project. If you have any questions or concerns about your rights as a research participant, you may contact Dr. Bruce Plouffe, Chair of the Research Ethics Board at the Office of Research Services (AH 505) at 585-4775 or by email to [email protected]. The Research Ethics Board of the Regina Qu'Appelle Health Region has approved of this project.
If you have any questions or concerns about your rights as a research participant, you may
contact Dr. Elan Paluck, Chair of the Research Ethics Board at (306) 766-5451.
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APPENDIX C: Consent and Assent Forms
INFORMATION AND ASSENT FORM FOR CHILDREN
PROJECT TITLE: The Role of Executive Functions and Social Emotional Learning Skills in Social Competence of Boys with High-Functioning Autism Spectrum Disorder.
Student Researcher: Nathalie Berard, MEd, University of Regina: Department of Psychology Supervisors: Dennis Alfano, Ph.D., University of Regina (585-4220)
Lynn Loutzenhiser, Ph.D., University of Regina (585-4078).
You are being invited to join a study that is looking at which things are most important for
making friends and getting along with others.
What will I be asked to do?
(1) We will meet today for about 1.5 hours, with breaks when needed. You will be asked to complete 10 different activities, including answering questions about what people think and feel in stories, pictures and short social scenarios. You will also be asked to do some work on the computer. There is NO reading, writing or mathematics involved.
(2) You will get one gift certificate for a Cineplex movie theatre with popcorn and a drink to thank you for taking part in the study.
(3) You will be given a copy of this assent form to keep
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Are there any risks if I do the study?
No, we do not think that there is any harm to you if you do this study. You may become tired
during the tasks and can take a break at any time. We will also have scheduled breaks where
you will be provided snacks and water or juice.
Are there any benefits if I do the study?
Your participation will help us learn important information, such as what skills are important for
getting along well with others, and how we can help children learn those skills.
Will anybody know how I answered the questions?
It is our job to keep your answers private, which means that we do not talk to anyone else about
what you tell us. All of the information we collect will be kept in a locked cabinet and on a
computer that needs a password, at the University of Regina. Your name will not be stored on
the computer or on any of your answer sheets.
Do I have to participate?
No, you only have to join the study if you want to. You can chose if you want to do it. Also, if
you do choose to do the study you can stop at any time, you can skip any questions that make
you uncomfortable, and/or you can ask that the answers you give not be used in the study.
Nobody will be mad if you decide not to do it, or if you decide to stop doing the study before
you are finished. It is your choice. If you decide to stop doing the study at any point, you will
still receive the gift certificate to the movie theatre.
Who can I talk to if I have any questions?
If you have any questions or have something to say about the study you can call the person
doing the study, Nathalie Berard at (306) 585 4078, or email her at [email protected].
You can also contact the (supervisors) people in charge, Dr.Lynn Loutzenhiser at (306) 585 4820
(email: [email protected]) or Dr. Dennis Alfano, at (306) 585-4220 (email:
If you have questions about your rights as a person doing it (participant), you may contact the
Chair of the Research Ethics Board at (306) 585-4775 (email: [email protected]).
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I AGREE TO PARTICIPATE IN THE STUDY
I UNDERSTAND I CAN STOP AT ANY TIME
______________________________________ ______________________________
Printed name of participant Date
____________________________________ ________________________
Signature of Witness Date
_____________________________________ ________________________
Signature of principal investigator/designated Date
representative-if applicable
The Research Ethics Board of the University of Regina has approved of this project. If you have
any questions or concerns about your rights as a research participant, you may contact Dr.
Bruce Plouffe, Chair of the Research Ethics Board at the Office of Research Services (AH 505) at
585-4775 or by email to [email protected].
The Research Ethics Board of the Regina Qu'Appelle Health Region has approved of this project.
If you have any questions or concerns about your rights as a research participant, you may
contact Dr. Elan Paluck, Chair of the Research Ethics Board at (306) 766-5451.
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INFORMATION AND CONSENT FORM FOR PARENTS
PROJECT TITLE: The Role of Executive Functions and Social Emotional Learning Skills in Social Competence of Boys with High-Functioning Autism Spectrum Disorder.
