Comparison of visual sensitivity to human and object motion in autism spectrum disorder
Bibliographic record
Abstract
Successful social behavior requires the accurate detection of other people's movements. Consistent with this, typical observers demonstrate enhanced visual sensitivity to human movement relative to equally complex, nonhuman movement [e.g., Pinto & Shiffrar, 2009]. A psychophysical study investigated visual sensitivity to human motion relative to object motion in observers with autism spectrum disorder (ASD). Participants viewed point-light depictions of a moving person and, for comparison, a moving tractor and discriminated between coherent and scrambled versions of these stimuli in unmasked and masked displays. There were three groups of participants: young adults with ASD, typically developing young adults, and typically developing children. Across masking conditions, typical observers showed enhanced visual sensitivity to human movement while observers in the ASD group did not. Because the human body is an inherently social stimulus, this result is consistent with social brain theories [e.g., Pelphrey & Carter, 2008; Schultz, 2005] and suggests that the visual systems of individuals with ASD may not be tuned for the detection of socially relevant information such as the presence of another person. Reduced visual sensitivity to human movements could compromise important social behaviors including, for example, gesture comprehension.
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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) | 0.000 | 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 | 0.000 |
| 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".