Proceedings of the Annual Meeting of the Cognitive Science Society

Understanding how people represent categories is a core prob-lem in cognitive science. Decades of research have yieldeda variety of formal theories of categories, but validating themwith naturalistic stimuli is difficult. The challenge is that hu-man category representations cannot be directly observed andrunning informative experiments with naturalistic stimuli suchas images requires a workable representation of these stimuli.Deep neural networks have recently been successful in solvinga range of computer vision tasks and provide a way to com-pactly represent image features. Here, we introduce a methodto estimate the structure of human categories that combinesideas from cognitive science and machine learning, blendinghuman-based algorithms with state-of-the-art deep image gen-erators. We provide qualitative and quantitative results as aproof-of-concept for the method’s feasibility. Samples drawnfrom human distributions rival those from state-of-the-art gen-erative models in quality and outperform alternative methodsfor estimating the structure of human categories.