Student Researcher: Nathalie Berard, Doctoral student, University of Regina.
Supervisors: Dennis Alfano, Ph.D., University of Regina (585-4220)
Lynn Loutzenhiser, Ph.D., University of Regina (585-4078).
Introduction: You and your child are being invited to take part in a study that is looking at which factors
are most important for making friends and getting along with others.
Purpose: The primary purpose of the study is to find out if and how certain skills predict social competence. Specifically, I am looking at whether social emotional learning (e.g. recognizing facial emotions, solving social problems, understanding others' thoughts and feelings) and executive function (e.g. attention, memory, planning) are associated with children's ability to interact well with others.
Voluntary Participation: Participation in this study is completely voluntary, so it is your decision whether or not you want to take part. To help you decide whether you do or do not want to participate, it is important to understand what this research involves. This consent form will describe the study, the purpose of the research, what will happen during the study, and the possible risks and benefits. If you do decide to take part in this study, you will be asked to sign this consent form to indicate your informed consent to participate. Although, even after you sign you can choose to drop-out at any time, refuse to answer any questions, as well as request that the information collected not be used. Lack of participation will not result in any negative consequences or affect any services you and your child access in the RQHR or any future services you may access.
Who is conducting the study: The Primary Investigator is Nathalie Berard. She is a registered
psychologist who is completing her doctorate in clinical psychology at the University of Regina. This
project is a part of a Ph.D. dissertation required for partial fulfillment of the University of Regina’s
Psychology Ph.D. program.
Procedures: You and your child will come to the University of Regina to participate. First, you will be asked to read and sign an informed consent form. Then your child will be read information regarding the study and be asked to sign an assent form that says that he agrees to participate in the study. Following consent and child assent, you (the parent/guardian) will be asked to complete a short demographic form and three questionnaires asking your view of your child’s daily social skills and interactions with others as well as some executive function abilities (e.g. memory, planning,
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attention). Next, your child will be asked to complete a series of questions and tasks related to social situations as well executive function.
These include 10 tasks that involve (1) answering questions about social situations in stories and pictures; and (2) tasks related to executive functions, that involves some work on the computer. There will not be any reading, writing or mathematics. The total time is expected to be approximately 1.5 to 2 hours, however it may take less time, and breaks will be provided when necessary.
In addition, if your child has been diagnosed with an autism spectrum disorder (Autism; Asperger's; PDD NOS), then you will also be asked to provide written confirmation of the diagnosis or provide permission for Nathalie Berard (principal researcher) to conduct a file review to confirm your child's diagnosis.
Potential Risks: There are no known or anticipated risks to you, or your child, by participating in this research. Your child may become tired during the tasks and can take a break at any time. We will also provide scheduled breaks where your child will be provided snacks and water or juice.
Potential Benefits: No direct benefit can be guaranteed. However, it is anticipated that the findings from this investigation will help us better understand the factors related to social interactions and friendships. Although participants may not benefit directly from this study, it has the potential to greatly improve our understanding of social competence.
Compensation: Children will be given a Cineplex movie and popcorn gift certificate for their participation. They will receive this even if they decide to discontinue the testing. Children will also be given small rewards during some of the tasks, such as fidget toys, bouncy balls; toy cars or pencils/erasers.
Confidentiality: Any information gathered during the data collection process is strictly confidential and will be used for research purposes only by the University of Regina. All information collected will be made anonymous. The electronic file will not contain any identifying information. The consent and assent forms (containing the participants' names) will be kept separate from the participant responses in a locked cabinet. Participant names will not be put on the demographics forms, questionnaires or response sheets. All of the information that we collect will be stored on a lab computer (requiring an access code) at the University of Regina in the Behavioural Neuroscience Research lab for 5 years, after which time the electronic files will be deleted and paper material will be shredded. This research does NOT involve a psychological assessment or intervention for your child and results will not be documented in any RQHR records or files.
