Women’s own voice pitch predicts their preferences for masculinity in men’s voices
Bibliographic record
Abstract
Previous studies have found that indices of women’s attractiveness predict variation in their mate preferences. For example, objective measures of women’s attractiveness (waist-hip ratio and other-rated facial attractiveness) are positively related to the strength of their preferences for masculinity in men’s faces. Here, we examined whether women’s preferences for masculine characteristics in men’s voices were related to their own vocal characteristics. We found that women’s preferences for men’s voices with lowered (i.e., masculinized) pitch versus raised (i.e., feminized) pitch were positively associated with women’s own average voice pitch. Because voice pitch is positively correlated with many indices of women’s attractiveness, our findings suggest that the attractiveness of the perceiver predicts variation in women’s preferences for masculinity in men’s voices. Such attractiveness-contingent preferences may be adaptive if attractive women are more likely to be able to attract and/or retain masculine mates than relatively unattractive women are. Interestingly, the attractiveness-contingent masculinity preferences observed in our study appeared to be modulated by the semantic content of the judged speech (positively valenced vs. negatively valenced speech), suggesting that attractiveness-contingent individual differences in masculinity preferences do not necessarily reflect variation in responses to simple physical properties of the stimulus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".