Facial expression recognition in the BAP
Bibliographic record
Abstract
Emotion recognition difficulties are widely reported in autism, but the “alexithymia hypothesis” proposes that such deficits are driven by co-occurring alexithymic traits rather than autism itself. We tested this hypothesis in a large sample of 556 adults spanning the broader autism phenotype, using self-reported autistic traits (Autism Quotient, AQ), alexithymia (Toronto Alexithymia Scale, TAS-20), and multiple face processing tasks. All partici?pants completed an emotional expression discrimination task; Subsample 1 (N = 231) additionally completed an expression labelling task and Raven's matrices, while Subsample 2 (N = 325) completed the Cambridge Face Memory Test. Across correlational, regression, partial correlation, and mediation analyses, autistic traits—particularly social-communicative difficulties—were the strongest and most consistent predictors of poorer emotion recognition. In contrast, the core alexithymia facets of difficulty identifying and describing feel?ings did not contribute unique variance once autistic traits were controlled. Importantly, externally oriented thinking (EOT) emerged as the only alexithymia facet with independent predictive value, consistently associated with reduced accuracy across both emotional and identity face recognition tasks. This suggests that EOT reflects a broader domain-general attentional style that deprioritises reflective engagement with socially salient informa?tion. Group-based analyses further confirmed that high autistic trait groups showed significant recognition im?pairments regardless of alexithymia levels. These findings challenge the alexithymia hypothesis and highlight autistic traits as primary drivers of emotion recognition difficulties, with EOT adding an additional, qualitatively distinct influence. The results call for revised multivariate models of face and emotion processing that integrate autistic traits, attentional orientations, cognitive ability, and gender.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".