Facial Visualizations of Women's Voices Suggest a Cross-Modality Preference for Femininity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.001 |
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 it