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The Integrated Self-Categorization Model of Autism

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Daniel P. Skorich
Daniel P. Skorich
Daniel P. Skorich
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S. Alexander Haslam at University of Queensland
S. Alexander Haslam
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Abstract

In this article, we formally present the Integrated Self-Categorization model of Autism (ISCA). This model brings together the cognitive-perceptual and social-communication features of autism under a single explanatory framework. Specifically, ISCA proposes that the social-communication features that are related to theory of mind dysfunction emerge from the cognitive-perceptual features related to enhanced perceptual functioning and weak central coherence, and proposes that they are linked by dysfunction in the self-categorization process. We present the assumptions on which the model is based, and from these, we derive a set of precise, testable hypotheses, including a set of novel hypotheses that do not emerge from any existing models of autism. We then provide evidence that supports the model, derived from a number of direct tests of the hypotheses that it generates. We conclude by discussing the implications of the model for understanding autism and for intervention to improve the lives of autistic people, as well as future directions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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The Integrated Self-Categorization Model of Autism
Daniel P. Skorich
1, 2
and S. Alexander Haslam
2
1
Research School of Psychology, The Australian National University
2
School of Psychology, University of Queensland
In this article, we formally present the Integrated Self-Categorization model of Autism (ISCA). This model
brings together the cognitive–perceptual and social–communication features of autism under a single
explanatory framework. Specifically, ISCA proposes that the social–communication features that are related
to theory of mind dysfunction emerge from the cognitive–perceptual features related to enhanced
perceptual functioning and weak central coherence, and proposes that they are linked by dysfunction in
the self-categorization process. We present the assumptions on which the model is based, and from
these, we derive a set of precise, testable hypotheses, including a set of novel hypotheses that do not
emerge from any existing models of autism. We then provide evidence that supports the model, derived
from a number of direct tests of the hypotheses that it generates. We conclude by discussing the
implications of the model for understanding autism and for intervention to improve the lives of autistic
people, as well as future directions.
Keywords: autism, enhanced perceptual functioning, self-categorization, social identity, theory of mind
Autism spectrum disorder (hereafter referred to as “autism”)isa
pervasive developmental disorder characterized by a wide range of
seemingly disparate cognitive–perceptual and social–communication
features (American Psychiatric Association [APA], 2013). A con-
sensus is emerging in the literature that there is no single explanation
for all the features of the condition at any level of analysis (Happé
et al., 2006). Indeed, autism is increasingly coming to be seen as a
“fractionated”disorder, with different proposed aetiologies for each
of a number of distinct clusters of features (Brunsdon & Happé,
2014). The autism literature is therefore replete with models that
attempt to explain each of these clusters separately, with only a few
models seeking to integrate the clusters under a single explanatory
framework (e.g., Markram & Markram, 2010;Stevenson et al., 2014;
Van de Cruys et al., 2014). In this article, we present a new cognitive
model of autism, the Integrated Self-Categorization model of Autism
(ISCA), which provides a unified and integrated account of the
disorder’s cognitive–perceptual and social–communication features.
Specifically, ISCA proposes that the social–communication
features—related to theory of mind (ToM) dysfunction (Baron-
Cohen, 1997,2005;Baron-Cohen et al., 1985)—emerge from the
cognitive–perceptual features—related to “enhanced perceptual func-
tioning”(EPF; Mottron et al., 2006)and“weak central coherence”
(WCC; Frith & Happé,1994;Happé& Frith, 2006)—via dysfunction
in the self-categorization process.
With a view to being clear and precise in our presentation of the
model, our discussion is structured intoseven parts. First,we provide a
general overview of the model, intended to frame subsequent sections
in which we delve more deeply into its precise details. Second, we
present the set of assumptions on which the model is based, and for
each, we present supporting empirical evidence. Third, from these
assumptions, we then derive a set of precise, testable hypotheses,
including (a) a set that emerges directly from the assumptions taken
together (derived hypotheses) and (b) a second set that emerges if we
assume the model to be true (emergent hypotheses). Fourth, we
present empirical tests of both the derived and emergent hypotheses.
We follow this, fifth, with a discussion of the implications of the
model for understanding autism and for clinical intervention with
autistic
1
individuals, before, sixth, discussing how ISCA relates to
biological and developmental models of autism. Finally, we conclude
with suggestions for future autism research.
Overview of ISCA
Autism is a pervasive developmental disorder, diagnosed on the
basis of: (a) difficulties in social communication and social interaction,
including difficulties in understanding, forming and maintaining re-
lationships, and differences in nonverbal communication, body lan-
guage, and eye contact relative to Non-autistic people; (b) a restricted,
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This article was published Online First July 18, 2022.
