Men's judgments of women's facial attractiveness from two- and three-dimensional images are similar
Bibliographic record
Abstract
Although most research on human facial attractiveness has used front-facing two-dimensional (2D) images, our primary visual experience with faces is in three dimensions. Because face coding in the human visual system is viewpoint-specific, faces may be processed differently from different angles. Thus, results from perceptual studies using front-facing 2D facial images may not be generalizable to other viewpoints. We used rotating three-dimensional (3D) images of women's faces to test whether men's attractiveness ratings of women's faces from 2D and 3D images differed. We found a significant positive correlation between men's judgments of women's facial attractiveness from 2D and 3D images (r = 0.707), suggesting that attractiveness judgments from 2D images are valid and provide similar information about women's attractiveness as do 3D images. We also found that women's faces were rated significantly more attractive in 3D images than in 2D images. Our study verifies a novel method using 3D facial images, which may be important for future research on viewpoint-specific social perception. This method may also be valuable for the accurate measurement and assessment of facial characteristics such as averageness, identity, attractiveness, and emotional expression.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".