High voice pitch mitigates the aversiveness of antisocial cues in men's speech
Bibliographic record
Abstract
Speech contains both explicit social information in semantic content and implicit cues to social behaviour and mate quality in voice pitch. Voice pitch has been demonstrated to have pervasive effects on social perceptions, but few studies have examined these perceptions in the context of meaningful speech. Here, we examined whether male voice pitch interacted with socially relevant cues in speech to influence listeners' perceptions of trustworthiness and attractiveness. We artificially manipulated men's voices to be higher and lower in pitch when speaking words that were either prosocial or antisocial in nature. In Study 1, we found that listeners perceived lower-pitched voices as more trustworthy and attractive in the context of prosocial words than in the context of antisocial words. In Study 2, we found evidence that suggests this effect was driven by stronger preferences for higher-pitched voices in the context of antisocial cues, as voice pitch preferences were not significantly different in the context of prosocial cues. These findings suggest that higher male voice pitch may ameliorate the negative effects of antisocial speech content and that listeners may be particularly avoidant of those who express multiple cues to antisociality across modalities.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".