Saccade Latency Provides Evidence for Reduced Face Inversion Effects With Higher Autism Traits
Bibliographic record
Abstract
Individuals on the autism spectrum are reported to show impairments in the processing of social information, including aspects of eye-movements towards faces. Abnormalities in basic-level visual processing are also reported. In the current study, we sought to determine if the latency of saccades made towards social targets (faces) in a natural scene as opposed to inanimate targets (cars) would be related to sub-clinical autism traits (ATs) in individuals drawn from a neurotypical population. The effect of stimulus inversion was also examined given that difficulties with processing inverted faces are thought to be a function of face expertise. No group differences in saccadic latency were established for face or car targets, regardless of image orientation. However, as expected, we found that individuals with higher autism-like traits did not demonstrate a saccadic face inversion effect, but those with lower autism-like traits did. Neither group showed a car inversion effect. Thus, these results suggest that neurotypical individuals with high autism-like traits also show anomalies in detecting and orienting to faces. In particular, the reduced saccadic face inversion effect established in these participants with high ATs suggests that speed of visual processing and orienting towards faces may be associated with the social difficulties found across the broader autism spectrum.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".