A Team Cognition Measurement Method for Single Pilot Operations Human-Machine System Design

Team cognition is an important indicator to reflect the trend of single pilot operation (SPO) human-machine system team performance. How to measure team cognition to explore different function allocation decisions before the implementation of prototypes or simulators has become a significant step in the early SPO human-machine system design. Therefore, this article proposed a team cognition measurement method based on the social network analysis (SNA) method, namely cognitive social network analysis (CSNA). The CSNA method firstly identified the nodes, edge directions and channel resource values of each task; secondly calculated the cognitive demands of each task based on multiple resource theory; thirdly summed the cognitive demands of the same task to construct the association matrix; and finally calculated global and nodal network metrics. The results of the study showed that (1) the CSNA method could identify design flaws of the initial SPO human-machine system; (2) the CSNA method increased the ability to identify the changes in both the cognitive demands of tasks and the overall workloads of human agents compared to the SNA method; (3) the CSNA method could help researchers make reasonable design suggestions to improve the team performance of the redesigned SPO human-machine system. The above results not only verify the feasibility of the CSNA method, but also show that the CSNA method is superior to the current SNA method.团队认知（Team cognition）是反映单飞行员操作（Single Pilot Operation, SPO）人机系统（human-machine system）团队绩效趋势的重要指标。在原型机或模拟器落地部署前，如何量化团队认知以探索不同功能分配方案，已成为SPO人机系统早期设计阶段的关键环节。为此，本文提出了一种基于社会网络分析（Social Network Analysis, SNA）的团队认知量化方法，即认知社会网络分析（Cognitive Social Network Analysis, CSNA）。该方法首先明确各任务的节点、边方向与信道资源值；其次基于多重资源理论（Multiple Resource Theory）计算各任务的认知需求；随后对同一任务的认知需求进行求和以构建关联矩阵；最终计算全局与节点级网络指标。研究结果表明：（1）CSNA方法可识别初始SPO人机系统的设计缺陷；（2）相较于传统SNA方法，CSNA方法能够更精准地识别任务认知需求与人类操作人员整体工作负荷的变化；（3）CSNA方法可助力研究人员提出合理设计建议，以优化改进后SPO人机系统的团队绩效。上述结果不仅验证了CSNA方法的可行性，同时证实该方法优于当前主流的SNA方法。