Recognition of emotions is affected by face masks
Bibliographic record
Abstract
While masks are critical in mitigating disease contagion, they obscure facial features that are important for nonverbal social communication, i.e., emotions, intentions, or mental states. Here we investigated how nonverbal social communication is affected by mask wearing. We asked participants (n=117) to identify happy, angry, fearful, sad, surprised, disgusted, and neutral emotional expressions from masked and unmasked face images. Overall, the data indicated that both hit rate and sensitivity were reduced for all emotions when faces wore masks. Discrimination of disgust was impacted the most (52% reduction in sensitivity), indicating that recognition of this emotion relies strongly on information from the bottom of the face. Perception of sad (18% reduction), happy (15%), and surprised expressions (15%) were impacted less while angry (12%), neutral (8%) and fearful expressions (7%) were impacted the least, indicating that the recognition of these expressions may be largely driven by information from the top of the face (e.g., eyes). Together these results reveal novel insights about the face features contributing to different emotion recognition and additionally suggest an important impact of the Covid-19 pandemic on human social communication.
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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 | 0.004 |
| 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".