Mechanistically Interpretable Neural Encoding Reveals Fine-Grained Functional Selectivity in Human Visual Cortex

In three linesMINE (Mechanistically Interpretable Neural Encoding) applies mechanistic interpretability to neural encoding models to identify visual features driving activation in individual voxels of human visual cortex. Using language-aligned image representations and counterfactual editing, the approach causally validates fine-grained selectivity in category-selective brain regions.

Summary generated by Claude — human-verified