Predicting Insight during Physical Reasoning

When people solve problems, they may try multiple invalid solutions before finally having an insight about the correct solution. Insight problem-solving is an example of the flexibility of the human mind which remains unmatched by machines. In this paper, we present a novel experimental paradigm for studying insight problem-solving behavior in a physical reasoning domain. Using this paradigm and several data-driven analyses, we seek to quantify what it means to have an insight during physical problem-solving and identify behavioral traces that predict subjective insight ratings collected from human participants. This project aims to provide the first steps towards a computationally informed theory of insight problems solving.