Overnight emotional adaptation to negative stimuli is altered by REM sleep deprivation and is correlated with intervening dream emotions
Bibliographic record
Abstract
Rapid eye movement (REM) sleep and dreaming may be implicated in cross-night adaptation to emotionally negative events. To evaluate the impact of REM sleep deprivation (REMD) and the presence of dream emotions on a possible emotional adaptation (EA) function, 35 healthy subjects randomly assigned to REMD (n = 17; mean age 26.4 +/- 4.3 years) and control (n = 18; mean age 23.7 +/- 4.4 years) groups underwent a partial REMD and control nights in the laboratory, respectively. In the evening preceding and morning following REMD, subjects rated neutral and negative pictures on scales of valence and arousal and EA scores were calculated. Subjects also rated dream emotions using the same scales and a 10-item emotions list. REMD was relatively successful in decreasing REM% on the experimental night, although a mean split procedure was applied to better differentiate subjects high and low in REM%. High and low groups differed - but in a direction contrary to expectations. Subjects high in REMD% showed greater adaptation to negative pictures on arousal ratings than did those low in REMD% (P < 0.05), even after statistically controlling sleep efficiency and awakening times. Subjects above the median on EA(valence) had less intense overall dream negativity (P < 0.005) and dream sadness (P < 0.004) than subjects below the median. A correlation between the emotional intensities of the morning dream and the morning picture ratings supports a possible emotional carry-over effect. REM sleep may enhance morning reactivity to negative emotional stimuli. Further, REM sleep and dreaming may be implicated in different dimensions of cross-night adaptation to negative emotions.
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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 | 0.001 |
| 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".