CMS-BN: A cognitive modeling and simulation environment for human performance assessment, part 1-methodology

Cognitive modeling and simulation studies how a human dynamically interacts with the external world. Human performance assessment based on this concept has long been researched in both cognitive sciences and engineering disciplines. However, existing methods have difficulties in describing the uncertain relationships in a human's knowledge and in considering the uncertainties in the cognitive process. To tackle these issues, we propose a novel cognitive modeling and simulation environment (CMS-BN) by introducing Bayesian networks to represent a human's knowledge and Monte Carlo simulation to account for the uncertainties in the cognitive process. The proposed environment explicitly models information perception, reasoning and response in a human's cognitive process. Information perception works as a filtering mechanism to downselect signals from the external world. Reasoning and response are modeled as traversing the human knowledge base represented as a Bayesian network to retrieve knowledge and updating human belief and attention distribution accordingly. Uncertainties in the cognitive process are characterized through Monte Carlo simulation. The proposed environment also models the interplay between the cognitive process and two performance shaping factors, stress and fatigue, though additional factors can be further considered. We expect the proposed environment to be useful in human reliability analysis and human performance improvement.

Automated summary

This study focuses on cognitive modeling and simulation, aiming to describe how humans interact with the external world dynamically. Bayesian networks and Monte Carlo simulation are introduced to address uncertainties in the cognitive process.