Neural moderators of social influence susceptibility on drinking
Bibliographic record
Abstract
Objectives: Conversations shape health behaviors. However, individuals vary in susceptibility to conversational influence and in their neural responses that track such influences. We examined whether activity in brain regions associated with social rewards and making sense of others’ minds was related to drinking following conversations about alcohol. We studied ten social groups of college students (total N = 104 students; 4760 total observations) across two University campuses. Methods: We collected whole-brain fMRI data while participants viewed photographs of the faces of peers with whom they tended to drink at varying frequencies (i.e., drinking vs. non-drinking peers). Next, using mobile diaries, we tracked alcohol-related conversations and alcohol use twice daily for 28 days. Results: On average, talking about alcohol was associated with a higher probability of drinking the following day. Controlling for baseline drinking, participants who responded more strongly to drinking peers—with whom they drank more frequently— in brain regions associated with social rewards and mentalizing showed higher susceptibility to conversational influence on drinking. Conversely, stronger neural responses to non-drinking peers—with whom they drank less frequently—decoupled the link between alcohol conversations and next-day drinking. Conclusions: These findings conceptually replicate prior findings linking peer conversations and drinking behavior in a longitudinal, ecologically valid setting, and provide new evidence that brain sensitivity to peers may exacerbate or buffer conversational susceptibility to drink.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".