Q-learning model of insight problem solving and the effects of learning traits on creativity

Abstract

Despite the fact that insight is a crucial component of creative thought, the means by which it is cultivated remain unknown. The effects of learning traits on insight, specifically, has not been the subject of investigation in pertinent research. This study quantitatively examines the effects of individual differences in learning traits estimated using a Q-learning model within the reinforcement learning framework and evaluates their effects on insight problem solving in two tasks, the 8-coin and 9-dot problems, which fall under the umbrella term “spatial insight problems.” Although the learning characteristics of the two problems were different, the results showed that there was a transfer of learning between them. In particular, performance on the insight tasks improved with increasing experience. Moreover, loss-taking, as opposed to loss aversion, had a significant effect on performance in both tasks, depending on the amount of experience one had. It is hypothesized that loss acceptance facilitates analogical transfer between the two tasks and improves performance. In addition, this is one of the few studies that attempted to analyze insight problems using a computational approach. This approach allows the identification of the underlying learning parameters for insight problem solving.