ABSTRACT
In this paper we review the benefits of abstract computational models of cognition and present one such model of behavior in a flight-control domain. The model's central assumptions are that differences among subjects are due to differences in sensing skills, and that the main form of learning involves updating statistics to distinguish relevant from irrelevant features. We report an implementation of this abstract model of sensory learning, along with a system that searches the space of parameter settings in order to fit the model to observations. We compare the sensory-learning framework to an alternative based on the power law, finding that the latter fits the data slightly better but that it requires many more parameters.
