Facial Visualizations of Women's Voices Suggest a Cross-Modality Preference for Femininity
Bibliographic record
Abstract
Women with higher-pitched voices and more feminine facial features are commonly judged as being more attractive than are women with lower-pitched voices and less feminine faces, possibly because both features are affected by (age-related) variations in endocrine status. These results are primarily derived from investigations of perceptions of variations in single-modality stimuli (i.e., faces or voices) in samples of young adult women. In the present study we sought to test whether male and female perceptions of women's voices affect visual representations of facial femininity. Eighty men and women judged voice recordings of 10 young girls (11-15 years), 10 adult women (19-28 years) and 10 peri-/post-menopausal women (50-64 years) on age, attractiveness, and femininity. Another 80 men and women were asked to indicate the face they think each voice corresponded to using a video that gradually changed from a masculine looking male face into a feminine looking female face. Both male and female participants perceived voices of young girls and adult women to be significantly younger, more attractive and feminine than those of peri-/post-menopausal women. Hearing young girls' and adult women's voices resulted in both men and women selecting faces that differed markedly in apparent femininity from those associated with peri-/post-menopausal women's voices. Voices of young girls had the strongest effect on visualizations of facial femininity. Our results suggest a cross-modal preference for women's vocal and facial femininity, which depends on female age and is independent of the perceiver's sex.
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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 | 0.000 | 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".