  Traditional models of category learning in psychology focus on representation
at the category level as opposed to the stimulus level, even though the two are
likely to interact. The stimulus representations employed in such models are
either hand-designed by the experimenter, inferred circuitously from human
judgments, or borrowed from pretrained deep neural networks that are themselves
competing models of category learning. In this work, we extend classic
prototype and exemplar models to learn both stimulus and category
representations jointly from raw input. This new class of models can be
parameterized by deep neural networks (DNN) and trained end-to-end. Following
their namesakes, we refer to them as Deep Prototype Models, Deep Exemplar
Models, and Deep Gaussian Mixture Models. Compared to typical DNNs, we find
that their cognitively inspired counterparts both provide better intrinsic fit
to human behavior and improve ground-truth classification.
