Abstract Deep neural networks predict neural responses across the visual hierarchy, yet published alignment scores do not reveal whether this correspondence reflects learned representations, architectural inductive biases, low-level image statistics, or categorical structure. We decompose DNN-brain alignment into four sources using variance partitioning on representational similarity analysis, applied to 7T fMRI from eight participants viewing approximately 10,000 natural images.
