Unattractive faces are more attractive when the bottom-half is masked, an effect that reverses when the top-half is concealed
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Facial attractiveness in humans signals an individual's genetic condition, underlying physiology and health status, serving as a cue to one's mate value. The practice of wearing face masks for prevention of transmission of airborne infections may disrupt one's ability to evaluate facial attractiveness, and with it, cues to an individual's health and genetic condition. The current research investigated the effect of face masks on the perception of face attractiveness. Across four studies, we tested if below- and above-average attractive full faces are equally affected by wearing facial masks. The results reveal that for young faces (Study 1) and old faces (Study 2) a facial mask increases the perceived attractiveness of relatively unattractive faces, but there is no effect of wearing a face mask for highly attractive faces. Study 3 shows that the same pattern of ratings emerged when the bottom-half of the faces are cropped rather than masked, indicating that the effect is not mask-specific. Our final Study 4, in which information from only the lower half of the faces was made available, showed that contrary to our previous findings, highly attractive half-faces are perceived to be less attractive than their full-face counterpart; but there is no such effect for the less attractive faces. This demonstrates the importance of the eye-region in the perception of attractiveness, especially for highly attractive faces. Collectively these findings suggest that a positivity-bias enhances the perception of unattractive faces when only the upper face is visible, a finding that may not extend to attractive faces because of the perceptual weight placed on their eye-region.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it