摘要: Identifying the underlying decision criteria used by operators to classify system state, and revealing the way in which that information is internally represented is one of the challenges facing designers of control systems. This ... 展开 Identifying the underlying decision criteria used by operators to classify system state, and revealing the way in which that information is internally represented is one of the challenges facing designers of control systems. This research describes the use of multidimensional scaling (MDS) to probe the structure and composition of the internal conceptual models used by operators to identify system state. Twenty subjects were trained to classify instances of system data. Prior to training, and immediately after training, pairwise similarity ratings were solicited from subjects. Results showed that the dominant dimensions used by subjects were a function of the type of display, and that the dimensions could be used to predict classification performance. Reprints. (AW) 收起