BayesCog: A freely available course in Bayesian statistics and hierarchical Bayesian modeling for psychological science

We present BayesCog, an openly-available online course for the computational modeling of human behaviour (i.e., cognitive modeling) using Bayesian inference, with reinforcement learning as a core example throughout the course. Assuming little to no prior experience, audience of this course will be formally grounded in key concepts including Bayesian statistics and reinforcement learning, and practically, will build, assess, compare, and validate models using the R interface to the Stan programming language, RStan. Starting with binary choice models, the audience will learn to estimate parameters representing latent components of behaviour by fitting reinforcement learning models, both at the individual and group-level, eventually with hierarchical modeling. The course is generally suitable for those interested in developing models of human cognition at any level of experience. In making the course openly available, we aim for computational modeling under the Bayesian approach to be more strongly represented in the psychological sciences.