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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".