S. Alexander Haslam https://orcid.org/0000-0001-9523-7921
The theoretical model advanced in this article has been presented on
numerous occasions between 2015 and 2021, including: as a Cecil Gibb
Seminar at the Research School of Psychology, the Australian National
University; at the Centre for Research in Social Psychology at the University
of Queensland (UQ); as a School of Psychology Seminar at UQ; at the
Annual Conference of the Society of Australasian Social Psychologists; at
the International Conference on Social Identity and Health; as a Social
Behavior Colloquium in the School of Psychology, Simon Fraser University;
and to the Mental Health Postgraduate Research Group at UQ. S. Alexander
Haslam received funding from Grant FLFL110100199 from the Australian
Research Council and Social Interactions Identity and Well-Being Program
from the Canadian Institute for Advanced Research. The authors have no
conflicts of interest to disclose.
Correspondence concerning this article should be addressed to Daniel P.
Skorich, Research School of Psychology, The Australian National
University, Building 39, Science Road, Acton, ACT 2601, Australia.
Email: Daniel.Skorich@anu.edu.au
1
The terms “autistic”or “autistic people/autistic individuals”are preferred
by many autistic people themselves (Kenny et al., 2016), so these are terms
that we use in this article.
Psychological Review
© 2022 American Psychological Association 2022, Vol. 129, No. 6, 1373–1393
ISSN: 0033-295X https://doi.org/10.1037/rev0000385
1373
... The social norms, institutional structures, and cultural practices that are part of common knowledge stimulate the construction of a collective identity, were people are members of a certain group with a certain identity (Skorich & Haslam, 2022;Stheynberg et al., 2020). This sense of collective identity is constituted by both the construction of collective attention with others and the common knowledge that is constructed in this process. ...
... It is both the outcome of the process and the process itself. Having a collective identity facilitates the construction of collective attention, since feelings of connectedness between people stimulate co-attending to the same objects, which subsequently facilitates the construction of common knowledge (Skorich & Haslam, 2022). ...
... Crucial for the construction of collective attention is a fourth concept, the concept of self-categorization (Shteynberg et al., 2020). Self-categorization is described as 'a process that groups together social stimuliother individuals, but also other representations of the self across situations and occasions (Skorich & Mavor, 2013) in ways that result in a holistic, higher order, emergent understanding of the self and its relations with others' (Skorich & Haslam, 2022, p. 1376. It is the mechanism through which individuals use social categories such as age, gender or occupation, to make sense of social cues such as, jargon, traits or behaviors (Skorich & Mavor, 2013). ...
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... The Integrated Self-Categorization model of Autism (ISCA;Bertschy et al., 2019;Skorich & Haslam, 2021) argues that the theory of mind differences seen in autism arises from Enhanced Perceptual Functioning/Weak Central Coherence, via a dysfunctional self-categorization mechanism. The ISCA model also makes the novel prediction that phenomena that arise from self-categorization should also be affected in autistic people. ...
... The Integrated Self-Categorization Model of Autism (Bertschy et al., 2019;Skorich & Haslam, 2021;Skorich et al., 2016;2017) Note. EPF = Enhanced Perceptual Functioning; WCC = Weak Central Coherence. ...
... Together, the results of the studies reported above provide some convergent evidence that supports the ISCA model (Bertschy et al., 2019;Skorich & Haslam, 2021;Skorich et al., 2016Skorich et al., , 2017. In particular, this research shows that typical group homogeneity effects that flow from self-categorization, are moderated by autistic traits. ...
Self-Categorization and Autism: Exploring the Relationship Between Autistic Traits and Group Homogeneity
Article
Full-text available
  • Jun 2021
The Integrated Self-Categorization model of Autism (ISCA; Bertschy et al., 2019; Skorich & Haslam, 2021) argues that the theory of mind differences seen in autism arises from Enhanced Perceptual Functioning/Weak Central Coherence, via a dysfunctional self-categorization mechanism. The ISCA model also makes the novel prediction that phenomena that arise from self-categorization should also be affected in autistic people. In this article, we report three studies exploring this prediction in the context of one such phenomenon: Group homogeneity. We first measure participants' autistic traits, then ask them to make homogeneity judgments of their ingroup alone or their outgroup alone (in Study 1, and in the Alone conditions of Studies 2a and 2b); or of their ingroup in comparison to their outgroup or their outgroup in comparison to their ingroup (in the Compare conditions of Studies 2a and 2b). As predicted, we find that: the degree of autistic traits negatively predicts ratings of group homogeneity; this relationship is mediated by social identification/self-categorization; and typical comparison-related homogeneity effects are strengthened at higher relative to lower levels of autistic traits. These studies provide convergent evidence for the ISCA model and suggest important avenues for well-being and social skills interventions for autistic people. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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... Along related lines, socially-minded intelligence may also be increased by increasing an individual's ability to build and harness social resources. This might be achieved by increasing social [155], emotional [156], or cultural [157] intelligence; or by developing relevant cognitive skills such as theory of mind [158] and social selfcategorization [159]. ...
