Memory Sources Associated with REM and NREM Dream Reports Throughout the Night: A New Look at the Data
Bibliographic record
Abstract
The data from three previously published studies on the memory sources of dreams, representing nine different moments of awakening throughout the night, are re-examined. In the original studies, elicited reports were recorded and segmented online into thematic units. The segmented reports were played back to Ss who were asked to identify memory sources or to associate to each segment. Memory sources were classified as episodic, semantic, or abstract self-references. In the meta-analysis and re-analyses reported here, the mean percentages of episodic memory sources are plotted separately for NREM and REM awakenings throughout the night. Within stages, neither NREM nor REM mean percentages differ significantly from each other, whereas between stages the mean percentage of episodic memory sources is significantly greater for NREM than for REM. Even when the correlation between report length and sleep stage is controlled by computing memory source density, the stage effect throughout the night persists for episodic memory sources. The relatively flat episodic memory curves for both NREM and REM indicate a rather constant recruitment of episodic memory sources throughout the night. No stage effect was found for strictly semantic memory sources. When semantic memory was defined generically, however, to include all non-spatio-temporal, "unmarked," information of self as well as of world, significantly more generic semantic memory sources derived from REM than from NREM reports, though not when corrected for the length of dream reports.
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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 | 0.019 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".