Human cognition and the expert system interface: mental models and inference explanations

Variables affecting user/expert system interaction are evaluated empirically, and theoretical development of the relationship between human cognition and the use of intelligent machines is addressed. The hypotheses are that a good mental model will lead to increased user/computer interaction and performance and that graphic inference explanations will lead to higher performance than textual explanation. The examination of these hypotheses, as well as the cognitive processing underlying them, contributes directly to expert system interface design principles, as well as to theoretical development of the role of mental models in the understanding and use of dynamic, complex systems. Based on Newell and Simon´s (1972) theory of problem solving and the concept occurring in a problem space, human interaction with expert systems is quantified, relevant search strategies and directions are identified, and a theory of mental models of complex, dynamic systems is developed. The basic findings are that the user must have a good understanding (i.e., good mental model) of the expert system´s reasoning process, and that the user must effectively understand the information present in the expert system´s inference explanations