Aesthetic taste in humans and machines

Aesthetic taste remains an elusive phenomenon that has been comparatively underexplored in psychology. We argue that taste integrates four dimensions: sensitivity to fine-grained differences within a domain; valence, through which these differences come to matter affectively; orientation, the selective tendency to seek encounters that may further transform one’s sensibility; and normativity, through which judgments of taste become shareable and contestable. These are not separate dimensions but faces of a single underlying capacity: continuously monitoring one’s grip on the world and letting that monitoring modulate what happens next. This makes human engagement non-uniform. Throughout, we use current artificial intelligence as a contrast case, exploring how far it can model or replicate human taste. Machines can be sensitive to fine-grained distinctions, predict human preferences, generate apparently tasteful outputs, and shape the environments in which human taste develops. But they do not yet possess taste in the full human sense, because they lack the stake that makes human processing non-uniform: self-directed exploration, online self-transformation, and genuine normative vulnerability. The upshot is a systematic framework for the study of human taste and for assessing how AI may transform its formation.