Directional Confusions Reveal Divergent Inductive Biases Through Rate-Distortion Geometry in Human and Machine Vision
arXiv:2604.21909v1 Announce Type: new
Abstract: Humans and modern vision models can reach similar classification accuracy while making systematically different kinds of mistakes – differing not in how often they err, but in who gets mistaken for whom,…