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

The experiment is just as important as the likelihood in understanding the prior: A cautionary note on robust cognitive modelling