Pose dependent face recognition in autism spectrum disorder
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in social communication and interaction, as well as restricted and repetitive behaviour and thoughts. In addition, challenges in face perception are commonly observed in ASD, but the origin of such challenges remains under debate. Recently, Kamensek et al., (2021) revealed that daily visual exposure to faces (the face-diet) for adults with ASD is reduced and atypical, suggesting an experiential contribution to face recognition challenges in this population. Specifically, the face-diet of those with ASD is biased towards faces in profile pose, regardless of familiarity, compared to neuro-typical adults. We examined whether an exposure pattern favouring profile pose may lead to enhanced encoding and recognition of this stimulus type. We tested adults with and without ASD in a 3-alternative forced-choice face recognition paradigm at two poses: frontal and profile. Preliminary results for 13 adults with ASD and 5 non-autistic controls reveals a main effect of group (p = .006), with greater accuracy for the controls (M=90.56%) than the ASD group (M=76%); a main effect of pose (p < .001) with better accuracy for frontal pose (M=87.96%) than profile (M=72.12%), but no interaction (p = .144). These results indicate better overall face recognition in the non-autistic control group, and contrary to our hypothesis, no profile pose advantage for the ASD group. Despite proportionally greater exposure to profile faces, overall limited exposure duration to faces in autistic individuals may preclude accumulation of experience necessary to yield an advantage in this pose. Furthermore, re-examination of the face-diet data revealed that profile faces were predominantly seen at far distances, potentially limiting development of expertise at this pose. Finally, the profile pose is uncommon in social interaction (Oruc et al., 2019) and may signal a lack of engagement, further impeding involvement of expert face processes
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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 | 0.000 | 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".