Revealing the neural representations underlying other-race face perception
Bibliographic record
Abstract
Abstract The other-race effect, a disadvantage at recognizing faces of other races than one’s own, has received considerable attention, especially regarding its wide scope and underlying mechanisms. Here, we aim to elucidate its neural and representational basis by relating behavioral performance in East Asian and White individuals to neural decoding and image reconstruction relying on electroencephalography data. Our investigation uncovers a reliable neural counterpart of the other-race effect (i.e., a decoding disadvantage for other-race faces) along with its extended dynamics and prominence across individuals. Further, it retrieves, via neural-based image reconstruction, visual representations underlying other-race face perception and their intrinsic biases. Notably, our data-driven approach reveals that other-race faces are perceived not just as more typical but, also, as younger and more expressive. These findings, pointing to multiple visual biases surrounding the other-race effect, speak to the complexity of its neural mechanisms and its 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 | 0.001 |
| 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) | 0.003 | 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".