Impressions of Dominance are Made Relative to others in the Visual Environment
Bibliographic record
Abstract
Face judgments of dominance play an important role in human social interaction. Perceived facial dominance is thought to indicate physical formidability, as well as resource acquisition and holding potential. Dominance cues in the face affect perceptions of attractiveness, emotional state, and physical strength. Most experimental paradigms test perceptions of facial dominance in individual faces, or they use manipulated versions of the same face in a forced-choice task but in the absence of other faces. Here, we extend this work by assessing whether dominance ratings are absolute or are judged relative to other faces. We presented participants with faces to be rated for dominance (target faces), while also presenting a second face (non-target faces) that was not to be rated. We found that both the masculinity and sex of the non-target face affected dominance ratings of the target face. Masculinized non-target faces decreased the perceived dominance of a target face relative to a feminized non-target face, and displaying a male non-target face decreased perceived dominance of a target face more so than a female non-target face. Perceived dominance of male target faces was affected more by masculinization of male non-target faces than female non-target faces. These results indicate that dominance perceptions can be altered by surrounding faces, demonstrating that facial dominance is judged at least partly relative to other faces.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".