Performance monitoring during sleep inertia after a 1-h daytime nap
Bibliographic record
Abstract
Performance monitoring is an essential function involved in the correction of errors. Deterioration of this function may result in serious accidents. This function is reflected in two event-related potential (ERP) components that occur after erroneous responses, specifically the error-related negativity/error negativity (ERN/Ne) and error positivity (Pe). The ERN/Ne is thought to be associated with error detection, while the Pe is thought to reflect motivational significance or recognition of errors. Using these ERP components, some studies have shown that sleepiness resulting from extended wakefulness may cause a decline in error-monitoring function. However, the effects of sleep inertia have not yet been explored. In this study, we examined the effects of sleep inertia immediately after a 1-h daytime nap on error-monitoring function as expressed through the ERN/Ne and Pe. Nine healthy young adults participated in two different experimental conditions (nap and rest). Participants performed the arrow-orientation task before and immediately after a 1-h nap or rest period. Immediately after the nap, participants reported an increased effort to perform the task and tended to estimate their performance as better, despite no objective difference in actual performance between the two conditions. ERN/Ne amplitude showed no difference between the conditions; however, the amplitude of the Pe was reduced following the nap. These results suggest that individuals can detect their own error responses, but the motivational significance ascribed to these errors might be diminished during the sleep inertia experienced after a 1-h nap. This decline might lead to overestimation of their performance.
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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".