Using Deep-Learning Representations of Complex Natural Stimuli as Input to Psychological Models of Classification

AbstractTests of formal models of human categorization have traditionally been restricted to artificial categories because deriving psychological representations for large numbers of natural stimuli has been an intractable task. We show that deep learning may be used to solve this problem. We train an ensemble of convolutional neural networks (CNNs) to produce the multidimensional scaling (MDS) coordinates of images of rocks. We then show that not only are the CNNs able to predict the MDS coordinates of a held-out test set of rocks, but that the CNN-derived representations can be used in combination with a formal psychological model to predict human categorization behavior on a completely new set of rocks.

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