Understanding cognitive flexibility in emotional evaluation in autistic males and females: the social context matters
Bibliographic record
Abstract
BACKGROUND: Autistic individuals often have difficulty flexibly adjusting their behavior. However, laboratory experiments have yielded inconsistent results, potentially due to various influencing factors, which need to be examined in detail. This study aimed to investigate the hypothesis that the social content of stimuli could play a specific role in some of the flexibility challenges faced by autistic individuals. The second aim was to explore sex differences in this context. METHODS: We analyzed data from 256 adult participants (124 with autism), matched on age, gender, and sex, who performed an emotional shifting task involving unpredictable shifts between positive and negative stimuli. Additionally, the task included both social and non-social conditions. RESULTS: Our results revealed a larger switch cost in the social than in the non-social condition, and this was more pronounced in autistic than in non-autistic individuals. Furthermore, we observed that autistic females differed from autistic males in the non-social condition and from non-autistic females in the social condition. LIMITATIONS: The online nature of the study reduced the control over participant conditions. In addition, further studies are needed to investigate whether these results apply to the broader autism spectrum. CONCLUSIONS: Building on previous research demonstrating a greater switch cost in autistic than non-autistic individuals for socio-emotional stimuli, our study further extends these findings by highlighting that the social context, rather than the emotional nature of the stimuli alone, may play a significant role in the flexibility challenges faced by autistic individuals. Our findings also contribute to the literature on sex differences in autism.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".