... Measuring a person's socially-minded intelligence requires new methods that capture not only their abilities as an individual, but also their social environment. As a starting point, a target person's ability to perceive, think, and act with others can be operationalized using existing constructs such as emotional intelligence [169], agreeableness [138], and selfcategorization ability [159]. These abilities could be combined to form a composite measure of socially-minded ability, representing the target person's individual-difference traits and skills that are important for socially-minded intelligence. ...
Socially-Minded Intelligence: How Individuals, Groups, and AI Systems Can Make Each-Other Smarter (or Not)
Preprint
Full-text available
  • Sep 2024
A core part of human intelligence is the ability to work flexibly with others to achieve both individual and collective goals. The incorporation of artificial agents into human spaces is making increasing demands on artificial intelligence (AI) to demonstrate and facilitate this ability. However, this kind of flexibility is not well understood because existing approaches to intelligence typically focus either on the individual or the collective level of analysis. At the individual level, intelligence is seen as an individual-difference trait that exists independently of the social environment. At the collective level intelligence is conceptualized as a property of groups, but not in a way that can be used to understand how groups can make group members smarter or how group members acting as individuals might make the group itself more intelligent. In the present paper we argue that by focusing either on individual or collective intelligence without considering their interaction, existing conceptualizations of intelligence limit the potential of people and machines. To address this impasse, we identify and explore a new kind of intelligence - socially-minded intelligence - that can be applied to both individuals (in a social context) and collectives (of individual minds). From a socially-minded intelligence perspective, the potential intelligence of individuals is unlocked in groups, while the potential intelligence of groups is maximized by the flexible, context-sensitive commitment of individual group members. We propose ways in which socially-minded intelligence might be measured and cultivated within people, as well as how it might be modelled in AI systems. Finally, we discuss ways in which socially-minded intelligence might be used to improve human-AI teaming.
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... While it is unclear why these differences exist, stigma, other people's responses to the diagnosis and knowledge about autism, may all play a role (Cooper et al., 2017). It has also been suggested that autistic people may be less inclined to self-categorize (Bertschy et al., 2020;Skorich & Haslam, 2022). This could affect the way social identity processes occur in autistic individuals and may impact their willingness to disclose in a variety of settings. ...
Disclosing an autism diagnosis: A social identity approach
Article
Full-text available
  • Aug 2023
  • AUTISM RES
Autistic people face a difficult dilemma around whether or not to disclose their diagnosis because autism is a stigmatized social group. The central aim of this study was to examine if a social identity approach could be useful in understanding the factors that predict the likelihood of autistic adults disclosing their autism diagnosis in social settings, in the workplace, in educational settings and in the family. The social identity approach predicts that autistic people may cope with this dilemma by using an individualistic strategy to distance from their autistic social identity. Alternatively, they may embrace their autistic social identity and use a collective strategy to resist stigma and advocate for autism. We present a survey based cross-sectional study (n = 175) with autistic adults living in Ireland. Participants completed a series of measures; autism social identification, stigma consciousness, and individualistic and collective strategy use to assess disclosing in the four settings. The overall models in each of the four regressions were significant. Autism social identification positively predicted disclosure in social, workplace and educational settings, while stigma consciousness negatively predicted disclosure in the family and in the workplace. Interestingly, over and above these predictors individualistic strategy use negatively predicted disclosure in each of the four settings, while collective strategy positively predicted disclosure in social, educational and family settings. Our novel social identity approach was useful for explaining autistic adult's strategies to cope with the complex disclosure dilemma. Strengths, limitations, and directions for future research are discussed.
