Effects of mask use and other‐race on face perception, emotion recognition, and social distancing during the COVID‐19 pandemic
Bibliographic record
Abstract
Abstract We tested the effect of mask use and other‐race effect on (a) face recognition, (b) recognition of facial expressions, and (c) social distance. Caucasian subjects were tested in a matching‐to‐sample paradigm with either masked or unmasked Caucasian and Asian faces. The participants exhibited the best performance in recognizing an unmasked face condition and the poorest to recognize a masked face that they had seen earlier without a mask. Accuracy was poorer for Asian faces than Caucasian faces. The second experiment presented Asian or Caucasian faces having emotional expressions, with and without masks. The participants' emotion recognition performance decreased for masked faces. From the most accurately to least accurately recognized emotions were as follows: happy, neutral, disgusted, fearful. Performance was poorer for Asian stimuli compared to Caucasian. In Experiment 3 the same participants indicated the social distance they would prefer with each pictured person. They preferred a wider distance with unmasked faces compared to masked faces. Distance from farther to closer was as follows: disgusted, fearful, neutral, and happy. They preferred wider social distance for Asian compared to Caucasian faces. Altogether, findings indicated that during the COVID‐19 pandemic mask wearing decreased recognition of faces and emotional expressions, negatively impacting communication among people from different ethnicities.
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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 | 0.002 |
| 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".