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 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".