Looking, seeing and believing in autism: Eye movements reveal how subtle cognitive processing differences impact in the social domain
Bibliographic record
Abstract
Adults with High Functioning Autism Spectrum Disorders (ASD) viewed scenes with people in them, while having their eye movements recorded. The task was to indicate, using a button press, whether the pictures were normal, or in some way weird or odd. Oddities in the pictures were categorized as violations of either perceptual or social norms. Compared to a Typically Developed (TD) control group, the ASD participants were equally able to categorize the scenes as odd or normal, but they took longer to respond. The eye movement patterns showed that the ASD group made more fixations and revisits to the target areas in the odd scenes compared with the TD group. Additionally, when the ASD group first fixated the target areas in the scenes, they failed to initially detect the social oddities. These two findings have clear implications for processing difficulties in ASD for the social domain, where it is important to detect social cues on-line, and where there is little opportunity to go back and recheck possible cues in fast dynamic interactions. Autism Res 2016, 9: 879-887. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".