Listen, follow me: Dynamic vocal signals of dominance predict emergent social rank in humans.
Bibliographic record
Abstract
Similar to the nonverbal signals shown by many nonhuman animals during aggressive conflicts, humans display a broad range of behavioral signals to advertise and augment their apparent size, strength, and fighting prowess when competing for social dominance. Favored by natural selection, these signals communicate the displayer's capacity and willingness to inflict harm, and increase responders' likelihood of detecting and establishing a rank asymmetry, and thus avoiding costly physical conflicts. Included among this suite of adaptations are vocal changes, which occur in a wide range of nonhuman animals (e.g., chimpanzees, rhesus monkeys) prior to aggression, but have not been systematically examined in humans. The present research tests whether and how humans use vocal pitch modulations to communicate information about their intention to dominate or submit. Results from Study 1 demonstrate that in the context of face-to-face group interactions, individuals spontaneously alter their vocal pitch in a manner consistent with rank signaling. Raising one's pitch early in the course of an interaction predicted lower emergent rank, whereas deepening one's pitch predicted higher emergent rank. Results from Study 2 provide causal evidence that these vocal shifts influence perceptions of rank and formidability. Together, findings suggest that humans use transient vocal changes to track, signal, and coordinate status relationships.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".