Right to Withdraw: As a reminder, your participation as well as your child's participation is voluntary and you and your child can answer only those questions that you are comfortable with. You may withdraw from the research project for any reason, at any time without explanation or penalty of any sort. Should you wish to withdraw, you may exit the study at any time and any information obtained will be permanently deleted from our data collection. Your right to withdraw data from the study will apply until data has been analyzed. After this it is possible that some form of research dissemination will have already occurred and it may not be possible to withdraw your data.
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Participant Consent to Participate
I have read and understand the information provided for the study as described herein.
I have had the opportunity to have my questions answered.
I agree to participate as well as permit my child to participate in this study.
I agree to provide written confirmation of my child's diagnosis or allow the principal researcher to conduct a file review to confirm my child's diagnosis.
I understand that I am not giving up my legal rights as a result of signing this consent form.
I understand that I can withdraw from this study at any time and for any reason.
I have been given a copy of this form.
I AGREE TO PARTICIPATE IN THE STUDY AND
(1) complete an information sheet and 3 questionnaires
______________________________________ ______________________________
Printed name of participant's parent Date
______________________________________ ______________________________
Signature of participant's parent Date
____________________________________ ________________________
Signature of Witness Date
_____________________________________ ________________________
Signature of principal investigator/designated Date
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representative-if applicable
(2) allow my child to participate in the study
______________________________________ ______________________________
Printed name of participant's parent Date
______________________________________ ______________________________
Signature of participant's parent Date
____________________________________ ________________________
Signature of Witness Date
_____________________________________ ________________________
Signature of principal investigator/designated Date
representative-if applicable
207
IF MY CHILD HAS A DIAGNOSIS OF AUTISM SPECTRUM DISORDER,
I ALSO AGREE TO
(4) either provide confirmation of diagnosis through a letter or report, or allow the researcher to complete a file review to confirm diagnosis through a file review.
______________________________________ ______________________________
Printed name of participant's parent Date
______________________________________ ______________________________
Signature of participant's parent Date
____________________________________ ________________________
Signature of Witness Date
_____________________________________ ________________________
Signature of principal investigator/designated Date
representative-if applicable
208
Contact Information:
If you have any questions, feedback or comments about the research study or the results of the
research study, please feel free to contact the Principal Investigator, Nathalie Berard, or her
direct supervisors, Dr. Lynn Loutzenhiser or Dr. Dennis Alfano.
Primary Investigator: Nathalie Berard Department of Psychology University of Regina 3737 Wascana Parkway Regina, SK S4S 0A2 Phone: (306) 585 4078 Email: [email protected] Research Supervisor: Lynn Loutzenhiser, Ph.D., R. D. Psych.
Professor Department of Psychology University of Regina 3737 Wascana Parkway Regina SK S4S 0A2
Phone: (306) 585-4180 Email: [email protected] Research Supervisor: Dr. Dennis Alfano, Ph.D., R. D. Psych.
Professor Department of Psychology University of Regina 3737 Wascana Parkway Regina SK S4S 0A2
Phone: (306) 585-4220 Email: [email protected]
The Research Ethics Board of the University of Regina has approved of this project. If you have
any questions or concerns about your rights as a research participant, you may contact Dr.
Bruce Plouffe, Chair of the Research Ethics Board at the Office of Research Services (AH 505) at
585-4775 or by email to [email protected].
The Research Ethics Board of the Regina Qu'Appelle Health Region has approved of this project.
If you have any questions or concerns about your rights as a research participant, you may
contact Dr. Elan Paluck, Chair of the Research Ethics Board at (306) 766-5451.
