摘要: |
目的 以降低操作者认知负荷为目标从人机认知任务分配角度探讨雷达界面信息图的交互设计策略。 方法 对基于雷达信息图的系统观察任务流程进行分析,基于新型人机协同、人机合作视角探讨系统人机功能分配的原则,采用认知负荷评测方法,通过用户绩效测量和NASA-TLX量表,采集分析任务完成的时间、正确率及认知负荷水平,比较不同的人机分工方案对操作者认知负荷的影响。结果 基于新的人机功能分配策略的设计方案中,用户完成时间、正确率均大于初始方案,完成任务的认知负荷小于初始设计方案。结论 复杂信息界面的信息呈现与交互设计,可以通过系统功能在人机之间的合理分配,充分发挥人机各自优势,降低操作者的认知负荷,提高观测准确率,提高系统绩效。 |
关键词: 认知负荷 雷达信息图 交互设计 人机分工 |
DOI:10.19554/j.cnki.1001-3563.2021.20.005 |
分类号:TB472 |
基金项目: |
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Interactive Design Strategy Research on Radar Information Views Based on Cognitive Load Theory |
GONG Xiao-dong1,2, GONG Qian1,2, LIU Yu-shun1,2, QIU Yue1
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(1.Beijing Institute of Technology, Beijing 100081, China;2.Radar Ergonomics Joint Innovation Laboratory, Beijing 100081, China)
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Abstract: |
This paper explores the interactive design strategy of the infographic on radar interface to reduce users’ cognitive load from the perspective of human-machine cognitive task allocation. This paper analyzed the systematic observation task flow of radar infographic, discussed different human-machine allocation methods of system tasks from the perspective of new human-machine coordination and cooperation, collected and analyzed the task completion time, accuracy rate and the cognitive loads through user performance measurement and NASA-TLX scale, and contrasted the impact of different human-machine task allocation methods on users’ cognitive load. In the design based on the new human-machine task allocation, the users’ completion time and accuracy rate were both greater than those in the initial interface design, and the cognitive load to complete the cognitive task was less than that in the initial interface design. Through rational allocation methods of system cognitive tasks between humans and machines, the information presentation and interactive design of complex information interfaces can give full play to the respective advantages of humans and machines, reduce the cognitive load of operators, and improve observation accuracy and system performance. |
Key words: cognitive load radar infographic interactive design human-machine task allocation |