The ACT-R production-system theory (Anderson, 1993) has been extended to include a theory of visual attention and pattern recognition. Production rules can direct attention to primitive visual features in the visual array. When attention is focused on a region, features in that region can be synthesized into declarative chunks. Assuming a time lo switch attention of about 200 msec, this model proves capable of simulating the results from a number of the basic studies of visual attention. W e have extended this model to complex problem-solving like equation solving where we have shown that an important component of learning is acquiring more efficient strategies for scanning the problem.

this paper, we examine the components of dynamic skill acquisition using a data set collected by Ackerman (1988) with the Kanfer-Ackerman Air-Traffic Controller Task©. Our analysis indicates that subjects arc improving in both the strategies they use to solve the task and the speed with which they execute the task. One strategy that subjects develop reduces the number of oven actions required lo land a plane. Another strategy that subjects develop enables them to land more planes simultaneously. A satisfactory model of this task must include both an improved strategic component and an improved speed component. The ACT-R theory (Anderson, 1993) is well suited to model these components as it is able lo separately learn over trials which strategies are better and how to execute each more efficiently.