Explore-exploit behaviors predict broad autism social phenotypes in general population
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is most often defined by social communication challenges and behavioral rigidity, but executive function deficits have long been considered potential contributors to autism-related impairments across these domains. Autism is a spectrum, with a broad range of phenotypes presenting below the diagnostic threshold, raising the possibility that variability in executive function may contribute to individual differences in autistic traits in the general population. Value based decision making tasks access aspects of executive function, and critically are amenable to computational approaches to dissect the latent variables that most contribute to individual differences in cognition. We capitalize on this approach to uncover the relationship between autistic traits as measured by the Broad Autism Phenotype Questionnaire (BAPQ) and explore-exploit balance in a three-armed restless bandit decision making task in a large (1001 participants) sample. We find that the BAPQ aloof subscale, which primarily describes social behavior related phenotypes, most strongly explains changes in choice behaviors, including sensitivity to outcomes, changes in choice flexibility, and level of exploration as inferred from a Hidden Markov Model. Canonical correlation analysis reveals that the strongest loading for these non-social reward related measures are in fact socially coded items. These findings suggest that different aspects of executive function challenges may be related to social and nonsocial autism-related behaviors in the general population, and that social components of behavior produce measurable differences in nonsocial tasks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".