Social Groups Prioritize Selective Attention to Faces: How Social Identity Shapes Distractor Interference
Bibliographic record
Abstract
Human faces automatically attract visual attention and this process appears to be guided by social group memberships. In two experiments, we examined how social groups guide selective attention toward in-group and out-group faces. Black and White participants detected a target letter among letter strings superimposed on faces (Experiment 1). White participants were less accurate on trials with racial out-group (Black) compared to in-group (White) distractor faces. Likewise, Black participants were less accurate on trials with racial out-group (White) compared to in-group (Black) distractor faces. However, this pattern of out-group bias was only evident under high perceptual load-when the task was visually difficult. To examine the malleability of this pattern of racial bias, a separate sample of participants were assigned to mixed-race minimal groups (Experiment 2). Participants assigned to groups were less accurate on trials with their minimal in-group members compared to minimal out-group distractor faces, regardless of race. Again, this pattern of out-group bias was only evident under high perceptual load. Taken together, these results suggest that social identity guides selective attention toward motivationally relevant social groups-shifting from out-group bias in the domain of race to in-group bias in the domain of minimal groups-when perceptual resources are scarce.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".