Mask-wearing selectivity alters observers’ face perception
Bibliographic record
Abstract
Face masks became prevalent across the globe as an efficient tool to stop the spread of COVID-19. A host of studies already demonstrated that masks lead to changes in facial identification and emotional expression processing. These changes were documented across ages and were consistent even with the increased exposure to masked faces. Notably, mask-wearing also changes the state of the observers in regard to their own bodies and other agents. Previous research has already demonstrated a plausible association between observers' states and their perceptual behaviors. Thus, an outstanding question is whether mask-wearing would alter face recognition abilities. To address this question, we conducted a set of experiments in which participants were asked to recognize non-masked faces (Experiment 1), masked faces (Experiment 2) and novel objects (Experiment 3) while they were either masked or unmasked. Mask wearing hindered face perception abilities but did not modulate object recognition ability. Finally, we demonstrated that the decrement in face perception ability relied on wearing the mask on distinctive facial features (Experiment 4). Together, these findings reveal a novel effect of mask-wearing on face recognition. We discuss these results considering the plausible effect of somatosensory stimulation on visual processing as well as the effect of involuntary perspective taking.
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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 | 0.001 |
| 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) | 0.003 | 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".