Sleep Enhances Consolidation of Memory Traces for Complex Problem-Solving Skills
Bibliographic record
Abstract
Sleep consolidates memory for procedural motor skills, reflected by sleep-dependent changes in the hippocampal-striatal-cortical network. Other forms of procedural skills require the acquisition of a novel strategy to solve a problem, which recruit overlapping brain regions and specialized areas including the caudate and prefrontal cortex. Sleep preferentially benefits strategy and problem-solving skills over the accompanying motor execution movements. However, it is unclear how acquiring new strategies benefit from sleep. Here, participants performed a task requiring the execution of a sequence of movements to learn a novel cognitive strategy. Participants performed this task while undergoing fMRI before and after an interval of either a full night sleep, a daytime nap, or wakefulness. Participants also performed a motor control task, which precluded the opportunity to learn the strategy. In this way, we subtracted motor execution-related brain activations from activations specific to the strategy. The sleep and nap groups experienced greater behavioral performance improvements compared to the wake group on the strategy-based task. Following sleep, we observed enhanced activation of the caudate in addition to other regions in the hippocampal-striatal-cortical network, compared to wakefulness. This study demonstrates that sleep is a privileged time to enhance newly acquired cognitive strategies needed to solve problems.
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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) | 0.003 | 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".