Perceiving ingroup and outgroup faces within and across nations
Bibliographic record
Abstract
The human face is arguably the most important of all social stimuli because it provides so much valuable information about others. Therefore, one critical factor for successful social communication is the ability to process faces. In general, a wide body of social cognitive research has demonstrated that perceivers are better at extracting information from their own-race compared to other-race faces and that these differences can be a barrier to positive cross-race relationships. The primary objective of the present paper was to provide an overview of how people process faces in diverse contexts, focusing on racial ingroup and outgroup members within one nation and across nations. To achieve this goal, we first broadly describe social cognitive research on categorization processes related to ingroups vs. outgroups. Next, we briefly examine two prominent mechanisms (experience and motivation) that have been used to explain differences in recognizing facial identities and identifying emotions when processing ingroup and outgroup racial faces within nations. Then, we explore research in this domain across nations and cultural explanations, such as norms and practices, that supplement the two proposed mechanisms. Finally, we propose future cross-cultural research that has the potential to help us better understand the role of these key mechanisms in processing ingroup and outgroup 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".