Proceedings of the Annual Meeting of the Cognitive Science Society

Humans learn from other knowledgeable informants whochoose data to foster learning. Mathematical models of teach-ing and learning have formalized this process of learning fromhelpful others. While these approaches have been successful incapturing teaching and learning in a variety of contexts, theyhave been limited to relatively simple domains. One of theopen questions regarding Bayesian teaching is whether it canscale to teach from naturalistic domains with more interestingdatasets. In this work, we show how to apply Bayesian teach-ing to teach human participants categories learned by a super-vised machine learning model. The effectiveness of teaching ismeasured by how well the participants can predict the behaviorof the target machine learning model. Our results demonstratethat Bayesian teaching can be applied to naturalistic domains,show that the best sets of examples according to the modelyield better learning, and suggest avenues for improving ourability to automate teaching of image categories.