Examining the Effects of Sleep Deprivation on Decision-Making: A Scoping Review
Bibliographic record
Abstract
Sleep deprivation (SD) is known to impair cognitive functions, and its effect on vigilance and concentration has been explored extensively. However, its effect on the decision-making ability has been researched to a lesser extent. With varying methodologies and conflicting findings in the literature, the effect of SD on decision-making remains complex and inconsistent. Given the critical implications for fields where decision-making is essential, such as medicine, understanding the impact of SD on this cognitive process is crucial. This scoping review aimed to map the existing literature on the effects of SD on decision-making, identify research trends, and highlight inconsistencies to provide implications for practice and research. The review was conducted following PRISMA-ScR guidelines. Databases searched include APA Psych, Web of Science, Scopus, Academic Search Complete, and PubMed. Inclusion criteria focused on peer-reviewed studies from 2014 onward, exploring the impact of SD on decision-making across various tasks and designs. The final selection included 25 articles, representing 2276 participants. The review may suggest that SD, whether partial or total, impairs decision-making ability, with many studies reporting increased risky decisions. The severity of impairment varied based on the type of decision-making task and the duration of SD. However, a few studies reported insignificant effects, particularly in economic decision-making tasks. Moderating factors, such as gender and the origin of sleep loss (voluntary vs. involuntary), were also identified as influential. Sleep deprivation commonly impairs the decision-making ability, with significant implications for high-stakes professions. However, the variability in findings suggests a need for further research into the moderating factors. The review underscores the importance of adequate sleep for cognitive function and the need for policies that mitigate the risks of SD in critical decision-making environments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".