A latent-mixture quantum probability model of causal reasoning within a Bayesian inference framework

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.