Category learning in autism: Are some situations better than others?
Bibliographic record
Abstract
Autism is diagnosed according to atypical social-communication and repetitive behaviors. However, autistic individuals are also distinctive in the high variability of specific abilities such as learning. Having been characterized as experiencing great difficulty with learning, autistics have also been reported to learn spontaneously in exceptional ways. These contrasting accounts suggest that some situations may be better than others for learning in autism. We tested this possibility using a probabilistic category learning task with four learning situations differing either in feedback intensity or information presentation. Two learning situations compared high- versus low-intensity feedback, while two other learning situations without external feedback compared isolated sequentially presented information versus arrays of simultaneously presented information. We assessed the categorization and generalization performance of 54 autistic and 52 age-matched typical school-age children after they learned in different situations. We found that children in both groups were able to learn and generalize novel probabilistic categories in all four learning situations. However, across and within groups, autistic children were advantaged by simultaneously presented information while typical children were advantaged by high-intensity feedback when learning. These findings question some common aspects of autism interventions (e.g., frequent intense feedback, minimized simplified information), and underline the importance of improving our current understanding of how and when autistics learn optimally. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".