The dual nature of the human face: there is a little Jekyll and a little Hyde in all of us
Bibliographic record
Abstract
The fact that a mere glance makes it possible to extract a wealth of information about the person being observed is testament to both the salience of the human face and the brain's high efficiency in processing this information. Prior work has revealed that social judgments of faces are determined by facial features that vary on two orthogonal dimensions: trustworthiness and dominance. We conducted two experiments to investigate the visual information subtending trustworthiness and dominance judgments. In Experiment 1, we used the Bubbles technique to identify the facial areas and the spatial frequencies that modulate these two judgments. Our results show that the eye and mouth areas in high-to-medium spatial frequency bands were positively correlated with judgments of trustworthiness; the eyebrows region in medium-to-low frequency bands was positively correlated with judgments of dominance; and the lower left jawbone in medium-to-low frequency bands was negatively correlated with judgments of dominance. In Experiment 2, we used the results of Experiment 1 to induce subtle variations in the relative contrast of different facial areas, and showed that it is possible to rig social perception using such a manipulation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".