Differences in dream content during a daytime nap and the relationship of the dream content to procedural learning in Vipassana meditators and controls.
Bibliographic record
Abstract
Vipassana meditation is characterized by observing bodily sensations, developing emotional and attentional stability and promoting pro-social qualities. Whether these qualities are also reflected in dream content is not currently known. Evidence relating dream content with sleep-depending learning is mixed: some studies suggest that dreaming of a task is beneficial for improvement, while others find no such effect. This study aimed at investigating whether meditators have qualitatively different dreams than controls; whether meditators incorporate a procedural learning task more often than controls; and whether dreaming about the task is related to better post-sleep performance on the task.20 meditators and 20 controls slept for a daytime nap at the laboratory. Prior to sleep and upon awakening they completed a procedural learning task. Dream reports were collected at sleep onset and upon awakening (REM/N2 sleep). Dreams were then scored for qualities associated with meditation practice and for incorporations of the procedural task and of the laboratory. Meditators had longer dreams, slightly more references to the body and friendlier and more compassionate interactions with dream characters. Dreams of meditation practitioners were not more lucid than those of controls. Meditators did not incorporate the learning task or laboratory into dream content more often than controls, and no relationship was found between dream content and performance on a procedural task. In control participants, in contrast, incorporating task or laboratory in REM/N2 dreams was associated with improvement on the task, but incorporations at sleep onset were associated with slightly worse performance on the task.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".