Lower empathy for face mask wearers is not explained by observer’s reduced facial mimicry
Bibliographic record
Abstract
Facial occlusion alters social processes that rely on face visibility, including spontaneous mimicry of emotions. Given that facial mimicry of emotions is theorized to play an important role in how we empathize or share emotions with others, here we investigated if empathy was reduced for faces wearing masks because masks may reduce the ability to mimic facial expressions. In two preregistered experiments, participants rated their empathy for faces displaying happy or neutral emotions and wearing masks or no masks. We manipulated mimicry by either blocking mimicry with observers holding a pen in between their teeth (Experiment 1) or by producing a state of constant congruent mimicry by instructing observers to smile (Experiment 2). Results showed reduced empathy ratings for masked faces. Mimicry overall facilitated empathy, with reduced empathy ratings when mimicry was blocked and higher empathy ratings when it was instructed. However, this effect of mimicry did not vary with mask condition. Thus, while observers were impaired in sharing emotions with masked faces, this impairment did not seem to be explained by a reduction in facial mimicry. These results show that mimicry is an important process for sharing emotions, but that occluding faces with masks reduces emotion sharing via a different mechanism.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".