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Relief Expectation and Sleep

2010· article· en· W2315481959 on OpenAlexafffund
Danièle Laverdure-Dupont, Pierre Rainville, Jacques Montplaisir, Gilles Lavigne

Bibliographic record

VenueReviews in the Neurosciences · 2010
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsSleep (system call)Cognitive psychologyPsychologyCognitionEpisodic memoryNon-rapid eye movement sleepAssociative learningNapEye movementComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.073
GPT teacher head0.354
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2010
Admission routes2
Has abstractyes

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