Faster social attention disengagement in individuals with higher autism traits
Bibliographic record
Abstract
INTRODUCTION: Atypical visual and social attention has often been associated with clinically diagnosed autism spectrum disorder (ASD), and with the broader autism phenotype. Atypical social attention is of particular research interest given the importance of facial expressions for social communication, with faces tending to attract and hold attention in neurotypical individuals. In autism, this is not necessarily so, where there is debate about the temporal differences in the ability to disengage attention from a face. METHOD: Thus, we have used eye-tracking to record saccadic latencies as a measure of time to disengage attention from a central task-irrelevant face before orienting to a newly presented peripheral nonsocial target during a gap-overlap task. Neurotypical participants with higher or lower autism-like traits (AT) completed the task that included central stimuli with varied expressions of facial emotion as well as an inverted face. RESULTS: High AT participants demonstrated faster saccadic responses to detect the nonsocial target than low AT participants when disengaging attention from a face. Furthermore, faster saccadic responses were recorded when comparing disengagement from upright to inverted faces in low AT but not in high AT participants. CONCLUSIONS: Together, these results extend findings of atypical social attention disengagement in autism and highlight how differences in attention to faces in the broader autism phenotype can lead to apparently superior task performance under certain conditions. Specifically, autism traits were linked to faster attention orienting to a nonsocial target due to the reduced attentional hold of the task irrelevant face stimuli. The absence of an inversion effect in high AT participants also reinforces the suggestion that they process upright or inverted faces similarly, unlike low AT participants for whom inverted faces are thought to be less socially engaging, thus allowing faster disengagement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".