The Saliency of Angular Shapes in Threatening and Nonthreatening Faces
Bibliographic record
Abstract
Several lines of evidence suggest that angularity and curvilinearity are relied upon to infer the presence or absence of threat. This study examines whether angular shapes are more salient in threatening compared with nonthreatening emotionally neutral faces. The saliency of angular shapes was measured by the amount of local maxima in S(θ), a function that characterizes how the Fourier magnitude spectrum varies along specific orientations. The validity of this metric was tested and supported with images of threatening and nonthreatening real-world objects and abstract patterns that have predominantly angular or curvilinear features (Experiment 1). This metric was then applied to computer-generated faces that maximally correlate with threat (Experiment 2a) and to real faces that have been rated according to threat (Experiment 3). For computer-generated faces, angular shapes became increasingly salient as the threat level of the faces increased. For real faces, the saliency of angular shapes was not predictive of threat ratings after controlling for other well-established threat cues, however, other facial features related to angularity (e.g., brow steepness) and curvilinearity (e.g., round eyes) were significant predictors. The results offer preliminary support for angularity as a threat cue for emotionally neutral faces.
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 | 0.005 |
| 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 | 0.001 |
| 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".