Effect of Time Pressure and Cognitive Demand on Line Workers’ Risk-Taking Behaviors: Assessment of Neuro-Psychophysiological Responses in a Mixed-Reality Environment
Bibliographic record
Abstract
Recent research suggests construction workers fall prey to the cognitive biases of risk compensation, wherein workers offset safety improvements by taking more risks. Parallel previous literature indicates that time pressure and mental load may increase workers’ arousal and stress. However, it is unclear whether time, productivity, and/or cognitive demands can worsen risk compensation behaviors by stimulating workers to make riskier decisions to complete tasks faster. Combining a multi-modal mixed-reality environment with wearable neuro-psychophysiological sensors, this study examines changes in safety and task performance for high-risk electrical-line tasks simulated under time/performance pressure and cognitive demand. The results show risk-compensation is in play as subjects over-rely on safety technologies and maintain their risk perception even while undertaking more risks to adapt to increased time pressure and/or cognitive demand. This paper contributes to body of knowledge by affecting safety-training approaches and the controls needed when providing workers with safety protection and new technological advances.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".