How robust is the own-group face recognition bias? Evidence from first- and second-generation East Asian Canadians
Bibliographic record
Abstract
There is mounting evidence that North Americans are better able to remember faces of targets who belong to the same social group, and this is true even when the social groups are experimentally created. Yet, how Western cultural contexts afford the development of this own group face recognition bias remains unknown. This question is particularly important given that recent findings suggest that first-generation East Asian Canadians do not show this bias. In the current research, we examined the own-group bias among first- and second-generation East Asian Canadians, who vary systematically in their exposure to and engagement in a Western cultural context, and tested mediators that could explain any difference. In Study 1, second-generation East Asian Canadians showed better memory for same-group (vs. other-group) faces. In Studies 2 and 3, as well as a meta-analysis of all three studies, we found some additional evidence that second-generation East Asian Canadians show better memory for same-group (vs. other-group) faces, whereas first-generation East Asian Canadians do not, but only when each cultural group was examined separately in each study, as no interaction with generational status emerged. In Study 2, and in a higher powered pre-registered Study 3, we also examined whether second- (vs. first-) generational status had a positive indirect effect on same-group face recognition through the effects of acculturation and perceived relational mobility in the immediate social environment, however this mediation model was not supported by the data. Overall, the results provide some additional evidence that the effect of mere social categorization on face recognition may not be as consistently found among East Asian participants.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".