Sleep Enhances Off-line Spatial and Temporal Motor Learning After Stroke
Bibliographic record
Abstract
BACKGROUND: Individuals with chronic stroke demonstrate sleep-dependent off-line motor learning of a continuous tracking task. However, it remains unclear which aspects of learned movements are preferentially enhanced by sleep (ie, spatial accuracy and/or the time lag of tracking). OBJECTIVE: . The purpose of this study was to investigate whether spatial tracking accuracy, temporal tracking accuracy, or both are enhanced by sleep during off-line motor learning after stroke. METHODS: . Individuals with chronic stroke and control participants either practiced a continuous tracking task in the evening and underwent retention testing the following morning (sleep groups) or practiced the task in the morning and underwent retention testing in the evening (no-sleep groups). RESULTS: . Individuals with stroke who slept between practice and retention testing demonstrated off-line improvements in both spatial and temporal elements of tracking at retention. Participants with a stroke who stayed awake between practice and retention testing did not demonstrate off-line improvements in either spatial tracking accuracy or the time lag of tracking. Control participants did not demonstrate sleep- or time-dependent enhancement of either component of the movement task. Time of day of testing was not a factor in practice related changes in motor performance. CONCLUSION: This study provides the first evidence that sleep enhances motor learning through both improved spatial tracking accuracy and anticipation of upcoming movements, as demonstrated by a reduction in the time lag of tracking in individuals following stroke. We propose that the cerebellum and hippocampus are likely important neural correlates associated with sleep-dependent off-line motor skill learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".