A model of learning task-specific knowledge for a new task

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.