Age Similarities in Recognizing Threat from Faces and Diagnostic Cues
Bibliographic record
Abstract
Background.Previous research indicates that younger adults (YA) can identify men's tendency to be aggressive based merely on their neutral expression faces.We compared older adults (OA) and YA accuracy and investigated contributing facial cues. Method.In Study 1, YA and OA rated the aggressiveness of young men depicted in facial photographs in a control, distraction, or accuracy motivation condition.In Study 2, YA and OA rated how angry, attractive, masculine, and babyfaced the men looked in addition to rating their aggressiveness.These measures plus measured facial width-to-height ratio (FWHR) were used to examine cues to aggressiveness.Results.Accuracy coefficients, calculated by correlating rated aggressiveness with the men's previously measured actual aggressiveness, were significant and equal for OA and YA.Accuracy was not moderated by distraction or accuracy motivation, suggesting automatic processing.A greater FWHR, lower attractiveness, and higher masculinity independently influenced rated aggressiveness by both age groups and also were valid cues to actual aggressiveness.Discussion.Despite previous evidence for positivity biases in OA, they can be just as accurate as YA when it comes to discerning actual differences in the aggressiveness of young men.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".