Linking social reward responsiveness and affective responses to the social environment: An ecological momentary assessment study
Bibliographic record
Abstract
Social support is a key predictor of well-being, but not everyone experiences mental health benefits from receiving it. However, given that a growing number of interventions are based on social support, it is crucial to identify the features that make individuals more likely to benefit from social ties. Emerging evidence suggests that neural responses to positive social feedback (i.e., social reward) might relate to individual differences in social functioning, but potential mechanisms linking these neural responses to psychological outcomes are yet unclear. This study examined whether neural correlates of social reward processing, indexed by the reward positivity (RewP), relate to individuals' affective experience following self-reported real-world positive social support events. To this aim, 193 university students (71% females) underwent an EEG assessment during the Island Getaway task and completed a 10-day ecological momentary assessment where participants reported their positive and negative affects (PA, NA) nine times a day and the count of daily positive and negative events. Experiencing a higher number of social support positive events was associated with higher PA. The RewP moderated this association, such that individuals with greater neural response to social feedback at baseline had a stronger positive association between social support positive events count and PA. Individual differences in the RewP to social feedback might be one indicator of the likelihood of experiencing positive affect when receiving social support.
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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 | 0.002 |
| 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) | 0.001 | 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".