Common and distinct neural bases of multiple positive emotion regulation strategies: A functional magnetic resonance imaging study
Bibliographic record
Abstract
Appropriate emotion regulation is crucially involved in mental and physical health. The neural basis of negative but not positive emotion regulation has been well investigated. Several strategies should be compared to elucidate the neural correlates of positive emotion regulation. However, there are no studies on multiple positive emotion regulation strategies. We aimed to investigate the neural correlates of positive emotion regulation with multiple emotion regulation strategies and identify common and differential brain areas involved in positive emotion upregulation. We acquired functional magnetic resonance imaging data from healthy college student volunteers while they upregulated positive emotions through instructed strategies or by viewing positive pictures. The instructed strategies included Attentional Deployment, Cognitive Change, and Response Modulation. These strategies increased subjective positive emotions and activation of the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). Region of interest analysis revealed greater activation of the ventral striatum during positive emotion regulation. There are different networks involved in Cognitive Change and Response Modulation. Our findings indicate that multiple strategies for positive emotion upregulation involve common (e.g., PFC, ACC, and ventral striatum) and unique networks.
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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 | 0.001 |
| 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 | 0.000 |
| 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".