The role of shared mental models in human-AI teams: a theoretical review

IF 1.4 Q4 ERGONOMICS
Robert W. Andrews, J. Lilly, Divya Srivastava, K. Feigh
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引用次数: 20

Abstract

Abstract Mental models are knowledge structures employed by humans to describe, explain, and predict the world around them. Shared Mental Models (SMMs) occur in teams whose members have similar mental models of their task and of the team itself. Research on human teaming has linked SMM quality to improved team performance. Applied understanding of SMMs should lead to improvements in human-AI teaming. Yet, it remains unclear how the SMM construct may differ in teams of human and AI agents, how and under what conditions such SMMs form, and how they should be quantified. This paper presents a review of SMMs and the associated literature, including their definition, measurement, and relation to other concepts. A synthesized conceptual model is proposed for the application of SMM literature to the human-AI setting. Several areas of AI research are identified and reviewed that are highly relevant to SMMs in human-AI teaming but which have not been discussed via a common vernacular. A summary of design considerations to support future experiments regarding Human-AI SMMs is presented. We find that while current research has made significant progress, a lack of consistency in terms and of effective means for measuring Human-AI SMMs currently impedes realization of the concept.
共享心智模型在人类-人工智能团队中的作用:理论回顾
摘要心理模型是人类用来描述、解释和预测周围世界的知识结构。共享心理模型(SMM)发生在团队中,其成员对其任务和团队本身有相似的心理模型。对人类团队合作的研究已经将SMM质量与团队绩效的提高联系起来。对SMM的应用理解应该会导致人类AI团队的改进。然而,目前尚不清楚SMM结构在人类和人工智能团队中的差异,这种SMM是如何形成的,在什么条件下形成的,以及应该如何量化。本文综述了SMM及其相关文献,包括其定义、测量以及与其他概念的关系。提出了一个综合概念模型,用于将SMM文献应用于人类人工智能环境。对人工智能研究的几个领域进行了识别和审查,这些领域与人类人工智能团队中的SMM高度相关,但尚未通过通用语言进行讨论。概述了支持未来人类人工智能SMMs实验的设计考虑因素。我们发现,尽管目前的研究已经取得了重大进展,但缺乏衡量人类人工智能SMM的术语和有效手段的一致性,目前阻碍了这一概念的实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.10
自引率
6.20%
发文量
38
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