209
APPENDIX D: Visual Schedule Provided to Children
Faces
Voices
Computer
Pictures & Questions
Computer
Pictures & Questions
Speed Game
Stories
Bead Game
Listening
I Feel Questionnaire
Hungry Donkey
Puzzles & Definitions
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APPENDIX E: List of Measures
SEL Variables Measures
Facial affect recognition DANVA-2: Child faces
Linguistic affect recognition DANVA-2: Child Paralanguage
Theory of mind Strange Stories (Happe, 1994)
Empathy Bryant Empathy Continuum
Pragmatics Test Of Pragmatic Language (TOPS-2)
Social problem solving Test of Problem Solving (TOPS-3)
EF Variables
Measures
Auditory Attention NEPSY-II: Auditory attention & response set
Planning Tower of London
Visual Attention
Working Memory
Test of Executive Control (TEC)
TEC: n-back
Inhibition NEPSY-II Inhibition TEC: Go/No-Go task
Affective Decision Making Hungry Donkey Task
Parent EF Ratings Behavior Ratings Inventory of Executive Functions (BRIEF)
Social Competence
Social Skills Inventory System (SSIS)
Vineland Adaptive Bhaviour Scales 2 (VABS2): Socialisation Domain
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APPENDIX F: Demographic Questionnaire
Dear Parent,
Thank you for volunteering to participate in our study. Please complete the following personal information. This confidential information will be kept separate from the parent and teacher questionnaires and your child's responses, and will be locked in a filing cabinet at the University of Regina.
PARENT'S NAME:
CHILD'S NAME:
CHILD'S DATE OF BIRTH:
DOES YOUR CHILD HAVE A DIAGNOSIS?
WHAT IS THE DIAGNOSIS:
AGE OF CHILD AT TIME OF DIAGNOSIS:
NAME OF MEDICATION (if any):
DEPARTMENT OF PSYCHOLOGY
Regina, Saskatchewan
Canada S4S 0A2
phone: (306) 585-4221
fax: (306) 585-5429
email:
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PARENT INFORMATION
What is the highest degree or level of school you have completed?
o No high school diploma
o High school diploma or the equivalent (for example: GED)
o College graduate
o Bachelor's degree
o Master's degree
o Doctorate degree (e.g. Ph.D.; Ed.D)
o Other: Please indicate
What is your total household income?
o Less than 24 999
o 25 000 – 50 000
o 50 000 – 75 000
o 75 000 – 100 000
o 100 000 – 125 000
o 125 000 – 150 000
o More than 150 000
213
APPENDIX G: Record Forms for SEL measures:
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Answer Sheet for DANVA2 - Child Faces & Voices
1. Happy Sad Angry Fearful 17. Happy Sad Angry Fearful
2. Happy Sad Angry Fearful 18. Happy Sad Angry Fearful
3. Happy Sad Angry Fearful 19. Happy Sad Angry Fearful
4. Happy Sad Angry Fearful 20. Happy Sad Angry Fearful
5. Happy Sad Angry Fearful 21. Happy Sad Angry Fearful
6. Happy Sad Angry Fearful 22. Happy Sad Angry Fearful
7. Happy Sad Angry Fearful 23. Happy Sad Angry Fearful
8. Happy Sad Angry Fearful 24. Happy Sad Angry Fearful
9. Happy Sad Angry Fearful
10.Happy Sad Angry Fearful
11.Happy Sad Angry Fearful
12.Happy Sad Angry Fearful
13.Happy Sad Angry Fearful
14.Happy Sad Angry Fearful
15.Happy Sad Angry Fearful
16.Happy Sad Angry Fearful
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Answer Key For Child Faces 2
Item Emotion Intensity
1 Angry Low
2 Happy High
3 Happy Low
4 Fearful Low
5 Sad High
6 Sad High
7 Angry High
8 Happy High
9 Angry Low
10 Sad Low
11 Fearful Low
12 Happy Low
13 Sad High
14 Angry Low
15 Fearful Low
16 Happy High
17 Sad Low
18 Fearful High
19 Fearful High
20 Angry High
21 Sad Low
22 Fearful igh
23 Happy Low
24 Angry High
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ANSWER KEY FOR CHILD VOICES 2.
