Neural fatigue due to intensive learning is reversed by a nap but not by quiet waking
Bibliographic record
Abstract
Do brain circuits become fatigued due to intensive neural activity or plasticity? Is sleep necessary for recovery? Well-rested subjects trained extensively in a visuo-motor rotation learning task (ROT) or a visuo-motor task without rotation learning (MOT), followed by sleep or quiet wake. High-density electroencephalography showed that ROT training led to broad increases in EEG power over a frontal cluster of electrodes, with peaks in the theta (mean ± SE: 24% ± 6%, p = 0.0013) and beta ranges (10% ± 3%, p = 0.01). These traces persisted in the spontaneous EEG (sEEG) between sessions (theta: 42% ± 8%, p = 0.0001; beta: 35% ± 7%, p = 0.002) and were accompanied by increased errors in a motor test with kinematic characteristics and neural substrates similar to ROT (81.8% ± 0.8% vs. 68.2% ± 2.3%; two-tailed paired t-test: p = 0.00001; Cohen's d = 1.58), as well as by score increases of subjective task-specific fatigue (4.00 ± 0.39 vs. 5.36 ± 0.39; p = 0.0007; Cohen's d = 0.60). Intensive practice with MOT did not affect theta sEEG or the motor test. A nap, but not quiet wake, induced a local sEEG decrease of theta power by 33% (SE: 8%, p = 0.02), renormalized test performance (70.9% ± 2.9% vs 79.1% ± 2.7%, p = 0.018, Cohen's d = 0.85), and improved learning ability in ROT (adaptation rate: 71.2 ± 1.2 vs. 73.4 ± 0.9, p = 0.024; Cohen's d = 0.60). Thus, sleep is necessary to restore plasticity-induced fatigue and performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".