Asking goal-oriented questions and learning from answers

Abstract

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