Beyond Object-Level Alignment: Do Brains and DNNs Preserve the Same Transformations?

Abstract

Brain-DNN alignment is usually assessed through stimulus-level correspondence or stimulus-set geometry. Inspired by category theory, we operationalize a different question: do brain and model preserve the same candidate transformations among stimuli? We formalize this as approximate naturality: if a proxy-defined stimulus change is propagated through the brain side and then translated to the model side, the result should match translating first and then propagating, so that the naturality square approximately commutes. We quantify deviations from commutativity by a Naturality Violation Score (NVS) normalized to a permutation null, shifting alignment from per-stimulus sameness to preservation of structure under an explicitly chosen comparison map. As a proof of concept, a controlled five-factor synthetic setting shows that NVS separates complementary alignment failures that aggregate object- and geometry-level scalars cannot resolve. Applied to fMRI responses from the GOD dataset (5 subjects), 3 vision DNNs, and 3 World-Model proxy embeddings, the axis-resolved analysis reveals a hierarchy crossover: semantic axes align most strongly toward HVC and deeper DNN layers (NVS^animacy = 0.39 vs 0.52 for the next-best axis and 1.0 for the permutation-null baseline), whereas low- and mid-level visual axes align toward earlier visual cortex and shallower layers. Supporting analyses (a 15-axis appendix atlas, dissociation tests against RSA/CKA and encoding/decoding accuracy, and a W-less anchor-ablation control) confirm that the alignment is selective over candidate morphism families rather than uniform. NVS thereby turns brain-DNN comparison into a test of jointly preserved candidate transformations, relative to an explicit proxy space and permutation null, and opens a path to richer proxy spaces and controlled world-side transformations.