Do people ask good questions?

People ask questions in order to efficiently learn about the world. But do people ask good questions? In this work, we designed an intuitive, gamebased task that allowed people to ask natural language questions to resolve their uncertainty. Question quality was measured through Bayesian idealobserver models that considered large spaces of possible game states. During free-form question generation, participants asked a creative variety of useful and goal-directed questions, yet they rarely asked the best questions as identified by the Bayesian ideal-observers (Experiment 1). In subsequent experiments, participants strongly preferred the best questions when evaluating questions that they did not generate themselves (Experiments 2 & 3). On the one hand, our results show that people can accurately evaluate question quality, even when the set of questions is diverse and an ideal-observer analysis has large computational requirements. On the other hand, people have a limited ability to synthesize maximally-informative questions from scratch, suggesting a bottleneck in the question asking process.