Executive Functions and the Ability to Sustain Vigilance During Sleep Loss
Bibliographic record
Abstract
INTRODUCTION: There is considerable individual variability in the ability to sustain performance during sleep loss. Preliminary evidence suggests that individuals with higher trait-like activation/functioning of the prefrontal cortex may be less vulnerable to fatigue. METHODS: We tested this hypothesis in a sample of 54 healthy volunteers who were assessed bi-hourly on a variant of the Psychomotor Vigilance Test during 41 h of sleep deprivation. A subset of these subjects, representing the top and bottom 25% of the sample based on their ability to sustain vigilance performance during sleep deprivation, were compared with respect to baseline neurocognitive abilities. RESULTS: The sleep deprivation Resistant group (N = 13) scored significantly higher than the sleep deprivation Vulnerable (N = 13) group on all three baseline tasks assessing prefrontal executive function abilities (letter fluency, Stroop Color-Word test, Color Trails Form 2), whereas no differences were found on non-executive function tasks. Similarly, groups showed no differences on demographic variables including age, education, hand preference, morningness-eveningness preference, global intellectual ability, or pre-study sleep history. DISCUSSION: Findings are consistent with the hypothesis that greater prefrontal/executive functioning may be protective against the adverse effects of sleep deprivation and suggest that baseline executive function testing may prove useful for prediction of resilience during sleep loss.
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 | 0.001 |
| 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) | 0.001 | 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".