Self-Relevance Predicts the Aesthetic Appeal of Real and Synthetic Artworks Generated via Neural Style Transfer.

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Abstract

What determines the aesthetic appeal of artworks? Recent work suggests that aesthetic appeal can, to some extent, be predicted from a visual artwork’s image features. Yet a large fraction of variance in aesthetic ratings remains unexplained and may relate to individual preferences. We hypothesized that an artwork’s aesthetic appeal depends strongly on self-relevance. In a first study (N = 33 adults, online replication N = 208), rated aesthetic appeal for real artworks was positively predicted by rated self-relevance. In a second experiment (N = 45 online), we created synthetic, self-relevant artworks using deep neural networks that transferred the style of existing artworks to photographs. Style transfer was applied to self-relevant photographs selected to reflect participant-specific attributes such as autobiographical memories. Self-relevant, synthetic artworks were rated as more aesthetically appealing than matched control images, at a level similar to human-made artworks. Thus, self-relevance is a key determinant of aesthetic appeal, independent of artistic skill and image features.