Preference for facial averageness: Evidence for a common mechanism in human and macaque infants
Bibliographic record
Abstract
Human adults and infants show a preference for average faces, which could stem from a general processing mechanism and may be shared among primates. However, little is known about preference for facial averageness in monkeys. We used a comparative developmental approach and eye-tracking methodology to assess visual attention in human and macaque infants to faces naturally varying in their distance from a prototypical face. In Experiment 1, we examined the preference for faces relatively close to or far from the prototype in 12-month-old human infants with human adult female faces. Infants preferred faces closer to the average than faces farther from it. In Experiment 2, we measured the looking time of 3-month-old rhesus macaques (Macaca mulatta) viewing macaque faces varying in their distance from the prototype. Like human infants, macaque infants looked longer to faces closer to the average. In Experiments 3 and 4, both species were presented with unfamiliar categories of faces (i.e., macaque infants tested with adult macaque faces; human infants and adults tested with infant macaque faces) and showed no prototype preferences, suggesting that the prototypicality effect is experience-dependent. Overall, the findings suggest a common processing mechanism across species, leading to averageness preferences in primates.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".