融合认知模型与人因工程，构建更懂人的机器交互架构。
Toward an~Integrated Cognitive--Ergonomic Architecture for~Human--Machine Interaction: Combining Cognitive Models with~Human Factors Ergonomics
- 整合SOAR、ACT-R等认知模型与人因工程，设计新交互架构。
- 在工业机器人场景中验证，提升操作者决策与适应能力。
- 适合人机交互、智能系统设计人员参考。
本文提出一种集成方法，将认知架构理论与人因工程（HFE）原则结合，以建模人类能力。通过对比分析主流认知模型——SOAR、ACT-R、LIDA和COCOM，我们构建了一个定制化架构，用于应对动态环境中人机交互（HMI）的复杂性。该架构嵌入人因工程框架，阐明了决策、技能习得与自适应行为的机制，弥合认知理论与系统设计之间的鸿沟。研究基于工业机器人应用，强调操作员专长、规范知识与实时反馈的重要性。所提架构不仅增强人机认知对齐，还提供可扩展的方法论，适用于高风险环境中的智能、以人为中心界面设计。本工作推动了对人类能力的理论理解，并促进了自适应、人因优化系统的实践落地。
原文摘要 · Abstract (English)
This paper presents an integrated approach to modeling human competencies by combining the theoretical foundations of cognitive architectures with principles from Human Factors Ergonomics (HFE). Through a comparative analysis of established cognitive models-SOAR, ACT-R, LIDA, and COCOM-we synthesize a tailored architecture designed to address the complexities of human-machine interaction (HMI) in dynamic environments. By contextualizing this model within ergonomic frameworks, we elucidate the mechanisms underlying decision-making, skill acquisition, and adaptive behavior, bridging the gap between cognitive theory and applied system design. Our framework is empirically grounded in industrial robotics applications, where operator expertise, normative knowledge, and real-time feedback loops are critical. The proposed architecture not only enhances the cognitive alignment of HMI systems but also provides a scalable methodology for designing intelligent, human-centered interfaces in high-stakes environments. This work advances both the theoretical understanding of human competencies and the practical implementation of adaptive, ergonomically optimized systems.
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