"Don't Look": Faces with Eyes Open Influence Visual Behavior in Neurotypicals but not in Individuals with High-Functioning Autism
Bibliographic record
Abstract
In tasks examining gaze orientation when viewing faces, open eyes are fixated upon longer than other face regions. High-functioning individuals with Autism (ASD) show inattention to faces and impaired orienting to eyes. In this study, we examined whether impaired orienting in ASD is from avoidance of the eye region or a weaker orienting bias towards open eyes. 13 ASD subjects and 13 healthy controls (HC) group-matched for age and gender viewed Facegen faces while eye-gaze was tracked. Half of the images depicted open eyes (EO) and half depicted closed eyes (EC). Images were presented in three blocks with instructions to 1) view freely 2) avoid the eyes or 3) avoid the mouth. Only controls showed differences in visual behavior for open versus closed eyes. In the "Free-view" condition, within-group comparison showed controls increased their gaze to mouth (p<0.05) and decreased gaze to eyes (p<0.05) for EC over EO, whereas for ASD, gaze to the ROIs stayed the same when viewing both stimulus types. In the "No Eyes" condition, a within-group comparison between stimulus types again showed the ASD group's gaze to ROIs did not change for EO versus EC, whereas controls spent more time on non-face screen regions with EO versus EC (p<0.05), perhaps a strategy to counteract the saliency of open eyes. The ASD group spent similar proportions of time on the eyes as controls in all conditions, indicating there is indeed a tendency to orient to the eye region in the ASD group. For controls however, eyes open versus closed elicited significant changes in gaze behavior, while there was little influence of eyes open versus closed on the ASD group's visual behavior in this sample set, suggesting ASD shows a relative indifference to open eyes versus closed rather than an overall avoidance of eye regions. Meeting abstract presented at VSS 2014
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".