Individual differences in autism-like traits are associated with reduced goal emulation in a computational model of observational learning
Bibliographic record
Abstract
One's ability to infer the goals and intentions of others is crucial for social interactions, and such social capabilities are broadly distributed across individuals. Autism-like traits (i.e., traits associated with autism spectrum disorder (ASD)) have been associated with reduced social inference, yet the underlying computational principles and social cognitive processes are not well characterized. Here we tackle this problem by investigating inference during social learning through computational modeling in two large cross-sectional samples of adult participants from the general population (N1=943, N2=352). Autism-like traits were extracted and isolated from other associated symptom dimensions through a factor analysis of the Social Responsiveness Scale. Participants completed an observational learning task that allowed quantifying the tradeoff between two social learning strategies: imitation (repeat the observed partner’s most recent action) and emulation (infer the observed partner’s goal). Autism-like traits were associated with reduced observational learning specifically through reduced emulation (but not imitation), revealing difficulties in social goal inference. This association held even when controlling for other model parameters (e.g., decision noise, heuristics), and was specifically related to social difficulties in autism but not social anxiety. The findings, replicated in an additional sample, provide a powerfully specific mechanistic hypothesis for social learning challenges in ASD, employing a computational psychiatry approach that could be applied to other disorders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".