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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".