The dynamics of emotional behaviors in rapid eye movement sleep
Bibliographic record
Abstract
Dream's emotions could exert a major role in desensitizing negative emotions. Studying emotional dynamics (how emotions fluctuate across time) during rapid eye movement (REM) sleep could provide some insight into this function. However, studies so far have been limited to dream reports. To bypass this limit, REM sleep behavior disorder (RBD), in which participants enact their dreams, enables direct access to overt emotional dream behaviors (such as facial expressions and speeches). In total, 17 participants with RBD, and 39.7 h of REM sleep video were analyzed. The frequency of emotional behaviors did not differ between REM sleep episodes of early and late night. Within individual REM sleep episodes, emotional behaviors exhibited a biphasic temporal course, including an increased frequency for the first 10 min, followed by a progressive decrease. The negative emotional behaviors occurred earlier (mean time: 11.3 ± 10 min) than positive (14.4 ± 10.7 min) and neutral behaviors (16.4 ± 11.8 min). Emotional behaviors of opposing (negative and positive) valences were observed in 31% (N = 14) of episodes containing at least one emotional behavior, and were separated by a median time of 4.2 [1.1-10.9] min. The biphasic temporal course of behaviors in REM sleep could include the generation reactivation of emotional content during the ascending phase, followed by processing and extinction during the descending phase. The earlier occurrence time of negative emotional behavior suggests that negative emotions may need to be processed first. The rapid succession of emotions of opposite valence could prevent prolonged periods of negative emotions and eventually nightmares.
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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 | 0.001 | 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) | 0.001 | 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".