EMC2: An R Package for cognitive models of choice

We introduce EMC2, an R package for Bayesian hierarchical analysis of cognitive models of choice. EMC2 bridges the gap between standard regression analyses and cognitive modeling through linear-model specifications for each type of cognitive model parameter. The flexible implementation of the linear modeling language allows users to map model parameters directly to complicated designs and hypotheses. EMC2 implements recent developments in Bayesian parameter estimation and hypothesis testing, including powerful and efficient sampling and marginal likelihood estimation algorithms, making it computationally feasible to estimate many different cognitive models and perform inference among them. Using two leading evidence-accumulation models, we illustrate how EMC2 provides a workflow that makes it easy to specify diverse parameterizations and informative priors, and to evaluate, refine, compare, and interpret models.