Asymmetric cognitive learning mechanisms underlying the persistence of intergroup bias
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Intergroup bias, the tendency to favor ingroups and be hostile towards outgroups, underlies many societal problems and persists even when intergroup members interact and share experiences. Here we study the way cognitive learning processes contribute to the persistence of intergroup bias. Participants played a game with ingroup and outgroup bot-players that entailed collecting stars and could sacrifice a move to zap another player. We found that intergroup bias persisted as participants were more likely to zap outgroup players, regardless of their zapping behavior. Using a computational model, we found that this bias was caused by asymmetries in three learning mechanisms. Participants had a greater prior bias to zap out-group players, they learned more readily about the negative behavior of out-groups and were less likely to attribute the positive behavior of one out-group player to other out-group players. Our results uncover the way cognitive social learning mechanisms shape and confound intergroup dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it