Our goal is to develop a cognitive model of how humans acquire skills on complex cognitive tasks. We are pursuing this goal by designing computational architectures for the NRL Navigation task, which requires competent sensorimotor coordination. In this paper, we analyze the NRL Navigation task in depth. We then use data from experiments with human subjects learning this task to guide us in constructing a cognitive model of skill acquisition for the task. Verbal protocol data augments the black box view provided by execution traces of inputs and outputs. Computational experiments allow us to explore a space of alternative architectures for the task, guided by the quality of fit to human performance data.

Our goal is to develop a hybrid cognitive model of how humans acquire skills on complex cognitive tasks. We aie pursuing this goal by designing hybrid computational architectures for the NRL Navigation task, which requires competent sensorimotor coordination. In this paper, we describe results of directly fitting human execution data on this task. We next present and then empirically compare two methods for modeling control knowledge acquisition (reinforcement learning and a novel variant of action models) with human learning on the task. The paper concludes with an experimental demonstration of the impact of background knowledge on system performance. Our results indicate that the performance of our action models approach more closely approximates the rate of human learning on this task than does reinforcement learning.

Our goal is to develop a cognitive model of how humans acquire skills on complex, sensorimotor tasks. To achieve this goai, we collected data from subjects learning the NRL Navigation task, then used the data to construct a model that reflects the bcisic, cognitive elements required to learn and thereby succeed at this task (Gordon & Subramzuiian, 1997). This paper describes a new experiment with humcin subjects on the task. Data from this experiment not only confirms the key cognitive element of our model, but also helps us better understand individual differences in learning this task. Four evaluation metrics indicate that we are able to model important trends in the evolution of action choice.