Comparing models allows us to test different hypotheses regarding the computational basis of perception and action. One difficulty in model comparison is that it requires testing stimuli for which the brain can dissociate between different processes. Bayesian adaptive stimuli selection can help to overcome this difficulty by selecting stimuli that are most informative for the model comparison. This approach can be used to test different hypotheses regarding the computational basis of perception and action, and can provide insights into the neural mechanisms underlying these processes.