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Implications of capacity-limited, generative models for human vision
Joseph Scott German1, Robert A Jacobs2
1Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA jgerman@ucsd.edu.
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Although discriminative deep neural networks are currently dominant in cognitive modeling, we suggest that capacity-limited, generative models are a promising avenue for future work. Generative models tend to learn both local and global features of stimuli and, when properly constrained, can learn componential representations and response biases found in people's behaviors.
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