We show how dynamic (changing) environments can affect choice behavior, and highlight the challenges that recent models face in explaining the learning and selection of heuristic strategies under such conditions, especially when decisions are made using only a small subset of the available information. We propose an enhanced modeling framework that includes a trial-by-trial implementation of a Bayesian adaptive toolbox, redefinition of heuristic strategies, and incorporation of intricate learning rate mechanisms into a strategy learning model. We use data from a new empirical study to show how this improves the quality of inference

We develop a quantum probability model that can account for situations where people’s causal judgments violate the properties of causal Bayes nets and demonstrate how the parameters of our model can be interpreted to provide information about underlying cognitive processes. We implement this model within a hierarchical Bayesian inference framework that allows us to systematically identify individual differences and also provide a latent classification of individuals into categories of causal and associative reasoners. Finally, we implement a basic normative causal Bayes net within the same inference framework that allows us to directly compare quantum and classical probability models using Bayes factors