Negative Affect Induces Rapid Learning of Counterfactual Representations: A Model-based Facial Expression Analysis Approach

Abstract

Whether we are making life-or-death decisions or thinking about the best way to phrase an email, counterfactual emotions including regret and disappointment play an ever-present role in how we make decisions. Functional theories of counterfactual thinking suggest that the experience and future expectation of counterfactual emotions should promote goal-oriented behavioral change. Although many studies find empirical support for such functional theories, the generative cognitive mechanisms through which counterfactual thinking facilitates changes in behavior are underexplored. Here, we develop generative models of risky decision-making that extend regret and disappointment theory to experience-based tasks, which we use to examine how people incorporate counterfactual information into their decisions across time. Further, we use computer-vision to detect positive and negative affect (valence) intensity from participants faces in response to feedback, which we use to explore how experienced emotion may correspond to cognitive mechanisms of learning, outcome valuation, or exploration/exploitation--any of which could result in functional changes in behavior. Using hierarchical Bayesian modeling and Bayesian model comparison methods, we found that a model assuming: (1) people learn to explicitly represent and subjectively weight counterfactual outcomes with increasing experience, and (2) people update their counterfactual expectations more rapidly as they experience increasingly intense negative affect best characterized empirical data. Our findings support functional accounts of regret and disappointment and demonstrate the potential for generative modeling and model-based facial expression analysis to enhance our understanding of cognition-emotion interactions.