  Cognitive modelling shares many features with statistical modelling, making
it seem trivial to borrow from the practices of robust Bayesian statistics to
protect the practice of robust cognitive modelling. We take one aspect of
statistical workflow-prior predictive checks-and explore how they might be
applied to a cognitive modelling task. We find that it is not only the
likelihood that is needed to interpret the priors, we also need to incorporate
experiment information as well. This suggests that while cognitive modelling
might borrow from statistical practices, especially workflow, care must be made
to make the adaptions necessary.
