Using Bayes factors to test the predictions of models: A case study in visual working memory

A critical property of Bayesian model selection, via Bayes factors, is that they test the predictions made by models. Such predictions are a joint function of the likelihood of the model, and the prior distributions placed on the parameters of the model. Prior distributions that are informed by previous data lead to more constrained predictions, and result in Bayes factors that test more specific versions of the models under question. We present a case study applying two models of visual working memory to a series of experiments. We outline a process by which the posterior distributions from previous experiments are used to define and update prior distributions for each subsequent experiment. For each experiment we obtain Bayes factors that test the predictions that these models make, and update our beliefs about the relative likelihood of each model.