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Functional magnetic resonance imaging of autism spectrum disorders
Article
Full-text available
  • Apr 2022
This review presents an overview of functional magnetic resonance imaging findings in autism spectrum disorders (ASDs), Although there is considerable heterogeneity with respect to results across studies, common themes have emerged, including: (i) hypoactivation in nodes of the “social brain” during social processing tasks, including regions within the prefrontal cortex, the posterior superior temporal sulcus, the amygdala, and the fusiform gyrus; (ii) aberrant frontostriatal activation during cognitive control tasks relevant to restricted and repetitive behaviors and interests, including regions within the dorsal prefrontal cortex and the basal ganglia; (iii) differential lateralization and activation of language processing and production regions during communication tasks; (iv) anomalous mesolimbic responses to social and nonsocial rewards; (v) task-based long-range functional hypoconnectivity and short-range hyper-connectivity; and (vi) decreased anterior-posterior functional connectivity during resting states. These findings provide mechanistic accounts of ASD pathophysiology and suggest directions for future research aimed at elucidating etiologic models and developing rationally derived and targeted treatments.
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Groups 4 Health versus cognitive–behavioural therapy for depression and loneliness in young people: randomised phase 3 non-inferiority trial with 12-month follow-up
Article
Full-text available
  • Sep 2021
Background Depression treatments are typically less effective for young people than for adults. However, treatments rarely target loneliness, which is a key risk factor in the onset, maintenance and development of depression. Aims This study evaluated the efficacy of a novel loneliness intervention, Groups 4 Health (G4H), relative to the best-practice treatment of cognitive–behavioural therapy (CBT) in reducing loneliness and depression over a 12-month period (Australian New Zealand Clinical Trial Registry: ACTRN12618000440224). Method The study was a phase 3 randomised non-inferiority trial comparing G 4 H with dose-controlled group CBT. Participants were 174 people aged 15–25 years experiencing loneliness and clinically significant symptoms of depression, who were not in receipt of adjunct treatment. Participants were recruited from mental health services in Southeast Queensland, Australia. Randomisation was conducted using computer software. Follow-up assessments and statistical analyses were masked to allocation. Both interventions consisted of five 75 min group-based psychotherapy sessions. The primary outcomes were depression and loneliness, with a non-inferiority margin of 2.20 for depression. Results The trial enrolled 174 participants between 24 April 2018 and 25 May 2019, with 84 in the G 4 H condition and 90 in the CBT condition. All randomised participants were included in the intention-to-treat analyses ( n = 174). The pre–post effect sizes for depression were d G4H = −0.71 and d CBT = −0.91. For loneliness, they were d G4H = −1.07 and d CBT = −0.89. At 12-month follow-up, the absolute difference between groups on depression was 1.176 (95% CI −1.94 to 4.29) and on loneliness it was −0.679 (95% CI −1.43 to 0.07). No adverse effects were observed. Conclusions G4H was non-inferior to CBT for depression and showed a slight advantage over CBT for loneliness that emerged after treatment completion.
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Groups 4 Health protects against unanticipated threats to mental health: Evaluating two interventions during COVID-19 lockdown among young people with a history of depression and loneliness
Article
Full-text available
  • Aug 2021
  • J AFFECT DISORDERS
Background Decades of research indicate that when social connectedness is threatened, mental health is at risk. However, extant interventions to tackle loneliness have had only modest success, and none have been trialled under conditions of such threat. Method 174 young people with depression and loneliness were randomised to one of two evidence-based treatments: cognitive behaviour therapy (CBT) or Groups 4 Health (G4H), an intervention designed to increase social group belonging. Depression, loneliness, and well-being outcomes were evaluated at one-year follow-up; COVID-19 lockdown restrictions were imposed partway through follow-up assessments. This provided a quasi-experimental test of the utility of each intervention in the presence (lockdown group) and absence (control group) of a threat to social connectedness. Results At one-year follow-up, participants in lockdown reported significantly poorer wellbeing than controls who completed follow-up before lockdown, t(152)=2.41, p=.017. Although both CBT and G4H led to symptom improvement, the benefits of G4H were more robust following an unanticipated threat to social connectedness for depression (χ²(16)=31.35, p=.001), loneliness (χ²(8)=21.622, p=.006), and wellbeing (χ²(8)=22.938, p=.003). Limitations Because the COVID-19 lockdown was unanticipated, this analysis represents an opportunistic use of available data. As a result, we could not measure the specific impact of restrictions on participants, such as reduced income, degree of isolation, or health-related anxieties. Conclusions G4H delivered one year prior to COVID-19 lockdown offered greater protection than CBT against relapse of loneliness and depression symptoms. Implications are discussed with a focus on how these benefits might be extended to other life stressors and transitions.
