Are Men’s Perceptions of Sexually Dimorphic Vocal Characteristics Related to Their Testosterone Levels?
Bibliographic record
Abstract
Feminine physical characteristics in women are positively correlated with markers of their mate quality. Previous research on men's judgments of women's facial attractiveness suggests that men show stronger preferences for feminine characteristics in women's faces when their own testosterone levels are relatively high. Such results could reflect stronger preferences for high quality mates when mating motivation is strong and/or following success in male-male competition. Given these findings, the current study investigated whether a similar effect of testosterone occurs for men's preferences for feminine characteristics in women's voices. Men's preferences for feminized versus masculinized versions of women's and men's voices were assessed in five weekly test sessions and saliva samples were collected in each test session. Analyses showed no relationship between men's voice preferences and their testosterone levels. Men's tendency to perceive masculinized men's and women's voices as more dominant was also unrelated to their testosterone levels. Together, the results of the current study suggest that testosterone-linked changes in responses to sexually dimorphic characteristics previously reported for men's perceptions of faces do not occur for men's perceptions of voices.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".