Avoidance begets avoidance: A computational account of negative stereotype persistence.
Bibliographic record
Abstract
Research on stereotype formation has proposed a variety of reasons for how inaccurate stereotypes arise, focusing largely on accounts of motivation and cognitive efficiency. Here, we instead consider how stereotypes arise from basic processes of approach and avoidance in social learning. Across five studies, we show that initial negative interactions with some members of a group can cause subsequent avoidance of the entire group, and that this avoidance perpetuates stereotypes in two ways. First, when information gain is contingent on approaching the target, avoidance restricts the information available with which to update one's beliefs. Second, computational models that consider the perceiver's full reinforcement history demonstrate that avoidance directly reinforces itself, such that initial avoidance of group members increases the probability of later acts of avoidance toward that group. Finally, we find initial evidence for a potential dissociation between behavior and explicit beliefs, with avoidance reinforcing avoidant behaviors without necessarily affecting self-reported beliefs. Overall, these results suggest that avoidance behaviors toward members of social groups can perpetuate inaccurate negative beliefs and expectations about those groups, such that initial interactions with a group have a compounding effect on overall impressions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".