Contribution of REM sleep and N2 sleep spindles to procedural memory consolidation in Vipassana meditators and non-meditating controls.
Bibliographic record
Abstract
Study objectives: Rapid eye movement (REM) sleep, non-rapid eye movement (NREM) sleep, and sleep spindles are all implicated in the consolidation of procedural memories. The relative contributions of sleep stages and sleep spindles was previously shown to depend on individual differences in task processing. Experience with Vipassana meditation is one such individual difference that has not been investigated in relation to sleep. Vipassana meditation is a form of mental training that enhances proprioceptive and somatic awareness and alters attentional style. The goal was thus to examine a potential moderating role for Vipassana meditation experience on sleep-dependent procedural memory consolidation.Methods: Groups of Vipassana meditation practitioners (N=20) and matched meditation-naïve controls (N=20) slept for a single daytime nap in the laboratory. Before and after the nap they completed a procedural task on the Wii Fit balance platform.Results: Meditators performed slightly better on the task before the nap, but the two groups improved similarly after sleep. The groups showed different patterns of sleep-dependent procedural memory consolidation: in meditators task learning was negatively correlated with density of fast and positively correlated with density of slow occipital spindles, while in controls task improvement was associated with increases in REM sleep. Meditation practitioners had a lower density of sleep spindles, especially in occipital regions.Conclusions: Results suggest that neuroplastic changes associated with sustained meditation practice may alter overall sleep architecture and reorganize sleep-dependent patterns of memory consolidation. The lower density of spindles in meditators may mean that meditation practice compensates for some of the memory functions of sleep.
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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 | 0.000 |
| 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) | 0.003 | 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".