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Regulatory Fit and Systematic Exploration in a Dynamic Decision-Making Environment

By A. Ross Otto, Arthur B. Markman, Todd M. Gureckis and Bradley C. Love


This work explores the influence of motivation on choice behavior in a dynamic decision-making environment, where the payoffs from each choice depend on one’s recent choice history. Previous research reveals that participants in a regulatory fit exhibit increased levels of exploratory choice and flexible use of multiple strategies over the course of an experiment. The present study placed promotion and prevention-focused participants in a dynamic environment for which optimal performance is facilitated by systematic exploration of the decision space. These participants either gained or lost points with each choice. Our experiment revealed that participants in a regulatory fit were more likely to engage in systematic exploration of the task environment than were participants in a regulatory mismatch and performed more optimally as a result. Implications for contemporary models of human reinforcement learning are discussed

Topics: decision making, reinforcement learning, exploration, motivation
Year: 2010
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