Probing the Mental Models of System State Categories with Multidimensional Scaling

-Identifying the underlying decision criteria used by operators to classi&y system state and revealing the way in which that information is internally represented by individual operators is one of the major challenges facing designers of decision aids for process plants. This researqh describes the use of multidimensional scaling (MDS) to probe the structure and composition of the mental models of used by operators to identify system state, and evaluate the impact of different display representations on those models. Twenty subjects were trained to classify instances of system data. Pairwise similarity ratings of instances of system data were analyzed by MDS to reveal the dominant dimensions used by operators. Results showed that significant individual differences emerged, and that the dimensions used by subjects were also a function of the type of display representation. Coury, Zubritzky and CuqlocK-Knpp 4.