Health AI Central
Peer-Reviewed Publication
ArXiv2026May 14, 2026Journal Article

Directional Confusions Reveal Divergent Inductive Biases Through Rate-Distortion Geometry in Human and Machine Vision.

Leyla R Caglar1, Pedro A M Mediano2, Baihan Lin3
1Windreich Department of AI and Human Health, Icahn School of Medicine, at Mount Sinai, New York, NY, USA.
2Department of Computing, Imperial College London, London, UK.
3Windreich Department of AI, and Human Health, Department of Psychiatry, Icahn School of Medicine, at Mount Sinai, New York, NY, USA.
PubMed PMC PDF

Abstract

To humans, a robin seems more like a bird than a bird seems like a robin, but does this asymmetry also hold for machine vision? Humans and modern vision models can match each other in accuracy while making systematically different kinds of errors, differing not in how often they fail, but in who gets mistaken for whom. We show these directional confusions reveal distinct inductive biases invisible…

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