Strategy for visual scanning of faces varies with the degree of Asperger Syndrome traits
Bibliographic record
Abstract
People with Asperger syndrome (AS) have difficulty identifying complex emotions (e.g., ‘wary’) that involve theory of mind but can identify basic emotions (e.g., ‘happy’) (Baron-Cohen et al., 2001 J.Child Psychol. Psychiat. 42: 241). They look less at the eye region of faces than controls do when discriminating basic emotions but not when viewing faces passively (Pelphrey et al., 2002 J. Autism Dev. Dis. 32:249). Does scan path strategy vary with AS severity? Is there a difference between how people with and without AS scan faces while identifying complex versus simple emotions? Eye movements were measured using an Eyelink 1000 while people with AS and controls viewed faces showing simple or complex emotions. Severity of AS was assessed using the Autism-Spectrum Quotient questionnaire (AQ: Baron-Cohen et al., 2001 J. Autism Dev. Dis. 31: 5). Participants viewed a fixation cross followed by a face which was surrounded, after 5s, by four words from which they selected the emotional expression. Scan paths were analysed in terms of the goal and latency of first saccade to the face, and the dwell times in different face regions during the face-viewing period. The frequency of people looking first at the nose, the dwell time in the nose region, and the dwell time on features in the lower part of the face in general, correlated with AQ. The frequency of looking first at the eyes, the dwell time in the eye regions, and dwell time on the upper part of the face were inversely correlated with AQ. There was no difference in scan paths when identifying simple vs. complex emotions. These data support the view that there is a continuum of AS tendencies extending into the so-called normal population that is reflected in scan path strategy.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".