Similar States, Different Paths: Neurodynamics of diverse meditation techniques
Bibliographic record
Abstract
Abstract Meditation encompasses diverse practices that train attention inward, in contrast to externally oriented task states. However, the neurodynamic features distinguishing meditative states from non-meditative states across traditions remain unclear. We analyzed high-density EEG data (N=170; 121 advanced meditators, 49 controls) across four traditions: Vipassana, Brahma Kumaris Raja Yoga, Heartfulness, and Isha Yoga. EEG features spanned oscillatory, aperiodic, nonlinear, and timescale components. Using random forest classifiers, we distinguished meditative from non-meditative states with robust classification performance (91%). Nonlinear features contributed the most, suggesting a core neurodynamic profile. Classification performance was higher in advanced meditators (92%) than in controls (85%), with distinct feature importance: nonlinear and aperiodic features dominated in meditators, and oscillatory and timescale features in controls. Each tradition showed distinct neurodynamic profiles, indicating technique-specific constellations. Our findings revealed shared yet distinct neurodynamic signatures across meditation techniques, suggesting that multiple neurodynamic pathways lead to meditative states.
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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".