Symmetry and sexual dimorphism in human faces: interrelated preferences suggest both signal quality
Bibliographic record
Abstract
Symmetry and masculinity in human faces have been proposed to be cues to the quality of the owner. Accordingly, symmetry is generally found attractive in male and female faces, and femininity is attractive in female faces. Women's preferences for male facial masculinity vary in ways that may maximize genetic benefits to women's offspring. Here we examine same- and opposite-sex preferences for both traits (Study 1) and intercorrelations between preferences for symmetry and sexual dimorphism in faces (Study 1 and Study 2) using computer-manipulated faces. For symmetry, we found that male and female judges preferred symmetric faces more when judging faces of the opposite-sex than when judging same-sex faces. A similar pattern was seen for sexual dimorphism (i.e., women preferred more masculine male faces than men did), but women also showed stronger preferences for femininity in female faces than men reported. This suggests that women are more concerned with female femininity than are men. We also found that in women, preferences for symmetry were positively correlated with preferences for masculinity in male faces and that in men preferences for symmetry were positively correlated with preferences for femininity in female faces. These latter findings suggest that symmetry and sexual dimorphism advertise a common quality in faces or that preferences for these facial cues are dependent on a common quality in the judges. Collectively, our findings support the view that preferences for symmetry and sexual dimorphism are related to mechanisms involved in sexual selection and mate choice rather than functionless by-products of other perceptual mechanisms.
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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) | 0.003 | 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".