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 taken to make the necessary adaptions. [This paper was published in "Computational Brain and Behavior" v2 p210-217 2019.]