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We introduce a framework that uses Generative Adversarial Networks (GANs) tostudy cognitive properties like memorability, aesthetics, and emotionalvalence. These attributes are of interest because we do not have a concretevisual definition of what they entail. What does it look like for a dog to bemore or less memorable? GANs allow us to generate a manifold of natural-lookingimages with fine-grained differences in their visual attributes. By navigatingthis manifold in directions that increase memorability, we can visualize whatit looks like for a particular generated image to become more or lessmemorable. The resulting ``visual definitions" surface image properties (like``object size") that may underlie memorability. Through behavioral experiments,we verify that our method indeed discovers image manipulations that causallyaffect human memory performance. We further demonstrate that the same frameworkcan be used to analyze image aesthetics and emotional valence. Visit theGANalyze website at http://ganalyze.csail.mit.edu/.

GANalyze: Toward Visual Definitions of Cognitive Image Properties