The Effects of Sleep Quality on Response Inhibition.
Bibliographic record
Abstract
Night-time sleep is critical for waking cognition. The duration, quality, and architecture (the distribution of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep) of sleep has been demonstrated to be linked to memory consolidation, emotional regulation, visual acuity, and other cognitive tasks essential to normative mental processes. Here, we investigate the relationship between response inhibition and sleep quality by comparing sleep measures and next day performance on an attention cognitive task; a measure of response inhibition. With a volunteer group of university students, we compared an experimental group that adjusted their sleep regimen according to sleep hygiene best practices to a control group without behavioral interventions. We used OURA rings to generate sleep index scores as measures of sleep quality. We hypothesized that 1) poor sleep quality would negatively impact attention task scores, 2) good sleep quality would positively impact attention scores and, 3) the experiment group would reflect higher frequency of positive moods based off a self-rated index. Our results suggest that the experiment group reported higher frequencies of positive moods, and that sleep quality is a positive predictor of performance on attention task scores, albeit an insignificant driver of attention task scores (p=0.44, CI: -0.103, 0.687). Instead, REM sleep is both a positive and significant driver of attention task scores (p=0.03, CI: 0.105, 0.614). Our findings suggest that the link between inhibitive emotional processing and REM sleep is one avenue to ensure altruistic behaviors between groups of people.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".