Regret-an emotion comprised of both a counterfactual, cognitive component and a negative, affective component-is one of the most commonly experienced emotions involved in decision making. For example, people often behave such that their decisions minimize potential regret and therefore maximize subjective pleasure. Importantly, functional accounts of emotion suggest that the experience and future expectation of regret should promote goal-oriented behavioral change. While many studies have confirmed the functional role of regret, the cognitive-emotional mechanisms through which regret facilitates behavioral change remain unclear. We hypothesize that a greater negative affective state accompanying the counterfactual component of regret will potentiate learning, value, and/or exploration of potential options-all of which could lead to behavioral change. Because prior studies on regret-driven decision making have focused almost exclusively on description-based paradigms, the potential role of learning from regret through experience is underexplored. Here, we leverage computational, emotion-driven models of decision making to determine the role of regret in risky decision making. Further, we use computer-vision to detect positive and negative affect (valence) intensity from subjects’ faces in response to feedback, which was entered as direct input into each model to determine whether valence affects learning, valuation, or exploration/exploitation. Using multiple model comparison methods, we found that: (1) people weight regret by its expectedness when making experience-based decisions, and (2) people learn more rapidly as they experience increasingly intense negative affect. Our findings support functional accounts of regret and demonstrate the potential for model-based facial expression analysis to enhance our understanding of cognition-emotion interactions.