Eye movement patterns correlate with overt emotional behaviours in rapid eye movement sleep
Bibliographic record
Abstract
Growing evidence suggests that sleep plays a key role in regulating emotions. Rapid eye movements (REMs) in REM sleep could be associated with dreams emotions, but supporting evidence is indirect. To highlight this association, we studied the REM sleep during video-polysomnography of 20 subjects with REM sleep behaviour disorder (RBD), a model of enacted dreams offering direct access to the emotional content of the sleeper (face expression, speeches, behaviour). Video and the electro-oculography recordings were divided into 3 s time intervals and classified as non-behavioural, or behavioural (neutral, positive or negative emotions), and as containing no eye movements (EMs), slow eye movements (SEMs) or REMs (isolated or bursts). Compared to the absence of EMs, neutral behaviours successively increased in the presence of SEMs (odd ratio, OR = 1.4), then isolated REMs (OR = 2.8) and then REM bursts (OR = 4.6). Positive behaviours increased with SEMs (OR = 2.8) but did not increase further with isolated REMs (OR = 2.8) and REM bursts (OR = 3). Negative behaviours were absent with SEMs, increased with isolated REMs (OR = 2.6) and further with REM bursts (OR = 10.1). These results support an association between REMs and SEMs, and dream 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".