“Micro-offline gains” convey no benefit for motor skill learning
Bibliographic record
Abstract
SUMMARY While practising a new motor skill, resting for a few seconds can improve performance immediately after the rest. This improvement has been interpreted as rapid offline learning 1,2 (“micro-offline gains”, MOG), supported by neural replay of the trained movement sequence during rest 3 . Here, we provide evidence that MOG reflect transient performance benefits, partially mediated by motor planning, and not replay-mediated offline learning. In five experiments, participants trained to produce a sequence of finger movements as many times as possible during fixed-duration practice periods. When participants trained during 10-second practice periods, each followed by a 10-second rest period, they produced more correct keypresses during training than participants who trained without taking breaks. However, this benefit vanished within seconds after the end of training, when both groups performed under comparable conditions, revealing similar levels of skill acquisition. This challenges the idea that MOG reflect offline learning, which, if present, should result in sustained performance benefits, compared to training without breaks. Furthermore, sequence-specific replay was not necessary for MOG, given that we observed persistent MOG when participants produced random sequences that never repeated, preventing any effect of (sequence-specific) replay on the performance. Importantly, we observed diminished MOG when participants could not pre-plan the first few movements of an upcoming practice period. We conclude that “micro-offline gains” represent short-lived performance benefits that are partially driven by motor pre-planning, rather than replay-mediated offline learning.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".