Category Learning Through Active Sampling

Laboratory studies of human category learning tend to emphasize passive learning by limiting participants’ control over the information they experience on every trial. In contrast, we explore the impact that active data selection has on category learning. In our experiment, participants attempted to learn categories under either entirely passive conditions, or by actively selecting and querying the labels associated with particular stimuli. We found that participants generally acquired categories faster in the active learning condition. Furthermore, this advantage depended on learners actually making decisions about which stimuli to query themselves. However, the effectiveness of active sampling was modulated by the particular structure of the target category. A probabilistic rule-learning model is proposed that explains the results in terms of a strong prior bias towards uni-dimensional rules which impairs learning of alternative category boundaries. Active learners appear to be able to bootstrap their own learning, but this ability may be strongly constrained by the space of hypotheses that are under consideration.