Memory is a crucial component of everyday decision making, yet little is known about how memory and choice processesinteract, and whether or not established memory regularities persist during memory-based decision making. In this paper,we introduce a novel experimental paradigm to study the differences between memory processes at play in standard listrecall versus in preferential choice. Using computational memory models, fit to data from two pre-registered experiments,we find that some established memory regularities (primacy, recency, semantic clustering) emerge in preferential choice,whereas others (temporal clustering) are significantly weakened relative to standard list recall. Notably, decision-relevantfeatures, such as item desirability, play a stronger role in guiding retrieval in choice. Our results suggest memory processesdiffer across preferential choice and standard memory tasks, and that choice modulates memory by differentially activatingdecision-relevant features such as what we like.

Lay perceptions of medical conditions and treatments determine people’s health behaviors, guide biomedical research funding, and have important consequences for both individual and societal wellbeing. Yet it has been nearly impossible to quantitatively predict lay health perceptions for hundreds of everyday diseases due to the myriad psychological forces governing health-related attitudes and beliefs. Here we present a data-driven approach that uses text explanations on healthcare websites, combined with large-scale survey data, to train a machine learning model capable of predicting lay health perception. We use our model to analyze how language influences health perceptions, interpret the psychological underpinnings of health judgment, and quantify differences between different descriptions of disease states. Our model is accurate, cost-effective, and scalable, and offers researchers and practitioners a new tool for studying health-related attitudes and beliefs.

People often think about counterfactual possibilities to an event and imagine how it could have been otherwise. The study of how this occurs is central to many areas of cognitive science, including decision making, social cognition, and causal judgment; however, modeling the memory processes at play in naturalistic counterfactual retrieval has been difficult. We use established memory models to evaluate and compare multiple mechanisms that could be involved in counterfactual retrieval. Our models are able to capture nuanced dynamics of retrieval (e.g. how retrieved counterfactuals cue subsequent counterfactuals), and can predict the effects of retrieval on evaluations and decisions. In doing so, we show how existing theories of counterfactual thinking can be combined with quantitative models of memory search to provide new insights about the formation and consequences of counterfactual thought.