Physiological Impact Assessment of Decision Support Systems on Control Room Operators: An ANCOVA Analysis
Bibliographic record
Abstract
This study investigates the physiological effects of AI based Decision Support Systems (DSS) on control room operators through a comprehensive analysis of multiple physiological indicators.Using data from 41 participants divided into control and experimental groups, we analyzed heart rate, temperature, electrodermal activity (EDA), and pupil diameter across three scenarios of increasing complexity.Analysis of Covariance (ANCOVA) was employed to control for baseline differences, revealing significant reductions in pupil diameter (p = 0.0029) for the DSS group, indicating lower cognitive load.While other physiological measures showed consistent trends suggesting reduced stress with DSS use, these differences were not statistically significant.The findings provide empirical evidence for DSS's positive impact on operator cognitive load, particularly during complex scenarios, while highlighting the need for comprehensive physiological monitoring in assessing human-system interaction.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".