When Things Get Worse before they Get Better: Regulatory Fit and Average-Reward Learning in a Dynamic Decision-Making Environment

Abstract

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 increased levels of exploratory choice among participants in a regulatory fit. The present study placed promotion and prevention-focused participants in a dynamic environment for which optimal performance requires that participants sustain a single choice strategy in the face of temporary payoff decreases. These participants either gained or lost points with each choice. Our behavioral results and model-based analysis, using the average-reward reinforcement learning framework, revealed differential levels of reactivity to local changes in payoffs—specifically, participants in a regulatory fit were less reactive to local perturbations in payoffs than participants in a regulatory mismatch and performed more optimally as a result.


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