Face perception develops similarly across viewpoint in children and adolescents with and without autism spectrum disorder
Bibliographic record
Abstract
Atypical face perception has been associated with the socio-communicative difficulties that characterize autism spectrum disorder (ASD). Growing evidence, however, suggests that a widespread impairment in face perception is not as common as once thought. One important issue arising with the interpretation of this literature is the relationship between face processing and a more general perceptual tendency to focus on local rather than global information. Previous work has demonstrated that when discriminating faces presented from the same view, older adolescents and adults with ASD perform similarly to typically developing individuals. When faces are presented from different views, however, they perform more poorly-specifically, when access to local cues is minimized. In this study, we assessed the cross-sectional development of face identity discrimination across viewpoint using same- and different-view conditions in children and adolescents with and without ASD. Contrary to the findings in adults, our results revealed that all participants experienced greater difficulty identifying faces from different views than from same views, and demonstrated similar age-expected improvements in performance across tasks. These results suggest that differences in face discrimination across views may only emerge beyond the age of 15 years in 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".