Category Learning Through Active Sampling


Laboratory studies of human category learning tend to emphasize passive learning by limiting participants’ control over what information they experience on every trial. In this paper, we explore the impact that active data selection has on category learning. In our experiment, participants attempted to learn standard rule-based (RB) and information-integration (II) 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 the decisions about which stimuli to query themselves. The effectiveness of active sampling was modulated, however, by the particular structure of the target category. Model based analyses explain these effects in terms of a strong bias towards uni- dimensional rules which impairs learning of the II category. 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.

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