Atypical Face Perception in Autism: A Point of View?
Bibliographic record
Abstract
Face perception is the most commonly used visual metric of social perception in autism. However, when found to be atypical, the origin of face perception differences in autism is contentious. One hypothesis proposes that a locally oriented visual analysis, characteristic of individuals with autism, ultimately affects performance on face tasks where a global analysis is optimal. The objective of this study was to evaluate this hypothesis by assessing face identity discrimination with synthetic faces presented with and without changes in viewpoint, with the former condition minimizing access to local face attributes used for identity discrimination. Twenty-eight individuals with autism and 30 neurotypical participants performed a face identity discrimination task. Stimuli were synthetic faces extracted from traditional face photographs in both front and 20° side viewpoints, digitized from 37 points to provide a continuous measure of facial geometry. Face identity discrimination thresholds were obtained using a two-alternative, temporal forced choice match-to-sample paradigm. Analyses revealed an interaction between group and condition, with group differences found only for the viewpoint change condition, where performance in the autism group was decreased compared to that of neurotypical participants. The selective decrease in performance for the viewpoint change condition suggests that face identity discrimination in autism is more difficult when access to local cues is minimized, and/or when dependence on integrative analysis is increased. These results lend support to a perceptual contribution of atypical face perception in autism.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".