Lucid Dreaming: Not Just Awareness, but Agency
Bibliographic record
Abstract
During lucid dreaming (LD), dreamers are aware that they are dreaming and may be able to influence the oneiric content. There has been recent debate about the relative importance of the ability to influence the dream and having agency over the pure awareness of dreaming. To underline this, we examined the associations of lucid dreams without agency (LD-Ag) and lucid dreams with agency (LD + Ag) to sleep and mental health problems and long COVID during the pandemic. We collected data in 16 countries on four continents from May to December 2021 on 10,715 subjects. Logistic regression was performed to predict LD-Ag and LD + Ag, with a sample of 8133 participants. We found that 30% of the participants frequently knew they were dreaming during the pandemic. About half of those (17%) reported that they could influence their dreams. Female gender and anxiety symptoms were negatively associated with LD + Ag. Dream recall, nightmares, insomnia, dream enactment behaviour (DEB), sleep vocalisation, short and long COVID and PTSD were positively associated with LD + Ag. Old age, dream recall, nightmares and anxiety symptoms were positively associated with LD-Ag, while short sleep length, being an evening type, and short COVID were negatively associated with LD-Ag. The different associations for LD-Ag and LD + Ag suggest that they may be distinct sleep states. This is also the first study to show that both COVID-19 and long COVID are associated with LD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".