Predicting insight during physical problem solving

LeGris, S., Lake, B. M., & Gureckis, T. M. (2024). Predicting insight during physical problem solving. Proceedings of the 46th Annual Conference of the Cognitive Science Society.

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Abstract

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, we seek to quantify precisely what it means to have an insight during physical problem-solving and identify behavioral traces that predict subjective insight ratings collected from human participants. The project provides the first steps towards a computationally informed theory of insight problems solving.

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Bibtex entry:

@inproceedings{legris2024physicalsolving,
	abstract = {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, we seek to quantify precisely what it means to have an insight during physical problem-solving and identify behavioral traces that predict subjective insight ratings collected from human participants.  The project provides the first steps towards a computationally informed theory of insight problems solving.},
	address = {Austin, TX},
	author = {LeGris, S. and Lake, B.M. and Gureckis, T.M.},
	booktitle = {Proceedings of the 46th Annual Conference of the Cognitive Science Society},
	organization = {Cognitive Science Society},
	publisher = {Cognitive Science Society},
	title = {Predicting insight during physical problem solving},
	year = {2024}}

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