Relief Expectation and Sleep
Bibliographic record
Abstract
Originally, a role for sleep in learning and memory has been advocated following the observation of sleep-dependent performance enhancements at simple procedural tasks. With the investigation of a variety of cognitive and behavioral abilities, multiple stages of memory were further suggested to benefit from the off-line reprocessing believed to occur during specific sleep stages. In particular, REM sleep has been implicated in the integration of new information into associative networks as well as in the abstraction and generalization of implicit rules allowing adaptive behaviors. In a recent study, we extended these observations by demonstrating that the mediating effect of expectation on placebo-induced analgesia is strengthened by sleep, and that the individual amount of REM sleep is predictive of the relief expected on the next morning. However, this relation is strongly modulated by the level of concordance between expectations and sensory information available prior to sleep. As placebo responses derive from the learned association between contextual cues and subsequent relief, these results are discussed in relation to the proposed roles of REM sleep in the integrative stages of memory processing. In light of the responsiveness of REM sleep to waking events, its expression is also proposed to reflect the cognitive demand associated with the offline reprocessing of information necessary for the assimilation of new expectations to one's belief system.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".