Facial dominance augments perceived proximity: Evidence from a visual illusion.
Bibliographic record
Abstract
Dominance is a major organizing principle of human societies that impacts a wide range of human behaviors, from gaze-following to voting choices. Here, we examined how dominance modulates a fundamental perceptual ability: the perception of proximity. We used the "Fat Face" illusion, a novel paradigm that measures perceived proximity implicitly. The illusion depicts a phenomenon that occurs when two identical faces are aligned vertically (one above the other) and the bottom face consistently appears larger. This illusion suggests that our visual system uses a vertical layout to infer the relative proximity of faces, so that the bottom face appears closer, and is thereby perceived as larger than the top one. We found that the illusion was larger for dominant than for submissive faces (Experiment 1). Moreover, when a dominant face was presented below a submissive one, participants reported a larger illusion than when a dominant face was above a submissive face (Experiments 2a and 2b). These findings suggest that dominant faces are perceived to be closer to observers than submissive faces. Furthermore, we found a stronger illusion for other-race faces as opposed to own-race faces, suggesting that we also misperceive other-race faces as closer than own-race faces. Together, these findings suggest that the visual system is highly sensitive to self-relevant, potentially threatening stimuli (e.g., dominant faces and other-race individuals) in the environment by misperceiving them as closer. In line with the recently proposed threat-signal hypothesis, this mechanism may allow for rapid and adaptive behaviors in our everyday social interactions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".