Above Average? Perceptions of attractiveness in children and adults
Bibliographic record
Abstract
Adults rate averaged faces approximating the population mean as more attractive than most individual faces (e.g., Langlois & Rogmann, 1990). However, an average created from highly attractive faces is judged by adults to be more attractive than an average created from a wider selection of faces (Perrett et al. 1994, DeBruine et al. 2007). We created two ‘attractiveness dimensions’: one of 21 male faces and the other of 21 female faces, each based on the differences between a typical average and an attractive average. We exaggerated the differences by up to 500% on either side of the typical averages. Adults (n=20) rated the attractiveness of each face, and judged the most attractive faces to be ‘above average’ (in the direction of the attractive average) on the attractiveness dimension for both male and female faces. To explore perceptions of ‘the attractiveness dimension’ developmentally, 5-year-olds (n=20) and a separate group of adults (n=20), viewed pairs of faces that varied along the attractiveness dimension and selected which face was more attractive. Regardless of pairing and gender of face, adults selected the face nearer the attractive average as more attractive. Across the pairings of male faces, five-year-olds selected faces closer to the attractive average to be more attractive to the same extent as adults. However, for female faces, the effect was weaker in children than in adults and for some pairings, not significantly different from chance. The results indicate that the ‘attractiveness dimension‘ already influences judgments of attractiveness by age 5, but to a greater extent for male than female faces. The results suggest influences on attractiveness in addition to cognitive fluency for processing average faces emerge early in development. Surprisingly, the results suggest greater maturity on this dimension for male than female faces. Meeting abstract presented at VSS 2013
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".