The mere presence of an outgroup member disrupts the brain’s feedback-monitoring system
Bibliographic record
Abstract
Much of human learning happens in the social world. A person's social identity-the groups to which they belong, the people with whom they identify-is a powerful cue that can affect our goal-directed behaviors, often implicitly. In the present experiment, we explored the underlying neural mechanisms driving these processes, testing hypotheses derived from social identity theory. In a within-subjects design, participants underwent a minimal group manipulation where they were randomly assigned to an arbitrary ingroup. In two blocks of the experiment, participants were asked to complete a task for money while being observed by an ingroup member and outgroup member separately. Results revealed that being observed by an ingroup or outgroup member led to divergent patterns of neural activity associated with feedback monitoring, namely the feedback-related negativity (FRN). Receiving feedback in the presence of an ingroup member produced a typical FRN signal, but the FRN was dampened while receiving feedback in the presence of an outgroup member. Further, this differentiated neural pattern was exaggerated in people who reported greater intergroup bias. Together, the mere presence of a person can alter how the brain adaptively monitors feedback, impairing the reinforcement learning signal when the person observing is an outgroup member.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".