Recognition of facial expressions in autism: Effects of face masks and alexithymia
Bibliographic record
Abstract
It is often assumed that the recognition of facial expressions is impaired in autism. However, recent evidence suggests that reports of expression recognition difficulties in autistic participants may be attributable to co-occurring alexithymia—a trait associated with difficulties interpreting interoceptive and emotional states—not autism per se. Due to problems fixating on the eye-region, autistic individuals may be more reliant on information from the mouth region when judging facial expressions. As such, it may be easier to detect expression recognition deficits attributable to autism, not alexithymia, when participants are forced to base expression judgements on the eye-region alone. To test this possibility, we compared the ability of autistic participants (with and without high levels of alexithymia) and non-autistic controls to categorise facial expressions (a) when the whole face was visible, and (b) when the lower portion of the face was covered with a surgical mask. High-alexithymic autistic participants showed clear evidence of expression recognition difficulties: they correctly categorised fewer expressions than non-autistic controls. In contrast, low-alexithymic autistic participants were unimpaired relative to non-autistic controls. The same pattern of results was seen when judging masked and unmasked expression stimuli. In sum, we find no evidence for an expression recognition deficit attributable to autism, in the absence of high levels of co-occurring alexithymia, either when participants judge whole-face stimuli or just the eye-region. These findings underscore the influence of co-occurring alexithymia on expression recognition in autism.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".