Neuro-Cognitive Profile of Morning and Evening Chronotypes at Different Times of Day
Bibliographic record
Abstract
BACKGROUND: Chronotype is the circadian time preference for sleep-wake timings. However, its impact on cognitive performance is least explored. OBJECTIVE: The present study investigated the effect of chronotype (morning "M" vs. evening "E") on cognitive measures as a function of time of the day. In addition, the correlation between electroencephalogram (EEG) waves and subjective/objective cognitive measures were investigated. METHOD: Cognitive status of 28 adult male subjects (15 "M" and 13 "E") was assessed objectively through event-related potential (ERP) by administering visual odd ball paradigm test and subjectively through Montreal Cognitive Assessment questionnaire. In addition, 20 to 30 min of resting EEG was recorded. Recordings were done from 8 to 10 am and from 4 to 6 pm on a single day. Power spectral analysis of EEG for alpha and beta waves at PZ and FZ cortical sites was done after subjecting selected epochs to fast Fourier transformation. Also, latency and amplitude of P300 potential from event-related potential record were measured. Appropriate statistical tests were applied for analysis. RESULTS: Higher alpha and beta power was observed in "E" at PZ in the evening. "M" showed increased P300 latency and amplitude during evening session for frequent and rare stimuli and vice versa in "E."' Significant negative correlation was seen between latency of rare stimuli and alpha and beta power at FZ site during evening in "E" chronotype only. CONCLUSION: Result indicates better attention and alertness during evening hours in evening chronotypes and vice versa in morning chronotypes. The findings could be implemented to schedule the mental performance/cognitive load according to individual chronotype.
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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".