The neural and behavioral plasticity of other-race face recognition
Bibliographic record
Abstract
Although it is well established that people are better at recognizing own- versus other-race faces, the neural mechanisms mediating this advantage are not well understood. In this study, Caucasian participants were trained to differentiate African (or Hispanic) faces at the subordinate individual level and categorize Hispanic (or African) faces at the basic level of race. Training occurred over five consecutive days of learning. Before and after training, participants were administered an old/new recognition test of novel African and Hispanic faces while recording electrophysiological activity. Previous event-related potential research has suggested that two posterior brain components, the N170 and N250, are linked to different aspects of face processing. Whereas the N170 provides an index of category exposure, the N250 is a marker of subordinate level identification. Consistent with this view, after training both African and Hispanic faces elicited a shorter N170 latency regardless of whether they were learned at the subordinate or at the basic level. However, faces trained at the subordinate level of the individual elicited a greater N250 and showed greater improvements in post-training recognition relative to faces trained at the basic level. These results suggest that subordinate level training enhances memory for other-race faces and improved recognition is indicated by the presence of the N250 component.
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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) | 0.002 | 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".