Facial masks affect emotion recognition in the general population and individuals with autistic traits
Bibliographic record
Abstract
Facial expressions, and the ability to recognize these expressions, have evolved in humans to communicate information to one another. Face masks are equipment used in healthcare by health professionals to prevent the transmission of airborne infections. As part of the social distancing efforts related to COVID-19, wearing facial masks has been practiced globally. Such practice might influence affective information communication among humans. Previous research suggests that masks disrupt expression recognition of some emotions (e.g., fear, sadness or neutrality) and lower the confidence in their identification. To extend the previous research, in the current study we tested a larger and more diverse sample of individuals and also investigated the effect of masks on perceived intensity of expressions. Moreover, for the first time in the literature we examined these questions using individuals with autistic traits. Specifically, across three experiments using different populations (college students and general population), and the 10-item Autism Spectrum Quotient (AQ-10; lower and higher scorers), we tested the effect of facial masks on facial emotion recognition of anger, disgust, fear, happiness, sadness, and neutrality. Results showed that the ability to identify all facial expressions decreased when faces were masked, a finding observed across all three studies, contradicting previous research on fear, sad, and neutral expressions. Participants were also less confident in their judgements for all emotions, supporting previous research; and participants perceived emotions as less expressive in the mask condition compared to the unmasked condition, a finding novel to the literature. An additional novel finding was that participants with higher scores on the AQ-10 were less accurate and less confident overall in facial expression recognition, as well as perceiving expressions as less intense. Our findings reveal that wearing face masks decreases facial expression recognition, confidence in expression identification, as well as the perception of intensity for all expressions, affecting high-scoring AQ-10 individuals more than low-scoring individuals.
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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".