Differences between the perception and the recognition of cross-race faces
Bibliographic record
Abstract
The Cross-Race Effect (CRE) refers to the robust finding that individuals more accurately recognize faces of their own race than those of other races. According to the Face Space Model, this effect arises because cross-race faces are represented more densely-i.e., with less differentiation-in psychological space than same-race faces (Valentine & Endo, 1992). Although some studies have demonstrated that same-race faces occupy more dispersed positions in multidimensional space (e.g., Byatt & Rhodes, 2004), few have directly examined the link between recognition performance and underlying psychological face representations. The present experiments tested the Face Space account of the CRE by assessing recognition accuracy and perceived facial similarity across racial group. In Experiments 1A and 1B, Asian, Black, and White participants completed old/new recognition tasks for same- and cross-race faces. In Experiments 2A and 2B, separate groups of participants rated facial similarity for the same stimuli and were represented in the face space. We hypothesized (a) superior recognition for same-race faces and (b) that cross-race faces would be judged as more similar and thus more densely clustered in Face Space. Results partially supported these predictions. Consistent with the Face Space account, distinctive faces were overall better recognized than less distinctive faces and some conditions revealed recognition performance was aligned with perceived face clustering by race. However, other conditions showed a dissociation between memory and similarity where cross-race faces were more poorly recognized than same-race faces but were perceived as equally similar. These findings suggest that the CRE may not be fully accounted for by representational density in Face Space alone and highlight the potential dissociation between cognitive and perceptual processes of same- and cross-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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".