CNS arousal and neurobehavioral performance in a short‐term sleep restriction paradigm
Bibliographic record
Abstract
Few studies have investigated waking electrophysiological measures of arousal during sleep restriction. This study examined electroencephalogram (EEG) activity and performance during a 96-hour laboratory protocol where participants slept a baseline night (8 h), were randomly assigned to 3-, 5-, or 8-hour sleep groups for the next two nights sleep restriction (SR1, SR2), and then slept a recovery night (8 h). There were dose-dependent deficits on measures of mood, sleepiness, and reaction time that were apparent during this short-term bout of sleep restriction. The ratio of alpha to theta EEG recorded at rest indicated dose-dependent changes in CNS arousal. At 9:00 hours, both the 3- and 5-hour groups showed EEG slowing (sleepiness) during restriction, with the 3-hour group exhibiting greater deficits. Later in the day at 13:00 hours, the 5-hour group no longer exhibited EEG slowing, but the extent of slowing was more widespread across the scalp for the 3-hour group. High-frequency EEG, a measure of effort, was greater on the mornings following sleep restriction. The 5-hour group had increased beta EEG at central-parietal sites following both nights of restriction, whereas the 3-hour group had increased beta and gamma EEG at occipital regions following the first night only. Short-term sleep restriction leads to deficits in performance as well as EEG slowing that correspond to the amount and duration of sleep loss. High-frequency EEG may be a marker of effort or compensation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".