Proceedings of the Annual Meeting of the Cognitive Science Society

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.