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Human categorization is one of the most important and successful targets ofcognitive modeling in psychology, yet decades of development and assessment ofcompeting models have been contingent on small sets of simple, artificialexperimental stimuli. Here we extend this modeling paradigm to the domain ofnatural images, revealing the crucial role that stimulus representation playsin categorization and its implications for conclusions about how people formcategories. Applying psychological models of categorization to natural imagesrequired two significant advances. First, we conducted the first large-scaleexperimental study of human categorization, involving over 500,000 humancategorization judgments of 10,000 natural images from ten non-overlappingobject categories. Second, we addressed the traditional bottleneck ofrepresenting high-dimensional images in cognitive models by exploring the bestof current supervised and unsupervised deep and shallow machine learningmethods. We find that selecting sufficiently expressive, data-drivenrepresentations is crucial to capturing human categorization, and using theserepresentations allows simple models that represent categories with abstractprototypes to outperform the more complex memory-based exemplar accounts ofcategorization that have dominated in studies using less naturalistic stimuli.

Capturing human categorization of natural images at scale by combining deep networks and cognitive models