Evidence from Meta-Analyses of the Facial Width-to-Height Ratio as an Evolved Cue of Threat
Bibliographic record
Abstract
The facial width-to-height ratio (FWHR) is the width of the face divided by the height of the upper face. There is mixed evidence for the hypothesis that the FWHR is a cue of threat and dominance in the human face. We conducted a systematic review and meta-analyses of all peer-reviewed studies (and 2 unpublished studies) to estimate the magnitude of the sex difference in the FWHR, and the magnitude of the relationship between the FWHR and threatening and dominant behaviours and perceptions. Studies were eligible for inclusion if the authors reported an analysis involving the FWHR. Our analyses revealed that the FWHR was larger in men than in women (d = .11, n = 10,853), cued judgements of masculinity in men (r = .35, n of faces = 487; n of observers = 339), and was related to body mass index (r = .31, n = 2,506). Further, the FWHR predicted both threat behaviour in men (r = .16, n = 4,603) and dominance behaviour in both sexes (r = .12, n = 948) across a variety of indices. Individuals with larger FWHRs were judged by observers as more threatening (r = .46, n of faces = 1,691; n of observers = 2,076) and more dominant (r = .20, n of faces = 603; n of observers = 236) than those with smaller FWHRs. Individuals with larger FWHRs were also judged as less attractive (r = -.26, n of faces = 721; n of observers = 335), especially when women made the judgements. These findings provide some support for the hypothesis that the FWHR is part of an evolved cueing system of intra-sexual threat and dominance in men. A limitation of the meta-analyses on perceptions of threat and dominance were the low number of stimuli involving female and older adult 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 | 0.029 | 0.122 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.031 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".