Sparse autoencoder features from LMs plus surprisal predict fMRI language responses, recovering prior interpretations and revealing a people-tuned voxel population while showing frontal areas are surprisal-driven and general features outperform arbitrary ones.
Generative causal testing to bridge data-driven models and scientific theories in language neuroscience.arXiv preprint arXiv:2410.00812,
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Interpreting Brain Responses to Language with Sparse Features from Language Models
Sparse autoencoder features from LMs plus surprisal predict fMRI language responses, recovering prior interpretations and revealing a people-tuned voxel population while showing frontal areas are surprisal-driven and general features outperform arbitrary ones.