  Deep learning, computational neuroscience, and cognitive science have
overlapping goals related to understanding intelligence such that perception
and behaviour can be simulated in computational systems. In neuroimaging,
machine learning methods have been used to test computational models of sensory
information processing. Recently, these model comparison techniques have been
used to evaluate deep neural networks (DNNs) as models of sensory information
processing. However, the interpretation of such model evaluations is muddied by
imprecise statistical conclusions. Here, we make explicit the types of
conclusions that can be drawn from these existing model comparison techniques
and how these conclusions change when the model in question is a DNN. We
discuss how DNNs are amenable to new model comparison techniques that allow for
stronger conclusions to be made about the computational mechanisms underlying
sensory information processing.
