Human preference for masculinity differs according to context in faces, bodies, voices, and smell
Bibliographic record
Abstract
Sexual dimorphism is important in mate choice in many species and can be appraised via multiple traits in any one individual. Thus, one question that arises is whether sexual dimorphism in different traits influences preferences consistently. Here, we examined human preferences for masculinity/femininity in different types of stimuli. For face and body stimuli, images were manipulated to be more or less masculine using computer graphic techniques. Voice stimuli were made more or less masculine by manipulating pitch. For smell, we used variation among male aftershaves as a proxy for manipulating masculinity of real male smell and used relatively masculine/feminine odors. For women, we found that preferences for more masculine stimuli were greater for short-term than for long-term relationships across all stimuli types. Further analyses revealed consistency in preferences for masculinity across stimuli types, at least for short-term judgments, whereby women with preferences for masculinity in one domain also had preferences for masculinity in the other domains. For men, we found that preferences for more feminine stimuli were greater for short-term than for long-term judgments across face and voice stimuli, whereas the reverse was true for body stimuli. Further analyses revealed consistency in preferences for masculinity across stimuli types for long-term judgments, whereby men with preferences for femininity in one domain also had preferences for femininity in the other domains. These data suggest that masculinity/femininity as a trait may be assessed via different modalities and that masculinity/femininity in the different modalities might be representing a single underlying quality in individuals.
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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) | 0.003 | 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".