PROBING THE MENTAL MODELS OF SYSTEM STATE CATEGORIES WITH MULTIDIMENSIONAL-SCALING

Identifying the underlying decision criteria used by people to classify system state is one of the major challenges facing designers of decision aids for complex systems. This research describes the use of multidimensional scaling (MDS) to probe the structure and composition of the mental models employed by users to identify system state, and to evaluate the impact of different display formats on those models. Twenty people were trained to classify instances of system data. Pairwise similarity ratings of instances of system data were analysed by MDS to reveal the dominant dimensions used in the task. Results showed that significant individual differences emerged, and that the dimensions used by people were also a function of the type of display format. © 1992.