Changes in Sleep Architecture following Motor Learning Depend on Initial Skill Level
Bibliographic record
Abstract
Previous research has linked both rapid eye movement (REM) sleep and Stage 2 sleep to procedural memory consolidation. The present study sought to clarify the relationship between sleep stages and procedural memory consolidation by examining the effect of initial skill level in this relationship in young adults. In-home sleep recordings were performed on participants before and after learning the pursuit rotor task. We divided the participants into low- and high-skill groups based on their initial performance of the pursuit rotor task. In high-skill participants, there was a significant increase in Stage 2 spindle density after learning, and there was a significant correlation between the spindle density that occurred after learning and pursuit rotor performance at retest 1 week later. In contrast, there was a significant correlation between changes in REM density and performance on the pursuit rotor task during retest 1 week later in low-skill participants, although the actual increase in REM density failed to reach significance in this group. The results of the present study suggest the presence of a double dissociation in the sleep-related processes that are involved in procedural memory consolidation in low- and high-skill individuals. These results indicate that the changes in sleep microarchitecture that take place after learning depend on the initial skill level of the individual and therefore provide validation for the model proposed by Smith et al. [Smith, C. T., Aubrey, J. B., & Peters, K. R. Different roles for REM and Stage 2 sleep in motor learning. Psychologica Belgica, 44, 79-102, 2004]. Accordingly, skill level is an important variable that needs to be considered in future research on sleep and memory consolidation.
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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".