Cross-Species RSA Reveals Conserved Early Visual Alignment but Divergent Higher-Area Rankings Across Human fMRI and Macaque Electrophysiology
Brain and artificial neural networks align similarly across species—but only for early vision
Different learning rules—the mathematical recipes that train artificial neural networks—produce surprisingly similar patterns of brain alignment in early visual areas of both humans and macaques. But in higher visual areas, the learning rule matters far less than the overall power and training data of the network itself, suggesting that basic visual processing follows similar rules across primates, while more complex vision relies on factors beyond how the network learns.
Understanding which principles are shared across primate brains helps neuroscientists and AI researchers build better models of vision. The finding that early visual processing is robust and rule-agnostic suggests this is a fundamental principle worth mimicking in artificial systems, while the brittleness of higher visual areas points to practical limits: you can't match complex visual reasoning by tweaking learning algorithms alone—you need better training data and larger networks.