Development of category formation for faces differing by age in 9‐ to 12‐month‐olds: An effect of experience with infant faces
Bibliographic record
Abstract
We examined category formation for faces differing in age in 9- and 12-month-olds, and the influence of exposure to infant faces on such ability. Infants were familiarized with adult or infant faces, and then tested with a novel exemplar from the familiarized category paired with a novel exemplar from a novel category (Experiment 1). Both age groups formed discrete categories of adult and infant faces, but exposure to infant faces in everyday life did not modulate performance. The same task was conducted with child versus infant faces (Experiment 2). Whereas 9-month-olds preferred infant faces after familiarization with child faces, but not child faces after familiarization with infant faces, 12-month-olds formed discrete categories of child and infant faces. Moreover, more exposure to infant faces correlated with higher novel category preference scores when infants were familiarized with infant faces in 12-month-olds, but not 9-month-olds. The 9-month-old asymmetry did not reflect spontaneous preference for infant over child faces (Experiment 3). These findings indicate that 9- and 12-month-olds can form age-based categories of faces. The ability of 12-month-olds to form separate child and infant categories suggests that they have a more exclusive representation of face age, one that may be influenced by prior experience with infant faces.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".