Modeling Interference Effects In Instructed Category Learning

Category learning is often seen as a process of inductive generalization from a set of class-labeled exemplars. Human learners, however, often receive direct instruction concerning the structure of a category before being presented with examples. Such explicit knowledge may often be smoothly integrated with knowledge garnered by exposure to instances, but some interference effects have been observed. Specifically, errors in instructed rule following may sometimes arise after the repeated presentation of correctly labeled exemplars. Despite perfect consistency between instance labels and the provided rule, such inductive training can drive categorization behavior away from rule following and towards a more prototype-based or instance-based pattern. In this paper we present a general connectionistmodel of instructed category learning which captures this kind of interference effect. We model instruction as a sequence of inputs to a network which transforms such advice into a modulating force on classification behavior. Exemplarbased learning is modeled in the usual way: as weight modification via backpropagation. The proposed architecture allows these two sources of information to interact in a psychologically plausible manner. Simulation results are provided on a simple instructed category learning task, and these results are compared with human performance on the same task.