Microsleep Episodes, Attention Lapses and Circadian Variation in Psychomotor Performance in a Driving Simulation Paradigm
Bibliographic record
Abstract
Numerous studies document circadian changes in sleepiness, with biphasic peaks in the early morning and late afternoon. Driving performance has also been demonstrated to be subject to time-of-day variation. This study investigated circadian variation in driving performance, attention lapses (AL) and/or frequency of microsleep (MS) episodes across the day. Sixteen healthy adults with valid driver’s licenses participated in the study. Using the York Driving Simulator, subjects performed four intentionally soporific 30-minute driving simulations at two-hour intervals (i.e., at 10:00, 12:00, 14:00, and 16:00). During each session, individuals had EEG monitoring for MS episodes (defined as 15 to 30 seconds of any sleep stage by polysomnographic criteria) and AL episodes (defined as intrusion of alpha- or theta-EEG activity lasting 4-14 seconds). Measured variables included: lane accuracy, average speed, speed deviation, mean reaction time (RT) to “virtual” wind gusts and off-road events. Mean values of each variable at every time were analyzed using a general linear model and paired sample t-tests. RT displayed significant within-group variation, with paired samples tests at df=15 showing RT at 10:00 significantly faster than at other times of the day, but no significant within-group variation between other times of the day. All other variables and EEG-defined AL episodes failed to exhibit any statistically significant variation across the day. However, MS episodes were found to occur more often at 16:00 in comparison to all other times. As RT was optimal before noon, it appears that psychomotor performance and therefore driving ability is subject to circadian variation. Coincident with the demonstrated circadian pattern of diminished alertness, this may partially explain the high incidence of motor vehicle accidents during the mid- to late-afternoon. By better understanding circadian fluctuations in driver sleepiness and psychomotor performance, human performance researchers may be in a position to better educate the public about cautionary measures to prevent accidents.
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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 | 0.000 |
| 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) | 0.000 | 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".