Typical Utilization of Gestalt Grouping Cues in Shape Perception by Persons with Autism Spectrum Disorder
Bibliographic record
Abstract
The common finding of better locally oriented perception among persons with autism spectrum disorder (ASD) is based on evidence from paradigms in which hierarchical stimuli are used to pit local and global processes against one another. However, in most cases, determining whether group differences reflect reduced global processing, enhanced local processing, or both is difficult. To provide more conclusive evidence for global perception in ASD, we examined shape formation and sensitivity to Gestalt heuristics. Children with persons with ASD and mental age matched typically developing children completed tasks in which the organization of contour segments into a shape was likely to depend on utilizing cues of closure, spatial proximity, and collinearity. In Experiment 1, search efficiency was measured, with the efficiency of the global organization indicated by the slope of the best-fitting linear reaction-time function over the number of presented items. In Experiment 2, contour integration task was administered, while Gestalt cues and the contour to background spacing ratio were manipulated independently. The findings indicated typical shape formation among the persons with ASD. Furthermore, certain interactive relations between Gestalt grouping cues that are known to govern shape formation in typically developing individuals determined the extraction of the global shape among the participants 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".