In the hands of the beholder: Wearing a COVID-19 mask is associated with its attractiveness
Bibliographic record
Abstract
Protective facial masks reduce the spread of COVID-19 infection and save lives. Yet a substantial number of people have been resistant to wearing them. Considerable effort has been invested in convincing people to put on a mask, if not for their own sake than for those more vulnerable. Social and cognitive psychologists know that use and liking go both ways: people use what they like, and they like what they use. Here we asked whether positive attitudes towards facial masks were higher in those who had been wearing them longer. We asked participants in a diverse sample ( N = 498 from five countries and more than 30 US states) to rate how attractive and emotionally arousing masks and other objects associated with COVID-19 were in comparison to neutral objects, as well as reporting on their mask-wearing habits. To confirm reliability of findings, the experiment was repeated in a subset of participants 8–10 weeks later. The findings show that regular use of protective masks was linked to their positive appraisal, with a higher frequency and a longer history of wearing a mask predicting increased mask attractiveness. These results extended to other COVID-related objects relative to controls. They also provide critical ecological validity for the idea that emotional appraisal of everyday objects is associated with our experience of using them. Practically, they imply that societal measures to encourage mask wearing may have contributed to positive emotional appraisals in those who put them on, whether due to personal choice or societal pressure.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".