In this paper I will present a detailed ACT-R model of how the task-specific knowledge for a new. complex task is learned. The model is capable of acquiring its knowledge through experience, using a declarative representation that is gradually compiled into a procedural representation. The model exhibits several characteristics that concur with Fitts' and Anderson's theories of skill learning, and can be used to show that individual differences in working-memory capacity initially have a large impact on performance, but that this impact diminished after sufficient experience, which is consistent with Ackermans's theory of skill learning. Some preliminary experimental data support these findings.

In this paper two approaches to problem solving, search and reflection, are discussed, and combined in two models, both based on rational analysis (Anderson, 1990). The first model is a dynamic growth model, which shows that alternating search and reflection is a rational strategy. The second model is a model in ACT-R, which can discover and revise strategies to solve simple problems. Both models exhibit the explore-insight pattern normally attributed to insight problem solving.