Men's Preferences for Women's Femininity in Dynamic Cross-Modal Stimuli
Bibliographic record
Abstract
Men generally prefer feminine women's faces and voices over masculine women's faces and voices, and these cross-modal preferences are positively correlated. Men's preferences for female facial and vocal femininity have typically been investigated independently by presenting soundless still images separately from audio-only vocal recordings. For the first time ever, we presented men with short video clips in which dynamic faces and voices were simultaneously manipulated in femininity/masculinity. Men preferred feminine men's faces over masculine men's faces, and preferred masculine men's voices over feminine men's voices. We found that men preferred feminine women's faces and voices over masculine women's faces and voices. Men's attractiveness ratings of both feminine and masculine faces were increased by the addition of vocal femininity. Also, men's attractiveness ratings of feminine and masculine voices were increased by the addition of facial femininity present in the video. Men's preferences for vocal and facial femininity were significantly and positively correlated when stimuli were female, but not when they were male. Our findings complement other evidence for cross-modal femininity preferences among male raters, and show that preferences observed in studies using still images and/or independently presented vocal stimuli are also observed when dynamic faces and voices are displayed simultaneously in video format.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".