A Daytime Nap Enhances Visual Working Memory Performance and Alters Event-Related Delay Activity
Bibliographic record
Abstract
Working memory (WM) is impaired following sleep loss and may be improved after a nap. The goal of the current study was to better understand sleep-related WM enhancement by: 1) employing a WM task that assesses the ability to hold and report visual representations as well as the fidelity of the reports on a fine scale, 2) investigating neurophysiological properties of sleep and WM capacity as potential predictors or moderators of sleep-related enhancement, and 3) exploring frontal and occipital event-related delay activity to index the neural processing of stimuli in WM. In a within-subjects design, thirty-six young adults (Mage = 20, 20 men, 16 women) completed a 300-trial, continuous-report task of visual WM following a 90-min nap opportunity and an equivalent period of wakefulness. Mixed-effect models were used to estimate the odds of successful WM reports and the fidelity of those reports. The odds of a successful report were approximately equal between nap and wake conditions at the start of the task, but by the end, the odds of success were 1.26 times greater in the nap condition. Successful WM reports were more accurate after a nap, independent of time on task. Neither WM capacity nor any of the sleep variables measured were found to significantly moderate the nap effect on WM. Lastly, amplitude of frontal and occipital delay activity was altered in the nap relative to the wake condition. The findings are discussed in relation to the role of sleep in sustained attention and contemporary models of visual WM.
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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 | 0.000 |
| 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) | 0.001 | 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".