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This article is about the cognitive science of visual art. Artists createphysical artifacts (such as sculptures or paintings) which depict people,objects, and events. These depictions are usually stylized rather thanphoto-realistic. How is it that humans are able to understand and createstylized representations? Does this ability depend on general cognitivecapacities or an evolutionary adaptation for art? What role is played bylearning and culture?  Machine Learning can shed light on these questions. It's possible to trainconvolutional neural networks (CNNs) to recognize objects without training themon any visual art. If such CNNs can generalize to visual art (by creating andunderstanding stylized representations), then CNNs provide a model for howhumans could understand art without innate adaptations or cultural learning. Iargue that Deep Dream and Style Transfer show that CNNs can create a basic formof visual art, and that humans could create art by similar processes. Thissuggests that artists make art by optimizing for effects on the humanobject-recognition system. Physical artifacts are optimized to evoke real-worldobjects for this system (e.g. to evoke people or landscapes) and to serve assuperstimuli for this system.

Sensory Optimization: Neural Networks as a Model for Understanding and Creating Art