Neurophysiological mechanisms of focused attention meditation: A scoping systematic review
Bibliographic record
Abstract
Focused attention meditation (FA) is a foundational and widely studied practice that cultivates sustained concentration by focusing on a specific object, such as the breath, while disengaging from distractions. Numerous studies have investigated the neurophysiological mechanisms of FA, examining aspects such as spectral power, connectivity patterns, and neural entropy. However, despite this extensive research, clarity regarding the methodological approaches and key findings in this field remains limited. This scoping systematic review aimed to collate and interpret key information from electroencephalography (EEG) and magnetoencephalography (MEG) studies on FA, with a focus on study population composition, experimental design, and neurophysiological outcomes. Our findings revealed substantial heterogeneity in participant characteristics, potentially contributing to variability in neurophysiological results, while the choice of FA tasks and control conditions was relatively consistent. In terms of neurophysiological outcomes, consistent trends indicate that FA is associated with increased power in the alpha, beta, and gamma bandwidths, as well as heightened complexity and reduced criticality measures. Based on the findings of this review, we propose several methodological recommendations to improve the quality of future research. Additionally, we identified significant evidence gaps when considering the whole body of research, including the limited use of MEG and a lack of longitudinal studies, pointing to areas for future investigation. Overall, this review provides a firm grounding for the study of the neurophysiology of FA, as well as the study of advanced meditation and neuroscience-informed meditative development.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".