Putting a face in its place: in- and out-group membership alters the N170 response
Bibliographic record
Abstract
Two mechanisms have been proposed to account for the difficulty in recognizing faces of other racial groups (the other-race effect; ORE): perceptual expertise and social cognitive factors. Focusing on the social cognitive factors alone, we manipulated in-group and out-group memberships based on two social categories (nationality and university affiliation), and controlled for perceptual expertise by testing Caucasian participants with Caucasian faces only. Using event-related potentials (ERPs) and focusing on the N170, a brain electrical component sensitive to faces, we provide for the first time strong support for the social cognitive influence on face processing within 200 ms. After participants learned the social categories, the N170 latency differentiated between double in-group and double out-group faces, taking longer to process the latter. In comparison, without group memberships, there was no difference in N170 latency among the faces. These results are consistent with recent findings of behavioral and imaging research, providing further support for the social cognitive model and its potential for understanding ORE.
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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) | 0.004 | 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".