Modelling autonomous agents (such as humans) requires us to make inferences about inference algorithms they might run internally to make decisions based on stimuli. Algorithms for such doubly Bayesian inference problems have been dubbed cognitive tomography, and underlie a set of recent results in cognitive science. In order to accelerate the development of such algorithms, we want to make use of probabilistic programming languages (PPL) that allow us to run inference by just specifying the statistical model. The student will participate in specifying a class of models for which automation is the most useful, and implementing and testing of the inference algorithm in a PPL.