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

机译：CMS-BN：人类绩效评估的认知建模和仿真环境，第1部分 - 方法

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摘要

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.
机译：认知建模和模拟研究人类如何与外部世界动态相互作用。基于这一概念的人类绩效评估长期以来一直在认知科学和工程学科中研究。然而，现有方法在描述人类知识中的不确定关系以及考虑认知过程中的不确定性时难以困难。为了解决这些问题，我们通过引入贝叶斯网络来提出一种新的认知建模和模拟环境（CMS-BN），以代表人类的知识和蒙特卡罗模拟，以解释认知过程中的不确定性。拟议的环境明确地模拟了人类认知过程中的信息感知，推理和反应。信息感知作为从外部世界的下扫描机制的过滤机制。推理和响应被建模为遍历代表为贝叶斯网络的人类知识库，以便相应地检索知识和更新人类信仰和注意力分配。认知过程中的不确定性是通过蒙特卡罗模拟的特征。拟议的环境还模拟了认知过程与两个性能塑造因子，压力和疲劳之间的相互作用，尽管可以进一步考虑额外的因素。我们希望建议的环境在人类可靠性分析和人类绩效改进方面有用。

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