Transparent masks reduce the negative impact of opaque masks on understanding emotional states but not on sharing them
Bibliographic record
Abstract
While face masks provide necessary protection against disease spread, they occlude the lower face parts (chin, mouth, nose) and consequently impair the ability to accurately perceive facial emotions. Here we examined how wearing face masks impacted making inferences about emotional states of others (i.e., affective theory of mind; Experiment 1) and sharing of emotions with others (i.e., affective empathy; Experiment 2). We also investigated whether wearing transparent masks ameliorated the occlusion impact of opaque masks. Participants viewed emotional faces presented within matching positive (happy), negative (sad), or neutral contexts. The faces wore opaque masks, transparent masks, or no masks. In Experiment 1, participants rated the protagonists' emotional valence and intensity. In Experiment 2, they indicated their empathy for the protagonist and the valence of their emotion. Wearing opaque masks impacted both affective theory of mind and affective empathy ratings. Compared to no masks, wearing opaque masks resulted in assumptions that the protagonist was feeling less intense and more neutral emotions. Wearing opaque masks also reduced positive empathy for the protagonist and resulted in more neutral shared valence ratings. Wearing transparent masks restored the affective theory of mind ratings but did not restore empathy ratings. Thus, wearing face masks impairs nonverbal social communication, with transparent masks able to restore some of the negative effects brought about by opaque masks. Implications for the theoretical understanding of socioemotional processing as well as for educational and professional settings are discussed.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".