Intrinsic dreams are not produced without REM sleep mechanisms: evidence through elicitation of sleep onset REM periods
Bibliographic record
Abstract
The hypothesis that there is a strict relationship between dreams and a specific rapid eye movement (REM) sleep mechanism is controversial. Many researchers have recently denied this relationship, yet none of their studies have simultaneously controlled both sleep length and depth prior to non-REM (NREM) and REM sleep awakenings, due to the natural rigid order of the NREM--REM sleep cycle. The failure to control sleep length and depth prior to arousal has confounded interpretations of the REM-dreams relationship. We have hypothesised that different physiological mechanisms underlie dreaming during REM and NREM sleep, based on recent findings concerning the specificity of REM sleep for cognitive function. Using the Sleep Interruption Technique, we elicited sleep onset REM periods (SOREMP) from 13 normal subjects to collect SOREMP and sleep onset NREM (NREMP) dreams without the confounds described above. Regression analyses showed that SOREMP dream occurrences were significantly related to the amount of REM sleep, while NREMP dream occurrences were related to arousals from NREM sleep. Dream properties evaluated using the Dream Property Scale showed qualitative differences between SOREMP and NREMP dream reports. These results support our hypothesis and we have concluded that although 'dreaming' may occur during both REM and NREM periods as previous researchers have suggested, the dreams obtained from these distinct periods differ significantly in their quantitative and qualitative aspects and are likely to be produced by different mechanisms.
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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 | 0.002 |
| 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".