Sleep quality influences subsequent motor skill acquisition.
Bibliographic record
Abstract
While the influence of sleep on motor memory consolidation has been extensively investigated, its relation to initial skill acquisition is less well understood. The purpose of the present study was to investigate the influence of sleep quality and quantity on subsequent motor skill acquisition in young adults without sleep disorders. Fifty-five healthy adults (mean age = 23.8 years; 34 women) wore actigraph wristbands for 4 nights, which provided data on sleep patterns before the experiment, and then returned to the laboratory to engage in a motor sequence learning task (explicit 5-item finger sequence tapping task). Indicators of sleep quality and quantity were then regressed on a measure of motor skill acquisition (Gains Within Training, GWT). Wake After Sleep Onset (WASO; i.e., the total amount of time the participants spent awake after falling asleep) was significantly and negatively related to GWT. This effect was not because of general arousal level, which was measured immediately before the motor task. Conversely, there was no relationship between GWT and sleep duration or self-reported sleep quality. These results indicate that sleep quality, as assessed by WASO and objectively measured with actigraphy before the motor task, significantly impacts motor skill acquisition in young healthy adults without sleep disorders. (PsycINFO Database Record
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".