New Methods for Modeling Human-Machine Interaction

Abstract

Use of the term mental model has proliferated in the discussion of human-machine interaction. Although it seems clear that humans must depend on mental models when doing problem solving in the domain of complex systems, the literature on the topic presents a confusing variety of perspectives, and there is little empirical evidence of the structure of the models people use or of how they influence human performance. The objectives of this symposium are to (a) provide a taxonomy for mental models and suggest a theory that is intended to unify what appear now to be disparate views, (b) outline an information-theoretic method for determining the structure of complex systems, and (c) describe an application of the theory and method to a process-control simulation. In the first presentation, Moray makes the case for the need for modeling methods that can deal effectively with systems of unusual complexity. In the second, Conant describes such a method. Jamieson, in the third, reports the results of an experiment in which this method was applied.