Gender aftereffects in adults with autism spectrum disorder
Bibliographic record
Abstract
Faces convey information that is crucial for social interactions, including identity, emotion and gender. Facial aftereffects have been used to explore the face categories, the psychological relationship among categories, and whether they are coded with the same, different, or overlapping neural networks. For example, adapting to distorted (e.g., contracted facial features) male faces will shift perception of subsequently viewed male faces in the direction of the distortion (i.e., contracted male faces will be perceived as more normal looking), known as a simple aftereffect. If female faces are encoded with an overlapping neural network, then an aftereffect will also be evident when female faces are used during testing. Previous research has shown just that: in typical individuals, adapting to distorted faces from one gender will create aftereffects for faces of both genders, so male and female faces are coded with overlapping networks. In the current study we examined whether adults with autism spectrum disorder (ASD) show aftereffects when faces of one gender are used during training, and faces of the opposite gender are used during test. This would be evidence of overlapping neural representation of the two genders. Nineteen adults with ASD and 16 controls rated faces that ranged from extremely contracted to extremely expanded before and after being adapting to contracted or expanded faces from a single gender. Aftereffects were measured for each gender by calculating the change in distortion level rated most normal before and after adaptation. Consistent with previous work, typical individuals showed cross-gender aftereffects. Adults with ASD showed similar cross-gender aftereffects, suggesting that for this population, overlapping neural networks encode male and female faces . With these results we can rule out the possibility that male and female faces are represented entirely separately in ASD. Meeting abstract presented at VSS 2013
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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 | 0.000 |
| 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".