The impact of face masks on autistic and non-autistic adults’ face processing abilities
Bibliographic record
Abstract
Atypical emotion recognition is argued to be characteristic of autism spectrum conditions (ASC), underpinned in part by atypical eye gaze, with less eye contact and more gaze direction towards the mouth. Given the widespread adoption of facemasks due to the Covid-19 pandemic, exploring the effect of facemasks on face processing in autism, as they occlude the mouth region, is of interest. This study investigated the impact of facemasks on emotion and identity recognition, with a non-autistic (N = 38) and autistic sample (N = 25). Participants also completed the Toronto Alexithymia Scale, and the abridged Autism Quotient, to investigate the roles of alexithymia and autistic traits in any impacts of facemasks on face processing abilities. Masks were detrimental to performance for both autistic and non-autistic groups, and masks affected participants’ performance on the emotion recognition task more than the identity recognition task. Autistic participants’ performance was more negatively affected by the presence of masks, for both identity and emotion recognition. The impact of facemasks was not predicted by alexithymia or autistic traits, after gender, ethnicity and age were taken into account. Overall, the results suggest that masks negatively affect face processing for both non-autistic and autistic groups, particularly emotion recognition, however the impact of masks is amplified for autistic people. These results supplement reports of autistic people that masks interfere with social interactions.
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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) | 0.002 | 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".