Proceedings of the Annual Meeting of the Cognitive Science Society

Recently, both psychology and machine learning have explored the ability of learners to ask questions. However, much of this work has focused on a single type of question: a “label query”. When making a label query, the learner selects an unfamiliar (unlabeled) item and requests a label for it (e.g., ”What is this?”). We hypothesized that people often prefer much richer types of questions (e.g., feature queries: “Is this feature relevant?”, demonstration queries: “Can I see an example of a ladybug?”, etc.). To study this behavior, we had people play a simple game where they generated natural language questions to determine a hidden configuration of objects. We compute the normative value of these rich questions as measured by modelbased analyses (e.g., information gain). A second experiment evaluates the ability of human observers to judge this value when the demands of question generation are removed.