Schürmann Tim, Beckerle Philipp
Work and Engineering Psychology Research Group, Department of Human Sciences, Technical University of Darmstadt, Darmstadt, Germany.
Elastic Lightweight Robotics, Department of Electrical Engineering and Information Technology, Robotics Research Institute, Technische Universität Dortmund, Dortmund, Germany.
Front Psychol. 2020 Sep 24;11:561510. doi: 10.3389/fpsyg.2020.561510. eCollection 2020.
Cognitive modeling of human behavior has advanced the understanding of underlying processes in several domains of psychology and cognitive science. In this article, we outline how we expect cognitive modeling to improve comprehension of individual cognitive processes in human-agent interaction and, particularly, human-robot interaction (HRI). We argue that cognitive models offer advantages compared to data-analytical models, specifically for research questions with expressed interest in theories of cognitive functions. However, the implementation of cognitive models is arguably more complex than common statistical procedures. Additionally, cognitive modeling paradigms typically have an explicit commitment to an underlying computational theory. We propose a conceptual framework for designing cognitive models that aims to identify whether the use of cognitive modeling is applicable to a given research question. The framework consists of five external and internal aspects related to the modeling process: research question, level of analysis, modeling paradigms, computational properties, and iterative model development. In addition to deriving our framework from a concise literature analysis, we discuss challenges and potentials of cognitive modeling. We expect cognitive models to leverage personalized human behavior prediction, agent behavior generation, and interaction pretraining as well as adaptation, which we outline with application examples from personalized HRI.
人类行为的认知建模推动了对心理学和认知科学多个领域潜在过程的理解。在本文中，我们概述了我们期望认知建模如何增进对人机交互，特别是人机互动（HRI）中个体认知过程的理解。我们认为，与数据分析模型相比，认知模型具有优势，特别是对于那些对认知功能理论表现出兴趣的研究问题。然而，可以说认知模型的实现比常见的统计程序更为复杂。此外，认知建模范式通常对潜在的计算理论有明确的承诺。我们提出了一个用于设计认知模型的概念框架，旨在确定认知建模的使用是否适用于特定的研究问题。该框架由与建模过程相关的五个外部和内部方面组成：研究问题、分析层次、建模范式、计算属性和迭代模型开发。除了从简洁的文献分析中得出我们的框架外，我们还讨论了认知建模的挑战和潜力。我们期望认知模型能够利用个性化的人类行为预测、智能体行为生成以及交互预训练和适应，我们将通过个性化HRI的应用示例来概述这些内容。
