Learning with individual-interest outcomes in Autism Spectrum Disorder
Bibliographic record
Abstract
Recent work has suggested atypical neural reward responses in individuals with Autism Spectrum Disorder (ASD), particularly for social reinforcers. Less is known about neural responses to restricted interests and few studies have investigated response to rewards in a learning context. We investigated neurophysiological differences in reinforcement learning between adolescents with ASD and typically developing (TD) adolescents (27 ASD, 31 TD). FMRI was acquired during a learning task in which participants chose one of two doors to reveal an image outcome. Doors differed in their probability of showing liked and not-liked images, which were individualized for each participant. Participants chose the door paired with liked images, but not the door paired with not-liked images, significantly above chance and choice allocation did not differ between groups. Interestingly, participants with ASD made choices less consistent with their initial door preferences. We found a neural prediction-error response at the time of outcome in the ventromedial prefrontal and posterior cingulate cortices that did not differ between groups. Together, behavioural and neural findings suggest that learning with individual interest outcomes is not different between individuals with and without ASD, adding to our understanding of motivational aspects of ASD.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".