The influence of face masks on emotion recognition and the role of individual differences
Bibliographic record
Abstract
With widespread adoption of mask wearing, the 2020 Covid-19 pandemic highlighted a need for a deeper understanding of how facial feature obstruction affects emotion recognition. Here we asked participants (n=120) to identify disgusted, angry, sad, neutral, surprised, happy, and fearful emotions from faces with and without masks, and examined if recognition performance was related to their level of social competence and personality traits. Performance was reduced for all masked relative to unmasked emotions. Masks impacted recognition of expressions with diagnostic lower face features the most (disgust, anger) and those with diagnostic upper face features the least (fear, surprise). Recognition performance also varied at the individual level. Persons with higher overall social competence were better at identifying unmasked expressions, while persons with lower trait extraversion and higher trait agreeableness were better at recognizing masked expressions. These results reveal novel insights about the role of face features in emotion recognition and show that obscuring facial features affects social communication differently as a function of individual social competence and personality traits.
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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 | 0.001 | 0.007 |
| 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".