Classical approaches to studying insight problem-solving typically use specialized problems (e.g., nine-dot problem, compound-remote associates task) as stimuli together with verbal reports from subjects during problem-solving to reveal their thought processes, possibly adding other task-related metrics such as completion rate and physiological measures like eye fixation and neural activity. This approach has led to the claims that insight and creative thought require impasse and mental restructuring. What is missing from this literature is a cognitive process model of insight, and one reason for the lack of such a model is the lack of a unified, scalable, and tunable experimental framework with which to study human creative problem-solving with higher fidelity. In this paper, we introduce ESCAPE, an experimental paradigm using puzzle video games as stimuli which allow for the collection of process data that can serve as a basis for computational models. We have specifically developed a set of puzzle games based on this paradigm and conducted experiments that demonstrate the utility of the approach by revealing a set of computational principles that need to be accounted for by a theory of creative problems and the computational models based on it.
@inproceedings{sarathyetal2024cogsci,
title={Using Puzzle Video Games to Study Cognitive Processes in Human Insight and Creative Problem-Solving},
author={Sarathy, Vasanth and Rabb, Nicholas and Kasenberg, Daniel and Scheutz, Matthias},
year={2024},
booktitle={Proceedings of the 46th Annual Meeting of the Cognitive Science Society},
url={https://hrilab.tufts.edu/publications/sarathyetal2024cogsci.pdf}
}