The papers in this Special Issue point to a new route for the future, one in which models built within embodied cognitive architectures provide information for the design of better interfaces. Pergamon, in press. A production system operates by means of a recognise-act cycle, in which the rule whose condition part is satisfied is identified, and its actions taken. Production systems have special properties that make them highly suited to modelling cognition, including their combination of parallel and serial processing, the independence of their rules, and their flexible control. From their origins as models of problem solving, production systems have grown to become a major formalism for modelling cognitive skill and aspects of learning, in areas such written arithmetic, reading, and knowledge-intensive areas of expertise such as chess playing and medical diagnosis.
Production systems lend themselves well to the modelling of learning and cognitive development.
Since the 1990s, they have become increasingly identified with the topic of integrated cognitive architectures. They have been useful to HCI by predicting task times, by assisting users, and by acting as surrogate users. If cognitive models could interact with the same interfaces that users do, the models would be easier to develop and would be easier to apply as interface testers. We present five case studies using three different UIMSes. These interaction mechanisms start to support and constrain performance in the same ways that human performance is supported and constrained by interaction.