Controlled Attention and Sleep Deprivation: Adding a Self-Regulation Approach?
Bibliographic record
Abstract
The current study examined performance on an automated task battery under short-term sleep deprivation andnon-sleep deprivation conditions. Twenty-six volunteers completed the sleep deprivation study. Twenty-threevolunteers completed the non-sleep deprivation study. Performance was examined across five test sessionsduring 25 hours of acute sleep deprivation conditions and during two days of non-sleep deprivation conditions.ANOVAs examining changes in performance from baseline levels indicated that performance under sleepdeprivation conditions resulted in a decrease in performance in some tasks and an increase in estimated bloodalcohol concentration. Non-sleep deprivation resulted in stable or increasing performance and a decrease inestimated blood alcohol concentration. The Controlled Attention Model suggests that the task characteristicswould have helped maintain performance levels but does not explain how performance decreased on some butnot all of the tasks. Extending the Controlled Attention Model to include a broader self-regulation approachsuggests that on some of the tasks the participants did not adequately regulate their engagement in the task (evenwith rapidly changing stimuli) resulting in a decrease in performance levels. Incorporating a self-regulationapproach with the Controlled Attention Model could provide a model that better explains the range of effectsseen under sleep deprivation conditions.
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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 | 0.004 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".