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Self-Categorization and Autism: Exploring the Relationship Between Autistic Traits and Group Homogeneity
Article
Full-text available
  • Jun 2021
The Integrated Self-Categorization model of Autism (ISCA; Bertschy et al., 2019; Skorich & Haslam, 2021) argues that the theory of mind differences seen in autism arises from Enhanced Perceptual Functioning/Weak Central Coherence, via a dysfunctional self-categorization mechanism. The ISCA model also makes the novel prediction that phenomena that arise from self-categorization should also be affected in autistic people. In this article, we report three studies exploring this prediction in the context of one such phenomenon: Group homogeneity. We first measure participants' autistic traits, then ask them to make homogeneity judgments of their ingroup alone or their outgroup alone (in Study 1, and in the Alone conditions of Studies 2a and 2b); or of their ingroup in comparison to their outgroup or their outgroup in comparison to their ingroup (in the Compare conditions of Studies 2a and 2b). As predicted, we find that: the degree of autistic traits negatively predicts ratings of group homogeneity; this relationship is mediated by social identification/self-categorization; and typical comparison-related homogeneity effects are strengthened at higher relative to lower levels of autistic traits. These studies provide convergent evidence for the ISCA model and suggest important avenues for well-being and social skills interventions for autistic people. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Response to Mottron
Article
Full-text available
  • Jun 2021
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A radical change in our autism research strategy is needed: Back to prototypes
Article
Full-text available
  • Jun 2021
The evolution of autism diagnosis, from its discovery to its current delineation using standardized instruments, has been paralleled by a steady increase in its prevalence and heterogeneity. In clinical settings, the diagnosis of autism is now too vague to specify the type of support required by the concerned individuals. In research, the inclusion of individuals categorically defined by over-inclusive, polythetic criteria in autism cohorts results in a population whose heterogeneity runs contrary to the advancement of scientific progress. Investigating individuals sharing only a trivial resemblance produces a large-scale type-2 error (not finding differences between autistic and dominant population) rather than detecting mechanistic differences to explain their phenotypic divergences. The dimensional approach of autism proposed to cure the disease of its categorical diagnosis is plagued by the arbitrariness of the dimensions under study. Here, we argue that an emphasis on the reliability rather than specificity of diagnostic criteria and the misuse of diagnostic instruments, which ignore the recognition of a prototype, leads to confound autism with the entire range of neurodevelopmental conditions and personality variants. We propose centering research on cohorts in which individuals are selected based on their expert judged prototypicality to advance the theoretical and practical pervasive issues pertaining to autism diagnostic thresholds. Reversing the current research strategy by giving more weight to specificity than reliability should increase our ability to discover the mechanisms of autism. Lay Summary Scientific research into the causes of autism and its mechanisms is carried out on large cohorts of people who are less and less different from the general population. This historical trend may explain the poor harvest of results obtained. Services and intervention are provided according to a diagnosis that now encompasses extremely different individuals. Last, we accept as a biological reality the constant increase over the years in the proportion of autistic people among the general population. These drifts are made possible by the attribution of a diagnosis of autism to people who meet vague criteria, rather than to people who experienced clinicians recognize as autistic. We propose to change our research strategy by focusing on the study of the latter, fewer in number, but more representative of the “prototype” of autism. To do this, it is necessary to clearly distinguish the population on which the research is carried out from that to which we provide support. People must receive services according to their needs, and not according to the clarity of their diagnosis.
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"Let's get physical or social": The role of physical activity versus social group memberships in predicting depression and anxiety over time
Article
  • Mar 2022
Background: Physical activity is often promoted as a way to prevent and combat anxiety and depression in adolescents. However, very little research has sought to establish whether the benefits of exercise arise from the excercise itself or from the social context in which it takes place. We explore the hypothesis that it is not physical activity on its own, but rather adolescents' engagement in group life (as part of a sports group or otherwise), that accounts for positive mental health effects associated with physical activity. Methods and results: We conducted a longitudinal study that tracked 558 high-school boys and found that anxiety and depression over time was not predicted by (a) T1 physical fitness as determined by 7 speed and agility tests, or (b) engaging in multiple sports as co-curricular activies at T1. In contrast, multiple group memberships - irrespective of the activity - predicted reduced depression and anxiety over time, particularly when these were groups that adolescents identified with and experienced as compatible with each other. Limitations: Limitations relate to (a) physical fitness only being measured at T1, (b) the absence of a measure of frequency and duration of physical activity, and (c) the homogeneity of the sample. Conclusions: We conclude that group memberships and the social identities that adolescents derive from these groups (including, but not restricted to, those involving sport) function as a psychological resource to reduce anxiety and depression over time.
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