Many psychological theories assume that different cognitive processes can result in the same observable responses. Multinomial processing tree (MPT) models allow researchers to disentangle mixtures of latent processes based on observed response frequencies. MPT models have recently been extended to account for participant and item heterogeneity by assuming hierarchical group-level distributions. Thereby, it has become possible to link latent cognitive processes to external covariates such as personality traits and other person characteristics. Independently, item response trees (IRTrees) have become popular for modeling response styles. Whereas cognitive and social psychology has usually focused on the experimental validation of MPT parameters at the group level, psychometric approaches consider both the item and person level, thus allowing researchers to test the convergent and discriminant validity of measurements. Bridging these different modeling approaches, Bayesian hierarchical MPT models provide an opportunity to connect traditionally isolated disciplines in psychology.