Brain Network Organization During Mindful Acceptance of Emotions
Bibliographic record
Abstract
Abstract Individuals use various strategies to cope with challenging emotions such as anxiety. Mindful acceptance involves broadening attentional scope and fully experiencing present moment sensory feelings (whether pleasant or unpleasant) without judgment or elaboration. In contrast, narrative-evaluation involves focusing on a narrow band of sensory experience and generating an elaborate narrative about the meaning and desirability of one’s emotional feelings. The current study examined brain network organization during these strategies using graph theoretic analyses. We used a naturalistic task paradigm in which participants reflected on an anxiety-provoking issue from their personal lives and adopted each strategy in different blocks. Compared to narrative-evaluation, mindful acceptance was associated with: (i) increased global network connectivity; (ii) greater integration of interoceptive regions (mid and posterior insula) into large-scale networks; (iii) reorganization of motivational circuits including a shift in the striatum’s network assignment from the default network to the salience network; and (iv) a shift from default network to frontoparietal control network (FPCN) regions as central hubs that coordinate information flow. Functional connectivity patterns within the left FPCN were associated with acceptance reports. Thus, broadening attentional scope during mindful acceptance is supported by a more globally interconnected neural landscape, as well as greater information flow through FPCN regions that underlie metacognitive awareness and cognitive control.
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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 | 0.000 |
| 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".