Perceptual expertise and the plasticity of other-race face recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
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 it