Becoming Familiar With a Newly Encountered Face: Evidence of an Own-Race Advantage
Bibliographic record
Abstract
Adults' ability to match identity in images of unfamiliar faces is impaired for other- compared with own-race faces; their ability to match identity in images of familiar faces is independent of face race. Exposure to within-person variability in appearance plays a key role in face learning. Past research suggests that children need exposure to higher levels of variability than adults to learn a new face-a difference that has been attributed to experience. We predicted that adults' limited experience with other-race faces would result in their needing exposure to higher levels of variability when learning other- compared with own-race faces. We introduced adults to four new identities (two own-race; two other-race) in one of the three conditions: a single image, a low-variability video (filmed on 1 day), or a high-variability video (filmed across 3 days). Adults' ability to recognize new instances of learned identities improved in the low-variability condition for own-race faces but only in the high-variability condition for other-race faces. We discuss learning mechanisms that might drive this difference-a difference we attribute to experience.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".