Perceptions of Dominance following Glimpses of Faces and Bodies
Bibliographic record
Abstract
Dominance is one of the most ecologically important social traits that humans express and perceive. Here, we examined perceivers' capacity to judge dominance under physical and temporal constraints. In study 1, dominant, neutral, and submissive poses of otherwise non-expressive faces and impoverished facial outlines were judged after exposure for 27 ms, 40 ms, 94 ms, or at a self-paced rate (approximately 2000 ms). Perceivers' judgments of dominance were significantly more accurate than chance guessing for exposures of 40 ms and greater, with no significant increase in accuracy given additional viewing time. In study 2, we replaced faces with bodies and figural outlines of bodies. Perceivers' judgments were again better than chance for exposures of 40 ms and greater, but significant increases in accuracy were observed for durations of 94 ms and at a self-paced rate. Finally, in study 3, we combined studies 1 and 2 to allow comparisons across stimuli. Results showed that judgments of dominance from the faces were significantly more accurate than were those of the bodies, and judgments of full stimuli were more accurate than were those of outlines. These data extend our knowledge of the efficient and accurate perception of social cues from nonverbal behavior.
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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 | 0.003 |
| 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".