Culture moderates the relationship between interdependence and face recognition
Bibliographic record
Abstract
Recent theory suggests that face recognition accuracy is affected by people's motivations, with people being particularly motivated to remember ingroup versus outgroup faces. In the current research we suggest that those higher in interdependence should have a greater motivation to remember ingroup faces, but this should depend on how ingroups are defined. To examine this possibility, we used a joint individual difference and cultural approach to test (a) whether individual differences in interdependence would predict face recognition accuracy, and (b) whether this effect would be moderated by culture. In Study 1 European Canadians higher in interdependence demonstrated greater recognition for same-race (White), but not cross-race (East Asian) faces. In Study 2 we found that culture moderated this effect. Interdependence again predicted greater recognition for same-race (White), but not cross-race (East Asian) faces among European Canadians; however, interdependence predicted worse recognition for both same-race (East Asian) and cross-race (White) faces among first-generation East Asians. The results provide insight into the role of motivation in face perception as well as cultural differences in the conception of ingroups.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".