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It has often been suggested that users understand and reason about complex systems on the basis of a mental model of the system's internal mechanics. This paper describes an empirical study of how mental model knowledge is used in operating a stack calculator. One group of naive users were taught step-by-step procedures for solving typical problems on the calculator. A second group of naive users were taught the same procedures in conjunction with an explicit model of the calculator's stack mechanism. The users then solved problems on the calculator while thinking aloud. Analysis of the performance of these two groups indicates that the model had little effect in routine problem solving situations, but significantly improved performance for novel problems. Analyses of the think-aloud protocols indicate that the users employed five distinct modes of problem solving: skilled methods, problem reduction strategies, a conversion algorithm, model-based problem space search, and methods-based problem space search. Skilled methods, problem reduction strategies and the conversion algorithm were used for solving more routine problems and did not necessarily depend on mental model knowledge. Problem space search was used in the novel problems. For the model users, the states and operations of the stack mechanism served as the problem space to be searched for a problem solution. In contrast, the no-model users employed a less effective search strategy based on the recombination of pieces of known procedures. These results indicate that explicitly teaching naive users an appropriate mental model of a system can provide a psychologically effective and robust basis for operating the machine.
Halasz et al. (Sat,) studied this question.