Noise and muscle contraction affecting vigilance task performance
Bibliographic record
Abstract
The objective of this study was to determine the effects of muscle contraction intensity, neuromuscular fatigue, and noise on vigilance performance. Dependent variables included simple (reaction time and movement time) and complex (video game: Tetris) vigilance tasks (SVT and CVT respectively) and maximum voluntary contraction (MVC) force and activation. Vigilance tasks and MVC were randomly allocated to 5 minute blocks during a pre-test. Following the pre-test, the tests were again randomly allocated within three, 15 minute testing sessions over 65 minutes, while 1) being exposed to high (95 dB (A)) or low (53 dB (A)) levels of noise, and 2) performing muscle contractions at 20% and 5% of MVC, or no contractions. Ninety-five (95) dB (A) noise increased (p ≤ 0.01) SVT (reaction time and movement time combined) by 11.2% and decreased (p ≤ 0.01) CVT by 20%. Both 20% and 5% MVC impaired SVT and CVT to a similar extent, while no changes were seen with no contractions. Furthermore, neuromuscular fatigue had no apparent effect on vigilance task performance. These findings suggest that the distraction of noise and divided attention between muscle contraction and a vigilance task decreases performance.
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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 | 0.003 |
| 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".