Item Emotion Intensity
1 Happy High
2 Sad Low
3 Angry Low
4 Angry High
5 Happy Low
6 Fearful Low
7 Angry High
8 Sad High
9 Fearful High
10 Happy High
11 Sad Low
12 Happy Low
13 Fearful High
14 Angry High
15 Sad High
16 Fearful Low
17 Happy High
18 Sad Low
19 Angry Low
20 Fearful High
21 Angry Low
22 Fearful Low
23 Happy Low
24 Sad High
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Bryant Empathy Questionnaire
It makes me sad to see a boy who can’t find anyone to play with
0 1 2 3
I get upset when I see a boy being hurt
0 1 2 3
Seeing a boy who is crying makes me feel like crying
0 1 2 3
I get upset when I see an animal being hurt
0 1 2 3
I really like to watch people open presents, even when I don’t get a present myself
0 1 2 3
Some songs make me so sad I feel like crying
0 1 2 3
Sometimes I cry when I watch TV
0 1 2 3
0 - not at all true
1- a little true
2- pretty much true
3- very much true
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Happe Strange Stories
Lie (Dentist)
John hates going to the dentist because every time he goes to the dentist he needs a
filling, and that hurts a lot. But John knows that when he has toothache, his mother
always takes him to the dentist. Now John has bad toothache at the moment, but when his
mother notices he is looking ill and asks him ‘‘Do you have toothache, John?’’. John says
‘‘No, Mummy’’.
1. Is it true what John says to his mother?
2. Why does John say this?
White Lie (Hat)
One day Aunt Jane came to visit Peter. Now Peter loves his aunt very much, but today
she is wearing a new hat; a new hat which Peter thinks is very ugly indeed. Peter thinks
his aunt looks silly in it, and much nicer in her old hat. But when Aunt Jane asks Peter,
‘‘How do you like my new hat?’’ Peter says, ‘‘Oh, it’s very nice’’.
1. Was it true what Peter said?
2. Why did he say it?
Sarcasm (Picnic)
Sarah and Tom are going on a picnic. It is Tom’s idea, he says it is going to be a lovely
sunny day for a picnic. But just as they are unpacking the food, it starts to rain and soon
they are both soaked to the skin. Sarah is angry. She says ‘‘Oh yes, a lovely day for a
picnic alright!’’
1. Is it true what Sarah says?
2. Why does she say this?
Joke (Haircut)
Daniel and Ian see Mrs. Thompson coming out of the hairdressers 1 day. She looks a bit
funny because the hairdresser has cut her hair much too short. Daniel says to Ian, ‘‘She
must have been in a fight with a
lawnmower!’’
1. Is it true what Daniel says?
2. Why does he say this?
Double Bluff (Bat)
Simon is a big liar. Simon’s brother Jim knows this, he knows that Simon never tells the
truth! Now yesterday Simon stole Jim’s bat and Jim knows Simon has hidden it
somewhere, though he can’t find it. He’s very upset. So he finds Simon and he says
‘‘Where is my bat? You must have hidden it either in the cupboard or under your bed,
because I’ve looked everywhere else. Where is it, in the cupboard or under your bed?’’
Simon tells him the bat is under his bed.
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1. Was it true what Simon told Jim?
2. Where will Jim look for his bat? 3. Why will Jim look there for his bat?
Happe Strange Stories Scoring Procedure
LIE(DENTIST)
Incorrect: Physical
He didn’t have toothache
Incorrect: Psychological
He’s just saying it for a joke
Correct: Partial psychological state
He thinks it’s really sore and he’s ill
He doesn’t like going to the dentist
He doesn’t like the dentist
He hates going to the dentist
Correct: Physical state
It hurts when he gets a filling
It’s sore when he goes to the dentist
He’s got toothache/He had toothache
He needs to go to the dentist
It hurts when he gets the toothache
Because they hurt
Because when you get a filling it’s sore
So that he doesn’t have to go to the dentist
Correct: Psychological state full and accurate answer
He doesn’t want to go to the dentist
He doesn’t want a filling
He doesn’t want to get a filling because it hurts
He doesn’t want to get hurt
He knows that it’ll hurt a lot
WHITE LIE (HAT)
Incorrect: Physical
It’s got a(u)nts on it
It looked nice
Psychological
He liked the hat
He wanted one
He liked the old hat
Incorrect:Psychological
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The lady asked him
It looked horrible
Partial psychological state
