A non-elaborative mental stance and decoupling of executive and pain-related cortices predicts low pain sensitivity in Zen meditators
Bibliographic record
Abstract
Concepts originating from ancient Eastern texts are now being explored scientifically, leading to new insights into mind/brain function. Meditative practice, often viewed as an emotion regulation strategy, has been associated with pain reduction, low pain sensitivity, chronic pain improvement, and thickness of pain-related cortices. Zen meditation is unlike previously studied emotion regulation techniques; more akin to 'no appraisal' than 'reappraisal'. This implies the cognitive evaluation of pain may be involved in the pain-related effects observed in meditators. Using functional magnetic resonance imaging and a thermal pain paradigm we show that practitioners of Zen, compared to controls, reduce activity in executive, evaluative and emotion areas during pain (prefrontal cortex, amygdala, hippocampus). Meditators with the most experience showed the largest activation reductions. Simultaneously, meditators more robustly activated primary pain processing regions (anterior cingulate cortex, thalamus, insula). Importantly, the lower pain sensitivity in meditators was strongly predicted by reductions in functional connectivity between executive and pain-related cortices. Results suggest a functional decoupling of the cognitive-evaluative and sensory-discriminative dimensions of pain, possibly allowing practitioners to view painful stimuli more neutrally. The activation pattern is remarkably consistent with the mindset described in Zen and the notion of mindfulness. Our findings contrast and challenge current concepts of pain and emotion regulation and cognitive control; commonly thought to manifest through increased activation of frontal executive areas. We suggest it is possible to self-regulate in a more 'passive' manner, by reducing higher-order evaluative processes, as demonstrated here by the disengagement of anterior brain systems in meditators.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".