Cognitive measurement models decompose observed behavior into latent cognitive processes. For situations with more than one condition, such models allow to test hypotheses on the level of the latent processes. We propose a fully Bayesian ensemble model approach to test hypotheses on the level of the latent processes in situations in which multiple measurement models or model classes exist. In the first step, one needs to perform a Bayesian model selection step comparing the hypotheses within each model class. Aggregating the results of the first step yields ensemble posterior model probabilities. We provide an example for a working memory data set using an ensemble of a resource model and a slots model.