The Other‐Race Effect in Infancy: Evidence Using a Morphing Technique
Bibliographic record
Abstract
Human adults are more accurate at discriminating faces from their own race than faces from another race. This other-race effect (ORE) has been characterized as a reflection of face processing specialization arising from differential experience with own-race faces. We examined whether 3.5-month-old infants exhibit ORE using morphed faces on which adults had displayed a crossover ORE (i.e., Caucasians performed better on Caucasian faces and Asians performed better on Asian faces). In this experiment, Caucasian infants who had grown up in a predominantly Caucasian environment discriminated 100% Caucasian faces from 70% Caucasian/30% Asian morphed faces but failed to discriminate between the corresponding 100% Asian and 70% Asian/30% Caucasian faces. Thus, 3.5-month-olds exhibited evidence of ORE. These results indicate that at least by 3.5 months of age, infants have attained enough face processing expertise to process familiar-race faces in a different manner than unfamiliar-race 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 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".