An extremely coarse feedback signal is sufficient for learning human-aligned visual representations

ArXi:2605.05556v1 Announce Type: new Artificial neural networks trained on visual tasks develop internal representations resembling those of the primate visual system, a discovery that has guided a decade of computational neuroscience. Research on building brain-aligned models has progressively embraced finer-grained supervisory signals, from object classification to contrastive self-supervised objectives that maximize distinctions among individual images, yet the role of supervisory signal granularity on brain alignment remains largely unexamined.