Multi-cultural cities reduce disadvantages in recognizing naturalistic images of other-race faces: evidence from a novel face learning task
Bibliographic record
Abstract
People often find it more difficult to recognize other- than own-race faces. This other-race effect is robust across numerous ethnic groups. Yet, it remains unclear how this effect changes in people who live in a multiracial environment, and in immigrants whose lifetime perceptual experience changes over time. In the present study, we developed a novel face recognition test that approximates face recognition in the real world. We tested five groups of White and East Asian adults (n = 120) living in racially homogeneous versus heterogeneous cities and East Asians who immigrated to a multiracial city between infancy and adulthood. Multiracial cities reduce the other-race effect. The magnitude of the other-race effect changes as a function of experience, mirroring the racial diversity in perceivers' living environment. Our study highlights the challenge of forming reliable face representations across naturalistic facial variability and suggests a facilitative role of multiracial environments in eliminating the other-race effect.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".