Finding Positive Meaning in Negative Experiences Engages Ventral Striatal and Ventromedial Prefrontal Regions Associated with Reward Valuation
Bibliographic record
Abstract
Neuroimaging research has identified systems that facilitate minimizing negative emotion, but how the brain is able to transform the valence of an emotional response from negative to positive is unclear. Behavioral and psychophysiological studies suggest a distinction between minimizing reappraisal, which entails diminishing the arousal elicited by negative stimuli, and positive reappraisal, which instead changes the emotional valence of arousal from negative to positive. Here we show that successful minimizing reappraisal tracked with decreased activity in the amygdala, but successful positive reappraisal tracked with increased activity in regions involved in computing reward value, including the ventral striatum and ventromedial pFC (vmPFC). Moreover, positive reappraisal enhanced positive connectivity between vmPFC and amygdala, and individual differences in positive connectivity between vmPFC and amygdala, ventral striatum, dorsomedial pFC, and dorsolateral pFC predicted greater positive reappraisal success. These data broaden models of emotion regulation as quantitative dampening of negative emotion and identify activity in a network of brain valuation, arousal, and control regions as a neural basis for the ability to create positive meaning from negative experiences.
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 | 0.000 |
| 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".