Directing attention based on incidental learning in children with autism spectrum disorder.
Bibliographic record
Abstract
OBJECTIVE: Attention is a complex construct that taps into multiple mechanisms. One type of attention that is underinvestigated in autism is incidentally or implicitly guided attention. The purpose of this study is to characterize how children with autism spectrum disorder (ASD) direct spatial attention based on incidental learning. METHOD: Children with high-functioning ASD and typically developing children engaged in a visual search task. For the first half of the study, over multiple trials, the target was more often found in some locations than other locations. For the second half, the target was equally likely to appear in all locations. We measured search performance for targets located in the high-probability and low-probability locations. RESULTS: Children with ASD were able to direct spatial attention using incidentally learned information about the target's location probability. Although unaware of the experimental manipulation, children with ASD were faster and more efficient in finding a target in the high-probability locations than low-probability locations, and this bias dissipated after the target's location probability was even. The pace and magnitude of learning, as well as later adjustment to new statistics, were comparable between children with ASD and typically developing children. CONCLUSIONS: Incidentally learned attention is preserved in children with 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 | 0.003 |
| 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 | 0.000 | 0.001 |
| 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".