The Effects of a Nighttime Nap on the Error-Monitoring Functions During Extended Wakefulness
Bibliographic record
Abstract
STUDY OBJECTIVES: To examine the effects of a 1-hr nighttime nap, and the associated sleep inertia, on the error-monitoring functions during extended wakefulness using the 2 event-related potential components thought to reflect error detection and emotional or motivational evaluation of the error, i.e., the error-related negativity/error-negativity (ERN/Ne) and error-positivity (Pe), respectively. DESIGN: Participants awakened at 07:00 the morning of the experimental day, and performed a stimulus-response compatibility (arrow-orientation) task at 21:00, 02:00, and 03:00. SETTING: A cognitive task with EEG data recording was performed in a laboratory setting. PARTICIPANTS: Twenty young adults (mean age 21.3 ± 1.0 yr, 14 males) participated. INTERVENTIONS: Half of the participants took a 1-hr nap, and the others had a 1-hr awake-rest period from 01:00-02:00. MEASUREMENTS AND RESULTS: Behavioral performance and amplitude of the Pe declined after midnight (i.e., 02:00 and 03:00) compared with the 21:00 task period in both groups. During the task period starting at 03:00, the participants in the awake-rest condition reported less alertness and showed fewer correct responses than those who napped. However, there were no effects of a nap on the amplitude of the ERN/Ne or Pe. CONCLUSIONS: Our results suggest that a 1-hr nap can alleviate the decline in subjective alertness and response accuracy during nighttime; however, error-monitoring functions, especially emotional or motivational evaluation of the error, might remain impaired by extended wakefulness even after the nap. This phenomenon could imply that night-shift workers experiencing extended wakefulness should not overestimate the positive effects of a nighttime 1-hr nap during extended wakefulness.
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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 | 0.001 | 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 | 0.001 |
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".