Revealing the neural representations underlying other-race face perception
Bibliographic record
Abstract
The other-race effect (ORE) refers to poorer recognition for faces of other races than one's own. This study investigates the neural and representational basis of ORE in East Asian and White participants using behavioral measures, neural decoding, and image reconstruction based on electroencephalography (EEG) data. Our investigation identifies a reliable neural counterpart of ORE, with reduced decoding accuracy for other-race faces, and it relates this result to higher density of other-race face representations in face space. Then, we characterize the temporal dynamics and the prominence of ORE for individual variability at the neural level. Importantly, we use a data-driven image reconstruction approach to reveal visual biases underlying other-race face perception, including a tendency to perceive other-race faces as more typical, younger, and more expressive. These findings provide neural evidence for a classical account of ORE invoking face space compression for other-race faces. Further, they indicate that ORE involves not only reduced identity information but also broader, systematic distortions in visual representation with considerable cognitive and social implications.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".