Cognitive modeling techniques provide a way of evaluating user interface designs, based on what is known about human cognitive strengths and limitations. Cognitive modelers face a tradeoff, however: more detailed models require disproportionately more time and effort to develop than coarser models. In this paper we describe a system, G2A, omatically produces translations from abstract GOMS models into more detailed ACT-R models. G2A demonstrates how even simple AI techniques can facilitate the construction of cognitive models and suggests new directions for improving modeling tools.
Robert St. Amant, Sean P. McBride, Frank E. Ritter