Influence of Perceived Height, Masculinity, and Age on Each Other and on Perceptions of Dominance in Male Faces
Bibliographic record
Abstract
Several studies have examined the individual effects of facial cues to height, masculinity, and age on interpersonal interactions and partner preferences. We know much less about the influence of these traits on each other. We, therefore, examined how facial cues to height, masculinity, and age influence perceptions of each other and found significant overlap. This suggests that studies investigating the effects of one of these traits in isolation may need to account for the influence of the other two traits. Additionally, there is inconsistent evidence on how each of these three facial traits affects dominance. We, therefore, investigated how varying such traits influences perceptions of dominance in male faces. We found that increases in perceived height, masculinity, and age (up to 35 years) all increased facial dominance. Our results may reflect perceptual generalizations from sex differences as men are on average taller, more dominant, and age faster than women. Furthermore, we found that the influences of height and age on perceptions of dominance are mediated by masculinity. These results give us a better understanding of the facial characteristics that convey the appearance of dominance, a trait that is linked to a wealth of real-world outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".