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Traditional models of category learning in psychology focus on representationat the category level as opposed to the stimulus level, even though the two arelikely to interact. The stimulus representations employed in such models areeither hand-designed by the experimenter, inferred circuitously from humanjudgments, or borrowed from pretrained deep neural networks that are themselvescompeting models of category learning. In this work, we extend classicprototype and exemplar models to learn both stimulus and categoryrepresentations jointly from raw input. This new class of models can beparameterized by deep neural networks (DNN) and trained end-to-end. Followingtheir namesakes, we refer to them as Deep Prototype Models, Deep ExemplarModels, and Deep Gaussian Mixture Models. Compared to typical DNNs, we findthat their cognitively inspired counterparts both provide better intrinsic fitto human behavior and improve ground-truth classification.

End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior