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

Counterfactual emotions including regret and disappointment play a crucial role in how people make decisions. For example, people often behave such that their decisions minimize potential regret or disappointment and therefore maximize subjective pleasure. Importantly, functional accounts of emotion 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 cognitive-emotional mechanisms through which counterfactual thinking facilitates changes in behavior remain unclear. Here, we leverage computational models of risky decision-making that extend regret and disappointment theory to experience-based tasks, which we use to determine how people learn counterfactual representations of 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 determine how experienced emotion may influence cognitive mechanisms of learning, reward sensitivity, 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: (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. Our findings support functional accounts of regret and disappointment and demonstrate the potential for computational modeling and model-based facial expression analysis to enhance our understanding of cognition-emotion interactions.

Intact counterfactual emotion processing in autism spectrum disorder: Evidence from eye-tracking

Counterfactual emotions, such as regret and relief, require an awareness of how things could have been different. We report a pre-registered experiment that examines the real-time understanding of counterfactual emotions in adults with and without ASD, based on research showing that the developmental trajectory of counterfactual thinking may be disrupted in people with ASD. Participants were eye-tracked as they read narratives in which a character made an explicit decision then subsequently experienced either a mildly negative or positive outcome. The final sentence in each story included an explicit remark about the character’s mood that was either consistent or inconsistent with the character’s expected feelings of regret or relief (e.g. “… she feels happy/annoyed about her decision.”). Results showed that adults with ASD are unimpaired in understanding emotions based on counterfactual reasoning, and in fact showed earlier sensitivity to inconsistencies within relief contexts compared to TD participants. This finding highlights a previously unknown strength in empathy and emotion processing in adults with ASD, which may have been masked in previous research that has typically relied on explicit, response-based measures to record emotional inferences, which are likely to be susceptible to demand characteristics and response biases. This study therefore highlights the value of employing implicit measures that provide insights on peoples’ immediate responses to emotional content without disrupting ongoing processing.

The costs of giving up: Action versus inaction asymmetries in regret

Kurzban et al.'s opportunity cost model of mental effort relies heavily on counterfactual thinking. We suggest that a closer inspection of the role of counterfactual emotions, and particularly of action/inaction asymmetries in anticipated regret, may be important in understanding the role of opportunity costs in decisions to persist with a current task.