Artificial intelligence has emerged as a powerful computational tool to create artworks. One application is Neural Style Transfer, which allows to transfer the style of one image, such as a painting, onto the content of another image, such as a photograph. In the present study, we ask how Neural Style Transfer affects objective image properties and how beholders perceive the novel (style-transferred) stimuli. In order to focus on the subjective perception of artistic style, we minimized the confounding effect of cognitive processing by eliminating all representational content from the input images. To this aim, we transferred the styles of 25 diverse abstract paintings onto 150 colored random-phase patterns with six different Fourier spectral slopes. This procedure resulted in 150 style-transferred stimuli. We then computed eight statistical image properties (complexity, self-similarity, edge-orientation entropy, variances of neural network features, and color statistics) for each image. In a rating study, we asked participants to evaluate the images along three aesthetic dimensions (Pleasing, Harmonious, and Interesting). Results demonstrate that not only objective image properties, but also subjective aesthetic preferences transferred from the original artworks onto the style-transferred images. The image properties of the style-transferred images explain 50 – 69% of the variance in the ratings. In the multidimensional space of statistical image properties, participants considered style-transferred images to be more Pleasing and Interesting if they were closer to a “sweet spot” where traditional Western paintings (JenAesthetics dataset) are represented. We conclude that NST is a useful tool to create novel artistic stimuli that preserve the image properties of the input style images. In the novel stimuli, we found a strong relationship between statistical image properties and subjective ratings, suggesting a prominent role of perceptual processing in the aesthetic evaluation of abstract images.

In this exploratory study, we asked whether objective statistical image properties can predict subjective aesthetic ratings for a set of 48 abstract paintings created by the artist Robert Pepperell. Ruta and colleagues (2021) used the artworks previously to study the effect of curved/angular contour on liking and wanting decisions. We related a predefined set of statistical image properties to the eight different dimensions of aesthetic judgments from their study. Our results show that the statistical image properties can predict a large portion of the variance in the different aesthetic judgments by Ruta and colleagues. For example, adjusted R 2 values for liking, attractiveness, visual comfort, and approachability range between 0.52 and 0.60 in multiple linear regression models with four predictors each. For wanting judgments in an (imagined) gallery context, the explained variance is even higher (adjusted R 2 of 0.78). To explain these findings, we hypothesize that differences in cognitive processing of Pepperell’s abstract paintings are minimized because this set of stimuli has no apparent content and is of uniform artistic style and cultural context. Under this condition, the aesthetic ratings by Ruta and colleagues are largely based on perceptual processing that systematically varies along a relatively small set of objective image properties.