Self-categorization and autism: Exploring the relationship between autistic traits and group homogeneity.
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".