Categorization, categorical perception, and asymmetry in infants’ representation of face race
Bibliographic record
Abstract
The present study examined whether 6- and 9-month-old Caucasian infants could categorize faces according to race. In Experiment 1, infants were familiarized with different female faces from a common ethnic background (i.e. either Caucasian or Asian) and then tested with female faces from a novel race category. Nine-month-olds were able to form discrete categories of Caucasian and Asian faces. However, 6-month-olds did not form discrete categories of faces based on race. In Experiment 2, a second group of 6- and 9-month-olds was tested to determine whether they could discriminate between different faces from the same race category. Results showed that both age groups could only discriminate between different faces from the own-race category of Caucasian faces. The findings of the two experiments taken together suggest that 9-month-olds formed a category of Caucasian faces that are further differentiated at the individual level. In contrast, although they could form a category of Asian faces, they could not discriminate between such other-race faces. This asymmetry in category formation at 9 months (i.e. categorization of own-race faces vs. categorical perception of other-race faces) suggests that differential experience with own- and other-race faces plays an important role in infants' acquisition of face processing abilities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".