No evidence that averaging voices influences attractiveness
Bibliographic record
Abstract
Vocal attractiveness influences important social outcomes. While most research on the acoustic parameters that influence vocal attractiveness has focused on the possible roles of sexually dimorphic characteristics of voices, such as fundamental frequency (i.e., pitch) and formant frequencies (i.e., a correlate of body size), other work has reported that increasing vocal averageness increases attractiveness. Here we investigated the roles these three characteristics play in judgments of the attractiveness of male and female voices. In Study 1, we found that increasing vocal averageness significantly decreased distinctiveness ratings, demonstrating that participants could detect manipulations of vocal averageness in this stimulus set and using this testing paradigm. However, in Study 2, we found no evidence that increasing averageness significantly increased attractiveness ratings of voices. In Study 3, we found that fundamental frequency was negatively correlated with male vocal attractiveness and positively correlated with female vocal attractiveness. By contrast with these results for fundamental frequency, vocal attractiveness and formant frequencies were not significantly correlated. Collectively, our results suggest that averageness may not necessarily significantly increase attractiveness judgments of voices and are consistent with previous work reporting significant associations between attractiveness and voice pitch.
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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 | 0.002 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".