  The success of methods based on artificial neural networks in creating
intelligent machines seems like it might pose a challenge to explanations of
human cognition in terms of Bayesian inference. We argue that this is not the
case, and that in fact these systems offer new opportunities for Bayesian
modeling. Specifically, we argue that Bayesian models of cognition and
artificial neural networks lie at different levels of analysis and are
complementary modeling approaches, together offering a way to understand human
cognition that spans these levels. We also argue that the same perspective can
be applied to intelligent machines, where a Bayesian approach may be uniquely
valuable in understanding the behavior of large, opaque artificial neural
networks that are trained on proprietary data.
