Estimating Aggression from Emotionally Neutral Faces: Which Facial Cues are Diagnostic?
Bibliographic record
Abstract
The facial width-to-height ratio, a size-independent sexually dimorphic property of the human face, is correlated with aggressive behaviour in men. Furthermore, observers' estimates of aggression from emotionally neutral faces are accurate and are highly correlated with the facial width-to-height ratio. We investigated whether observers use the facial width-to-height ratio to estimate propensity for aggression. In experiments 1a-1c, estimates of aggression remained accurate when faces were blurred or cropped, manipulations that reduce featural cues but maintain the facial width-to-height ratio. Accuracy decreased when faces were scrambled, a manipulation that retains featural information but disrupts the facial width-to-height ratio. In experiment 2, computer-modeling software identified eight facial metrics that correlated with estimates of aggression; regression analyses revealed that the facial width-to-height ratio was the only metric that uniquely predicted these estimates. In experiment 3, we used a computer-generated set of faces varying in perceived threat (Oosterhof and Todorov, 2008 Proceedings of the National Academy of Sciences of the USA 105 11087-11092) and found that as emotionally neutral faces became more 'threatening', the facial width-to-height ratio increased. Together, these experiments suggest that the facial width-to-height ratio is an honest signal of propensity for aggressive behaviour.
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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 | 0.002 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".