The attractiveness of facial avergeness: A comparison of adults and children
Bibliographic record
Abstract
Adults rate averaged faces with feature shapes, sizes, and locations approximating the population mean as more attractive than most individual faces (e.g., Langlois & Rogmann, 1990). We are examining developmental changes in the influence of averageness on judgments of attractiveness by showing adults and children pairs of individual faces, in which one face was transformed 50% towards average, while the other face was transformed 50% away from average. In separate blocks of 16 trials, participants judged pairs of adult female faces, pairs of girls' faces, and pairs of boys' faces, and selected which face in each pair they found more attractive. Before testing, faces were made symmetrical and were rated as looking natural by adult judges (M score out of 5= 3 for all three face sets). Adults (n = 36) rated the more average faces as more attractive than the less average faces for all three types of faces (M choice of more average > .92 for women's, girls', and boys' faces; all ps < .001). Five-year-olds (n = 36) rated the more average faces as more attractive than the less average corresponding faces (all ps < .001). The strength of child preferences, however, was significantly weaker than that of adults (M choice of more average > .74; main effect of age, p < .001). Results will be compared to those from ongoing tests of older children. The results indicate that the influence of averageness increases between age 5 and adulthood. The changes may reflect the refinement of an average face prototype as the child is exposed to more faces, increased sensitivity to configural and subtle featural cues in the faces experienced, and/or the greater salience of attractiveness after puberty.
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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 | 0.003 |
| 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) | 0.003 | 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".