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Human-based dynamics of mental workload in complicated systems.

Mohammad-Javad Jafari1, Farid Zaeri2, Amir H Jafari3

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Summary

This study presents a dynamic system model to measure mental workload, addressing a gap in current research. The model, validated in a train simulator, aids decision-makers in preventing accidents by analyzing long-term workload impacts.

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Area of Science:

  • Human Factors Engineering
  • Systems Dynamics
  • Cognitive Psychology

Background:

  • Mental workload is a complex, dynamic factor influencing human performance in intricate systems.
  • Existing measures often fail to capture the dynamic interactions affecting mental workload.
  • A dynamic approach is needed to monitor and manage mental workload effectively.

Purpose of the Study:

  • To introduce a system dynamics-based model for designing feedback mechanisms for mental workload.
  • To develop a human-based archetype of mental workload.
  • To provide a tool for analyzing the long-term behavior of mental workload.

Main Methods:

  • Literature review and content analysis of previous studies.
  • Development of a system dynamics model based on identified archetypes.
  • Validation of the dynamic model using an urban train simulator.

Main Results:

  • A human-based archetype of mental workload was identified and modeled.
  • The dynamic model was successfully validated in a realistic simulation environment.
  • The model demonstrates the capability to analyze long-term mental workload behavior.

Conclusions:

  • The developed system dynamics model offers a novel approach to measuring and understanding mental workload.
  • Decision-makers can utilize this model to evaluate the impact of interventions on accident prevention.
  • This research contributes to a more dynamic and comprehensive understanding of mental workload in complex systems.