Sleep, Neurocognitive Function, Learning, and Memory
Bibliographic record
Abstract
Abstract Sleep has been shown to play a central role in the optimal consolidation of memory and maintenance of neurocognitive function. This chapter first provides a comprehensive review of the various sleep-specific electrophysiological markers related to learning and memory consolidation. Second, accumulating evidence for the role of sleep in declarative memory consolidation will be discussed, differentiating between the contribution of slow wave–spindle–ripple coupling during non-rapid-eye-movement (NREM) sleep and the specific contribution of rapid-eye-movement (REM) sleep in memory consolidation. Third, the authors explore the contribution of sleep in cognitively simple motor procedural memories, suggesting that NREM sleep and, critically, sleep spindles play a central role in this process. Finally, the specific case of cognitively complex procedural memory consolidation will be considered, suggesting that, as compared to the case of simple procedural memories, REM sleep may be involved when the task demands are more challenging and cognitively complex (e.g., new rules, novel strategies, problem-solving skills), whereas spindles are involved when the task requires well-established skills (e.g., cognitively simple motor skills).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".