Whether it’s the enigmatic playfulness of Mona Lisa’s smile or the swirling soft colors of a Monet painting, there are qualities of fine art that attract audiences, like a moth to a flame. What is it about these pieces that has captivated people throughout centuries? Researchers are now using machine-learning algorithms to tease apart these intricacies and explore the relationship between the aesthetics, sentimental value, and memorability of fine art.

Eva Cetinic is an art enthusiast and researcher at the Rudjer Boskovic Institute in Croatia. While she believes that art is indescribable in many ways, she wanted to challenge her own perspective by exploring how machine learning might quantify art. “The rise of artificial intelligence forces us to re-think what values are specifically human, and the understanding of art is a particularly fruitful playground for this kind of investigation,” she explains.

To start, Cetinic and her colleagues analyzed more than 100,000 images from WikiArt. Their results, published 5 June in IEEE Access, hint at common themes of what we find beautiful and captivating.