Proceedings of the Annual Meeting of the Cognitive Science Society

The study of question asking in humans and machines hasgained attention in recent years. A key aspect of question ask-ing is the ability to select good (informative) questions froma provided set. Machines—in particular neural networks—generally struggle with two important aspects of question ask-ing, namely to learn from the answer to their selected ques-tion and to flexibly adjust their questioning to new goals. Inthe present paper, we show that people are sensitive to both ofthese aspects and describe a unified Bayesian account of ques-tion asking that is capable of similar ingenuity. In the first ex-periment, we predict people’s judgments when adjusting theirquestion-asking towards a particular goal. In the second ex-periment, we predict people’s judgments when deciding whatfollow-up question to ask. An alternative model based on su-perficial features, such as the existence of certain key wordsin the questions, was not able to capture these judgments to areasonable degree.