Do Individuals with and without Autism Spectrum Disorder Scan Faces Differently? A New Multi‐Method Look at an Existing Controversy
Bibliographic record
Abstract
Individuals with autism spectrum disorder (ASD) are known to process faces atypically. However, there has been considerable controversy regarding whether ASD individuals also scan faces differently from typical adults. Here we compared ASD individuals' face-scanning patterns with those of typically developing (TD) controls and intellectually disabled (ID) but non-ASD individuals with the use of an eye tracker and multiple approaches to analyze eye-tracking data. First, we analyzed the eye movement data with a traditional approach, measuring fixation duration on each area of interest within the face. We found that compared with TD and ID individuals, ASD individuals looked significantly shorter at the right eye. Second, we used a data-driven method that analyzes fixations on each pixel of the face stimulus and found that individuals with ASD looked more at the central nasal area than TD and ID individuals. Third, we used a novel saccade path analysis that measures frequencies of saccades between major face areas. We found that ASD individuals scanned less often between core facial features than TD individuals but did not differ from ID individuals. Findings from the multi-method approaches show that individuals with ASD appear not to have a pervasive ASD-specific atypicality in visual attention toward the face. The ASD-specific atypical face-scanning patterns were shown to be limited to fixations on the eyes and nose.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".