A Comparison of Cognitive Performance Decreases During Acute, Progressive Fatigue Arising From Different Concurrent Stressors
Bibliographic record
Abstract
Fatigue is known to impair cognitive performance, but it remains unclear whether concurrent common stressors affect cognitive performance similarly. We used the Stroop Color-Word Conflict Test to assess cognitive performance over 24 hours for four groups: control, sleep-deprived (SD), SD + energy deficit, and SD + energy deficit + fluid restricted. Fatigue levels were quantified using the Profile of Mood States (POMS) survey. Linear mixed-effects (LME) models allowed for testing of group-specific differences in cognitive performance while accounting for subject-level variation. Starting fatigue levels were similar among all groups, while 24-hour fatigue levels differed significantly. For each cognitive performance test, results were modeled separately. The simplest LME model contained a significant fixed-effects term for slope and intercept. Moreover, the simplest LME model used a single slope coefficient to fit data from all four groups, suggesting that loss in cognitive performance over a 24-hour duty cycle with respect to fatigue level is similar regardless of the cause.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".