135 One night of mild sleep restriction affects EEG and behavioural measures of vigilance
Bibliographic record
Abstract
Abstract Introduction Much is known about the behavioural and cognitive consequences of chronic sleep loss but relatively little is known about the changes in brain activity associated with reduced vigilance after mild and acute sleep loss. Mild and acute sleep loss is generally thought to be innocuous despite research showing emotional processing, visual attention and behavioural responding are all negatively impacted by even small amounts of sleep loss. The current study investigated behavioural, cognitive, and electrophysiological consequences of mild (i.e., a couple of hours) and acute (i.e., a single night) sleep loss via simultaneous behavioural and physiological measures of vigilance. Methods Participants (N = 23; 18 females, Mage = 22 ± 3 years) came into the lab (from ~12 pm to 3 pm) for two testing days after sleeping from 1 am to 6 am (Sleep Restriction), or from 12 am to 9 am (Normally Rested). Brain activity was recorded using electroencephalography (EEG) from 15 scalp derivations, while vigilance was assessed simultaneously using the psychomotor vigilance task (PVT). Results Vigilance was reduced in the Sleep Restricted vs. Normally Rested condition, (F(1,22)=9.02, p=0.007). This was exacerbated over the course of performing the PVT, (F(5,110)=8.12, p<0.001). Sleep Restriction also resulted in increased intensity of alpha burst activity compared to the Normally Rested condition (F(1,20)=6.19, p=0.022). Lastly, EEG spectral power differed between restriction sleep conditions across deepening stages of sleep onset, particularly for frequencies that reflect arousal e.g., delta, alpha and beta activity (F(1,20)>5.52, p<0.029). Conclusion These results suggest that even a small amount of sleep loss, occurring on only one night significantly reduces vigilance and impacts the physiology of the brain in ways that reflect reduced arousal. Understanding the neural correlates and cognitive processes associated with sleep loss may lead to important advancements in identifying and preventing potentially deleterious or dangerous, sleep-related lapses in vigilance (e.g., in the classroom, workplace), and when lapses in vigilance can be life-threatening (e.g., while driving). Support (if any):
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".