The prioritization of voice fundamental frequency or formants in listeners’ assessments of speaker size, masculinity, and attractiveness
Bibliographic record
Abstract
Key features of the voice--fundamental frequency (F(0)) and formant frequencies (Fn)--can vary extensively among individuals. Some of this variation might cue fitness-related, biosocial dimensions of speakers. Three experiments tested the independent, joint and relative effects of F(0) and Fn on listeners' assessments of the body size, masculinity (or femininity), and attractiveness of male and female speakers. Experiment 1 replicated previous findings concerning the joint and independent effects of F(0) and Fn on these assessments. Experiment 2 established frequency discrimination thresholds (or just-noticeable differences, JND's) for both vocal features to use in subsequent tests of their relative salience. JND's for F(0) and Fn were consistent in the range of 5%-6% for each sex. Experiment 3 put the two voice features in conflict by equally discriminable amounts and found that listeners consistently tracked Fn over F(0) in rating all three dimensions. Several non-exclusive possibilities for this outcome are considered, including that voice Fn provides more reliable cues to one or more dimensions and that listeners' assessments of the different dimensions are partially interdependent. Results highlight the value of first establishing JND's for discrimination of specific features of natural voices in future work examining their effects on voice-based social judgments.
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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 | 0.006 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".