Neural responses to social acceptance predict behavioral adjustments following peer feedback in the context of a real‐time social interaction task
Bibliographic record
Abstract
Strong social connections are important predictors of both mental and physical health. The ability to effectively process social feedback from other people and adjust behavior accordingly is a critical part of skillfully navigating the social landscape. However, relatively few studies have considered neural systems driving these behavioral adjustments. In this study, 254 participants engaged in a peer interaction game while electroencephalogram (EEG) was recorded. In this game, participants repeatedly "interact" with a small set of virtual peers over a series of rounds, in which they provide feedback to their peers, and receive feedback from them in turn. A reward-sensitive event-related potential called the Reward Positivity (RewP) was extracted from the EEG following positive feedback from peers, and multilevel modeling was used to examine whether the RewP moderated associations between the feedback participants received during the task and their subsequent behavior. Participants were more likely to accept coplayers who had previously voted to accept them, and to like coplayers who had voted to accept them on the same round. A larger RewP was associated with a stronger tendency to modify behavior following feedback from peers, both in terms of voting behavior and expressions of liking. These data suggest that initial neural responsiveness to reward within 300 ms of positive social feedback may guide social behaviors. Thus, this line of research represents an important step toward a more complete understanding of the ways in which neural responses to feedback are involved in human social behaviors.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".