Proceedings of the Annual Meeting of the Cognitive Science Society

The ability to ask questions during learning is a key aspect ofhuman cognition. While recent research has suggested com-mon principles underlying human and machine “active learn-ing,” the existing literature has focused on relatively simpletypes of queries. In this paper, we study how humans constructrich and sophisticated natural language queries to search for in-formation in a large yet computationally tractable hypothesisspace. In Experiment 1, participants were allowed to ask anyquestion they liked in natural language. In Experiment 2, par-ticipants were asked to evaluate questions that they did not gen-erate themselves. While people rarely asked the most informa-tive questions in Experiment 1, they strongly preferred moreinformative questions in Experiment 2, as predicted by an idealBayesian analysis. Our results show that rigorous information-based accounts of human question asking are more widely ap-plicable than previously studied, explaining preferences acrossa diverse set of natural language questions.