Bayesian hierarchical modeling can provide novel insights into the mechanistic processes that allow humans to complete cognitive tasks. This method may be especially useful when cognitive models from experimental psychology are applied in clinical or neuroimaging research because it allows complex models to be fit even in situations where behavioral data from individual participants is sparse. This presentation will provide a general overview of Bayesian cognitive modeling methods, and of the sometimes challenging task of addressing concerns from reviewers who may be less familiar with them, using an example of a paper that Alex and his co-authors have recently been working through the review process with. Topics discussed will include assessing model fit in this framework and describing Bayesian methods for modeling and hypothesis testing to Frequentist readers.