Impact of similarity on recognition of faces of Black and White targets
Bibliographic record
Abstract
One reason for the persistence of racial inequality may be anticipated dissimilarity with racial outgroups. In the present research, we explored the impact of perceived similarity with White and Black targets on facial identity recognition accuracy. In two studies, participants first completed an ostensible personality survey. Next, in a Learning Phase, Black and White faces were presented on one of three background colours. Participants were led to believe that these colours indicated similarities between them and the target person in the image. Specifically, they were informed that the background colours were associated with the extent to which responses by the target person on the personality survey and their own responses overlapped. In actual fact, faces were randomly assigned to colour. In both studies, non-Black participants (Experiment 1) and White participants (Experiment 2) showed better recognition of White than Black faces. More importantly in the present context, a positive linear effect of similarity was found in both studies, with better recognition of increasingly similar Black and White targets. The independent effects for race of target and similarity, with no interaction, indicated that participants responded to Black and White faces according to category membership as well as on an interpersonal level related to similarity with specific targets. Together these findings suggest that while perceived similarity may enhance identity recognition accuracy for Black and White faces, it may not reduce differences in facial memory for these racial categories.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".