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What might sound like the beginning of a joke has become an attractiveprospect for many cognitive scientists: the use of deep neural network models(DNNs) as models of human behavior in perceptual and cognitive tasks. AlthoughDNNs have taken over machine learning, attempts to use them as models of humanbehavior are still in the early stages. Can they become a versatile model classin the cognitive scientist's toolbox? We first argue why DNNs have thepotential to be interesting models of human behavior. We then discuss how thatpotential can be more fully realized. On the one hand, we argue that the cycleof training, testing, and revising DNNs needs to be revisited through the lensof the cognitive scientist's goals. Specifically, we argue that methods forassessing the goodness of fit between DNN models and human behavior have todate been impoverished. On the other hand, cognitive science might have tostart using more complex tasks (including richer stimulus spaces), but doing somight be beneficial for DNN-independent reasons as well. Finally, we highlightavenues where traditional cognitive process models and DNNs may show productivesynergy.

A neural network walks into a lab: towards using deep nets as models for human behavior