Emotional context can reduce the negative impact of face masks on inferring emotions
Bibliographic record
Abstract
While face masks prevent the spread of disease, they occlude lower face parts and thus impair facial emotion recognition. Since emotions are often also contextually situated, it remains unknown whether providing a descriptive emotional context alongside the facial emotion may reduce some of the negative impact of facial occlusion on emotional communication. To address this question, here we examined how emotional inferences were affected by facial occlusion and the availability of emotional context. Participants were presented with happy or sad emotional faces who were either fully visible or partially obstructed by an opaque surgical mask. The faces were shown either within an emotionally congruent (e.g., "Her cat was found/lost yesterday afternoon") or neutral ("Get ready to see the next person") context. Participants were asked to infer the emotional states of the protagonists by rating their emotional intensity and valence. Facial occlusion by masks impacted the ratings, such that protagonists were judged to feel less intense and more neutral emotions when they wore masks relative to when their face was fully visible. Importantly, this negative impact of visual occlusion by mask was reduced but not fully eliminated when the faces were presented within a congruent emotional context. Thus, visual occlusion of facial emotions impairs understanding of emotions, with this negative effect of face masks partially mitigated by the availability of a larger emotional context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".