Development of preferences for differently aged faces of different races
Bibliographic record
Abstract
Children's experiences with differently aged faces changes in the course of development. During infancy, most faces encountered are adult, however as children mature, exposure to child faces becomes more extensive. Does this change in experience influence preference for differently aged faces? The preferences of children for adult versus child, and adult versus infant faces were investigated. Caucasian 3- to 6-year-olds and adults were presented with adult/child and adult/infant face pairs which were either Caucasian or Asian (race consistent within pairs). Younger children (3 to 4 years) preferred adults over children, whereas older children (5 to 6 years) preferred children over adults. This preference was only detected for Caucasian faces. These data support a "here and now" model of the development of face age processing from infancy to childhood. In particular, the findings suggest that growing experience with peers influences age preferences and that race impacts on these preferences. In contrast, adults preferred infants and children over adults when the faces were Caucasian or Asian, suggesting an increasing influence of a baby schema, and a decreasing influence of race. The different preferences of younger children, older children, and adults also suggest discontinuity and the possibility of different mechanisms at work during different developmental periods.
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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 | 0.001 |
| 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) | 0.002 | 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".