Facial identity is encoded relative to the norm in adults with autism spectrum disorder
Bibliographic record
Abstract
Face identification is thought to involve implicit evaluation of how an individual face differs from a face prototype (norm-based coding) (Rhodes et al., 2005). The characteristics of the prototype are thought to be influenced by experience with faces. People with autism spectrum disorders (ASD) appear to have both deficits and areas of preserved processing in face perception (e.g., Weigelt, Koldewyn, & Kanwisher, 2012). We examined the extent to which adults with ASD show evidence of norm-based coding of facial identity. Participants were adapted to faces that differed from the average in the physically opposite way that the target face did (an anti-identity), and then asked to categorize the average face. The norm-based model would predict that the average face would be perceived as more like the target identity; a phenomenon known as a face identity aftereffect. The adapting faces were either very different from the average face (80% anti-identity strength) or close to the average face (40% anti-identity strength). The norm-based coding model predicts that more extreme adapting faces should produce a larger aftereffect than less extreme adapting faces. Both the ASD and control groups displayed larger identity aftereffects for more extreme adapting faces compared to weaker adapting faces, evidence that both groups use norm-based coding. There was no group difference in aftereffect size. This pattern is in contrast with previous findings of abnormally small identity aftereffects in children with ASD (Pellicano et al., 2007). The current results provide the first evidence of intact norm-based coding of facial identity in high-functioning adults with ASD, and suggest that deficits in face processing observed in this population may arise from another source. Meeting abstract presented at VSS 2014
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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 | 0.004 |
| 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".