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

Abstract

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 Fitt’s theory 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. Some preliminary experimental data support these findings.