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Record W2475062202 · doi:10.5334/pb.1018

Different Roles for REM and Stage 2 Sleep in Motor Learning: A Proposed Model

2004· article· en· W2475062202 on OpenAlexaff
Carlyle Smith, Jocelyn B. Aubrey, Kevin R. Peters

Bibliographic record

VenuePsychologica Belgica · 2004
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsTrent University
Fundersnot available
KeywordsPsychologySleep (system call)NeurochemicalCognitive psychologyRapid eye movement sleepEye movementNeuroscienceMemory consolidationNon-rapid eye movement sleepMotor skillNeurophysiologyProcedural memoryCognitionComputer scienceHippocampus

Abstract

fetched live from OpenAlex

It is now clear that states of sleep are involved with the off-line memory reprocessing or consolidation of a variety of tasks. The large majority of these sleep sensitive tasks has been of the procedural type, tasks that are usually learned implicitly. It is still unclear which states of sleep are most important. For motor skills tasks, Stage 2 sleep has sometimes been implicated, while at other times the important sleep state appears to be rapid eye movement (REM). This paper is an attempt to more clearly identify the characteristics that differentiate REM-dependent from Stage 2-dependent motor tasks and to examine the nature of the brain state differences between the two stages at the neurophysiological and neurochemical levels. We have developed a model to explain how motor skills tasks involving REM and Stage 2 sleep might be dependent on two separate, but overlapping, neural systems.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0030.006
Open science0.0040.002
Research integrity0.0040.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.082
GPT teacher head0.346
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations77
Published2004
Admission routes1
Has abstractyes

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