Abstract
Vessel, E. A., Pasqualette, L., Uran, C., Koldehoff, S., Bignardi, G., & Vinck, M. (2023). Self-relevance predicts the aesthetic appeal of real and synthetic artworks generated via neural style transfer. Psychological Science. https://doi.org/10.1177/09567976231188107
An error in the production process led to the removal of the open practices statement. The open practices statement below has been added to the article and should now appear correct online and in print.
Open Practices
The studies reported in this article were not preregistered. Deidentified data and code for all studies are publicly accessible at https://osf.io/6zxc5. Executable code and tutorial to reproduce Variance Component Analysis as carried out in this study are available at https://github.com/giacomobignardi/empirical-aesthetics-VCA/tree/main. Generated artworks are also available. Real artworks are not included as they contain images for which the authors do not have distribution rights. Please direct requests for access to the corresponding author.