Narrowing in categorical responding to other‐race face classes by infants
Bibliographic record
Abstract
Infants can form object categories based on perceptual cues, but their ability to form categories based on differential experience is less clear. Here we examined whether infants filter through perceptual differences among faces from different other-race classes and represent them as a single other-race class different only from own-race faces. We used a familiarization/novelty-preference procedure to investigate category formation for two other-race face classes (Black vs. Asian) by White 6- and 9-month-olds. The data indicated that while White 6-month-olds categorically represented the distinction between Black and Asian faces, White 9-month-olds formed a broad other-race category inclusive of Black and Asian faces, but exclusive of own-race White faces. The findings provide evidence that narrowing can occur for mental processes other than discrimination: category formation is also affected. The results suggest that frequency of experience with own-race versus other-race classes of faces may propel infants to contrast own-race faces with other-race faces, but not different classes of other-race faces with each other.
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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 | 0.000 | 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".