Race-specific perceptual discrimination improvement following short individuation training with faces
Bibliographic record
Abstract
We explore the effect of individuation training on the acquisition of race-specific expertise with faces. The own-race-advantage (“ORA”) – superior performance for own-race faces relative to those of less familiar races – has been explained by the tendency to individuate own-race faces but to categorize faces of other races. Here we ask whether practice individuating other-race faces yields improvement in perceptual discrimination for novel faces of the trained race. We predicted that this improvement would not generalize to novel faces of another race to which participants were equally exposed in an orthogonal task that did not require individuation, yet was at least as difficult. Caucasian participants were trained to individuate faces of one race through subordinate-level naming (African American or Hispanic) and to make difficult eye luminance judgments on faces of the other race. In the latter task, participants judged which eye was of a brighter luminance, while identity and brightest eye were always orthogonal. Given these tasks we are able to rule out differences in exposure, attention and reward in producing race-specific improvements. Our results indicate that the skills acquired during individuation training generalize to novel exemplars of a category but, at least in the case of faces from two different races, they do not generalize to faces of another race experienced with equal frequency in a task that required at least as much attention. Our work demonstrates training effects that generalize to novel stimuli using a much shorter procedure (90 minutes of training, half of which was devoted to individuation) than in prior studies. The results suggest that differential effects in recognition performance could depend on differences in perceptual encoding due to differential practice with individuation. This could magnify any own-race face advantage arising from cognitive, perceptual, or social processes that promote individuation of own-race faces relative to other-race faces.
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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 | 0.001 |
| 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".