He didn’t want to get a row
He didn’t want to get into trouble
He didn’t like the hat
He loved his aunt
Psychological state full and accurate answer
To make his auntie feel that he likes it
He didn’t want his auntie to think that he didn’t like it
He didn’t want her to get sad/to make his auntie sad
He didn’t want to hurt her feelings
He didn’t want to upset his auntie
So his auntie wouldn’t be offended
He didn’t want to tell her he hated it
He didn’t want to be rude
He didn’t want to be nasty to her
He wanted to make his auntie feel good
He wanted to make his auntie happy
SARCASM (PICNIC)
Incorrect: Physical
Because she says it’s a lovely day but it’s not
If they’re eating sandwiches they might get wet
So that she could have a picnic
Incorrect: Psychological
She was exaggerating
She likes rain
She thinks it’s a sunny day
She wanted it to be sunny
She really wanted to go on a picnic
She wanted Tom to get wet
She was pretending
She didn’t want to hurt Tom’s feelings/make him sad
Because Tom thought she’d give him a row
Partial psychological state
She was cross
She was trying to be funny
People say stuff like that when they’re angry
She was annoyed with Tom
She’s angry
She’s copying what Tom says but it isn’t really
She’s joking
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Physical state
It was sunny and it started raining
It’s raining
Because Tom said it was going to be sunny but it wasn’t
The boy thought it was going to be a lovely day but it wasn’t
Psychological state full and accurate answer
She’s being sarcastic
JOKE (HAIRCUT)
Incorrect: Physical
Because her hair is cut like the grass
She doesn’t wear nice clothes
She went to the hairdresser and got her hair cut
Incorrect: Psychological
He doesn’t want to upset her
He thinks that’s what it looks like
He doesn’t like it, he’s just pretending
He thought it looked like she’d got it cut by a lawn mower/had a fight with a lawnmower
He’s lying
He was being sarcastic
Physical state
Because her hair is funny
Because her hair looks quite short/hair is too short
Because her hair is too short for a woman, she’s in the army!
Because her hair got cut too short
Because she doesn’t look nice
Because it looks like a lawn mower has cut her hair
Because Mrs. T looks like a boy
Partial psychological state
He didn’t like her hair
Psychological state full and accurate answer
He wants to make his friend laugh/impress his friend
He wants to make fun of her
To have a laugh/to have a joke/be funny
To slag off Mrs. T
It’s a/for a joke
To act smart
He’s being silly in front of his friend
Double Bluff (Bat)
Incorrect: Physical
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He said it was under the bed or in the cupboard
Because it’s a good hiding place
He would look under the bed because he would see the lumps on it
I’d look everywhere just in case, may be in the shed
Incorrect: Psychological
He can’t remember
He thinks in there or in the cupboard
I think it’s broken and in the bin
I think it’s in the cupboard because he wouldn’t have hid it under
his bed in case his mum found it
He wants to play ping-pong/find it
He doesn’t know Simon never tells the truth
He doesn’t know where it is
Physical state
Because Simon said so/told him
Partial psychological state
It wont be there because Simon lied
Simon never tells the truth
Because it’s the opposite from what Simon says
Because Simon is a liar/always lies
Simon never tells the truth
Psychological state full and accurate answer
Because he doesn’t believe Simon
Jim knows Simon is a liar/always lies
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Hungry Donkey Record Sheet
Response Options
1 2 3 4 5 6 7 8 9 10
DECK A
DECK B
Response Options
11 12 13 14 15 16 17 18 19 20
DECK A
DECK B
Response Options
21 22 23 24 25 26 27 28 29 30
DECK A
DECK B
Response Options
31 32 33 34 35 36 37 38 39 40
DECK A
DECK B
224
Response Options
41 42 43 44 45 46 47 48 49 50
DECK A
DECK B
Response Options
51 52 53 54 55 56 57 58 59 60
DECK A
DECK B
Response Options
61 62 63 64 65 66 67 68 69 70
DECK A
DECK B
Response Options
71 72 73 74 75 76 77 78 79 80
DECK A
DECK B
225
Response Options
81 82 83 84 85 86 87 88 89 90
DECK A
DECK B
Response Options
91 92 93 94 95 96 97 98 99 100
DECK A
DECK B
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