Sleep Contribution to Motor Memory Consolidation: A Motor Imagery Study
Bibliographic record
Abstract
STUDY OBJECTIVES: Sleep is known to enhance performance following physical practice (PP) of a new sequence of movements. Apart from a pilot study, it is still unknown whether a similar sleep-dependent consolidation effect can be observed following motor imagery (MI) and whether this mnemonic process is related to MI speed. DESIGN: Counterbalanced within-subject design. SETTING: The laboratory. PARTICIPANTS: Thirty-two participants. INTERVENTIONS: PP, real-time MI, fast MI, and NoSleep (control) groups. MEASUREMENTS AND RESULTS: Subjects practiced an explicitly known sequence of finger movements, and were assigned to PP, real-time MI, or fast MI, in which they intentionally imagined the sequence at a faster pace. A NoSleep group subjected to real-time MI, but without any intervening sleep, was also tested. Performance was evaluated before practice, as well as prior to, and after a night of sleep or a similar time interval during the daytime. Compared with the NoSleep group, the results revealed offline gains in performance after sleep in the PP, real-time MI, and fast MI groups. There was no correlation between a measure of underestimation of the time to imagine the motor sequence and the actual speed gains after sleep, neither between the ease/difficulty to form mental images and performance gains. CONCLUSIONS: These results provide evidence that sleep contributes to the consolidation of motor sequence learning acquired through MI and further suggests that offline delayed gains are not related to the MI content per se. They extend our previous findings and strongly confirm that performance enhancement following MI is sleep dependent.
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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) | 0.000 | 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".