Proceedings of the Annual Meeting of the Cognitive Science Society

A key goal in both education and higher-order cognitionresearch is to understand how relational concepts are bestlearned. In the current work, we present a novel approach forlearning complex relational categories – a low-support,interactive discovery interface. The platform, which allowslearners to make modifications to exemplars and see thecorresponding effects on membership, holds the potential toaugment relational learning by facilitating self-directed,alignably-different comparisons that explore what the learnerdoes not yet understand. We compared interactive learning to anidentification learning task. Participants were assessed on theirability to generalize category knowledge to novel exemplarsfrom the same domain. Although identification learners wereprovided with seven times as many positive examples of thecategory during training, interactive learners demonstratedenhanced generalization accuracy and knowledge of specificmembership constraints. Moreover, the data suggest thatidentification learners tended to overgeneralize categoryknowledge to non-members – a problem that interactive learnersexhibited to a significantly lesser degree. Overall, the resultsshow interactive training to be a powerful tool forsupplementing relational category learning, with particularutility for refining category knowledge. We conclude withimplications of these findings and promising future directions.