Observers use facial masculinity to make physical dominance assessments following 100‐ms exposure
Bibliographic record
Abstract
Research has consistently demonstrated that faces manipulated to appear more masculine are perceived as more dominant. These studies, however, have used forced-choice paradigms, in which a pair of masculinized and feminized faces was presented side by side. These studies are susceptible to demand characteristics, because participants may be able to draw the conclusion that faces which appear more masculine should be rated as more dominant. To prevent this, we tested if dominance could be perceived when masculinized or feminized faces were presented individually for only 100 ms. We predicted higher dominance ratings to masculinized faces and better memory of them in a surprise recognition memory test. In the experiment, 96 men rated the physical dominance of 40 facial photographs (masculinized = 20, feminized = 20), which were randomly drawn from a larger set of faces. This was followed by a surprise recognition memory test. Half of the participants were assigned to a condition in which the contours of the facial photographs were set to an oval to control for sexual dimorphism in face shape. Overall, men assigned higher dominance ratings to masculinized faces, suggesting that they can appraise differences in facial sexual dimorphism following very brief exposure. This effect occurred regardless of whether the outline of the face was set to an oval, suggesting that masculinized internal facial features were sufficient to affect dominance ratings. However, participants' recognition memory did not differ for masculinized and feminized faces, which could be due to a floor effect.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".