Scalable Interpretable Mapping of Hundreds of Visual Concepts in Human Brain Activity

Systems neuroscience and mechanistic interpretability share a central challenge: identifying interpretable components within high-dimensional, distributed representations. Recently, sparse autoencoders (SAEs) have been shown to yield rich concept dictionaries that aid in the interpretation of deep embeddings in vision foundation models. This suggests that SAEs might be a similarly powerful tool for interpreting brain activity patterns in the visual system, where a relatively small set of concept maps has been established by decades of dedicated functional localization experiments. However, limited samples and measurement noise complicate the naive application of SAEs to brain recordings. Here we use SAEs to perform scalable, interpretable mapping of visual concepts in human brain activity by following an SAE dictionary derived from a vision foundation model through a fitted voxelwise fMRI encoder. Correlating these maps with measured brain activity retrieves recognizable stimulus sets, including faces, surfers, black-and-white photographs, and tiled montages. Comparison with functional localizers supports consistency with known cortical organization, while measures of visual coherence and model-brain correspondence reveal preservation of feature identity through the fitted mapping. The same maps support intervention as well as interpretation: off-the-shelf brain decoders turn them into concept-reactivating images, and adding them to recorded neural responses produces targeted changes in reconstructed content and appearance. Across large-scale steering experiments, specific target-feature reactivation increases far beyond random map perturbations. We deliver hundreds of concept maps for the visual cortex, expanding the vocabulary of selectivity and validating their interpretation through complementary methods from neuroscience and mechanistic interpretability.