Novel Self-categorization Overrides Racial Bias: A Multi-level Approach to Intergroup Perception and Evaluation
Bibliographic record
Abstract
People engage in a constant and reflexive process of categorizing others according to their race, gender, age or other salient social category. Decades of research have shown that social categorization often elicits stereotypes, prejudice, and discrimination. Social perception is complicated by the fact that people have multiple social identities and self-categorization with these identities can shift from one situation to another, coloring perceptions and evaluations of the self and others. This dissertation provides evidence that self-categorization with a novel group can override ostensible stable and pervasive racial biases in memory and evaluation and examines the neural substrates that mediate these processes. Experiment 1 shows that self-categorization with a novel mixed-race group elicited liking for ingroup members, regardless of race. This preference for ingroup members was mediated by the orbitofrontal cortex – a region of the brain linked to subjective valuation. Participants in novel groups also had greater fusiform and amygdala activity to novel ingroup members, suggesting that these regions are sensitive to the current self-categorization rather than features associated with race. Experiment 2 shows that preferences for ingroup members are evoked rapidly and spontaneously, regardless of race, indicating that ingroup bias can override automatic racial bias. Experiment 3 provides evidence that preferences for ingroup members are driven by ingroup bias rather than outgroup derogation. Experiment 4 shows that self-categorization increases memory for ingroup members eliminating the own-race memory bias. Experiment 5 provides direct evidence that fusiform activity to ingroup members is associated with superior memory for ingroup members. This study also shows greater amygdala activity to Black than White faces who are unaffiliated with either the ingroup or outgroup, suggesting that social categorization is flexible, shifting from group membership to race within a given social context. These five experiments illustrate that social perception and evaluation are sensitive to the current self-categorization – however minimal – and characterized by ingroup bias. This research also offers a relatively simple approach for erasing several pervasive racial biases. This multi-level approach extends several theories of intergroup perception and evaluation by making explicit links between self-categorization, neural processes, and social perception and evaluation.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".