Mindreading in the Pandemic: Face Masks Negatively Skew Theory of Mind Judgements
Bibliographic record
Abstract
Face masks obscure a significant portion of the face, reducing the amount of information available to gauge the mental states of others—that is, to exercise the Theory of Mind (ToM) capacity. In two experiments, we assessed the effect of face masks on ToM judgements, measuring recognition accuracy, perceived valence, and perceived arousal of a set facial expressions corresponding to 45 different mental states. Significant effects of face masks were found in all three variables, with differential effects found for positive and negative mental states: Judgements of positive expressions are less accurate when masked, and the expressions are perceived to be less positive and less intense; judgements of negative expressions are also less accurate but, in contrast, the expressions are perceived to be more intense. In addition, we identified face muscles associated with changes in perceived valence and arousal, shedding light on the mechanisms through which face masks impact ToM judgements, which might be relevant for mitigation strategies. We discuss the implications of these findings for real-world social interactions.
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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 | 0.002 | 0.027 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".