Perceptual expertise and the plasticity of other-race face recognition
Bibliographic record
Abstract
In this paper, we argue that our ability to recognize own-race faces can be treated as a form of perceptual expertise. Similar to object experts (e.g., birdwatchers), people differentiate own-race faces at the subordinate level of categorization. In contrast, like novices, we tend to classify other-race faces at the basic level of race. We demonstrate that, as a form of perceptual expertise, other-race face recognition can be systematically taught in the lab through subordinate-level training. When participants learn to quickly and accurately differentiate other-race faces at the subordinate level of the individual, the individuating training transfers to improved recognition of untrained other-race faces, produces changes in event-related brain components, and reduces implicit racial bias. Subsequent work has shown that other-race learning can be optimized by directing participants to the diagnostic features of a racial group. The benefits of other-race training are fairly long-lived and are evident even 2 weeks after training. Collectively, the training studies demonstrate the plasticity of other-race face recognition. Rather than a process that is fixed by early developmental events, other-race face recognition is malleable and dynamic, continually being reshaped by the perceptual experiences of the observer.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".