The Effect of Reward Prediction Errors on Subjective Affect Depends on Outcome Valence and Decision Context

The valence of an individual's emotional response to an event is often thought to depend on their prior expectations for the event: better-than-expected outcomes produce positive affect and worse-than-expected outcomes produce negative affect. In recent years, this hypothesis has been instantiated within influential computational models of subjective affect that assume the valence of affect is driven by reward prediction errors. However, there remain a number of open questions regarding this association. In this project, we investigated the moderating effects of outcome valence and decision context (Experiment 1: free vs. forced choices; Experiment 2: trials with vs. trials without counterfactual feedback) on the effects of reward prediction errors on subjective affect. We conducted two online experiments (N = 300 in total) of general-population samples recruited via Prolific to complete a risky decision-making task with an embedded high-resolution sampling of subjective affect. Hierarchical Bayesian computational modeling revealed that both outcome amount and reward prediction errors influenced subjective affect, but that the effects of reward prediction errors were moderated by both outcome valence and decision context. Specifically, we found evidence that only negative reward prediction errors (worse-than-expected outcomes) influenced subjective affect, with no significant effect of positive reward prediction errors (better-than-expected outcomes). Moreover, these effects were only apparent on trials in which participants made a choice freely (but not on forced-choice trials) and when counterfactual feedback was absent (but not when counterfactual feedback was present). These results deepen our understanding of the effects of reward prediction errors on subjective affect.