Low Vocal Pitch Preference Drives First Impressions Irrespective of Context in Male Voices but Not in Female Voices
Bibliographic record
Abstract
Vocal pitch has been found to influence judgments of perceived trustworthiness and dominance from a novel voice. However, the majority of findings arise from using only male voices and in context-specific scenarios. In two experiments, we first explore the influence of average vocal pitch on first-impression judgments of perceived trustworthiness and dominance, before establishing the existence of an overall preference for high or low pitch across genders. In Experiment 1, pairs of high- and low-pitched temporally reversed recordings of male and female vocal utterances were presented in a two-alternative forced-choice task. Results revealed a tendency to select the low-pitched voice over the high-pitched voice as more trustworthy, for both genders, and more dominant, for male voices only. Experiment 2 tested an overall preference for low-pitched voices, and whether judgments were modulated by speech content, using forward and reversed speech to manipulate context. Results revealed an overall preference for low pitch, irrespective of direction of speech, in male voices only. No such overall preference was found for female voices. We propose that an overall preference for low pitch is a default prior in male voices irrespective of context, whereas pitch preferences in female voices are more context- and situation-dependent. The present study confirms the important role of vocal pitch in the formation of first-impression personality judgments and advances understanding of the impact of context on pitch preferences across genders.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".