Memory Modeling of Counterfactual Generation

We use a computational model of memory search to study how people generate counterfactual outcomes in response to an established target outcome. Hierarchical Bayesian model fitting to data from six experiments reveals that counterfactual outcomes that are perceived as more desirable and more likely to occur are also more likely to come to mind and are generated earlier than other outcomes. Additionally, core memory mechanisms such as semantic clustering and word frequency biases have a strong influence on retrieval dynamics in counterfactual thinking. Finally, we find that the set of counterfactuals that come to mind can be manipulated by modifying the total number of counterfactuals that participants are prompted to generate, and our model can predict these effects. Overall, our findings demonstrate how computational memory search models can be integrated with current theories of counterfactual thinking to provide novel insights into the process of generating counterfactual thoughts.