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This paper demonstrates that fine-tuning language encoding models on fMRI data improves their ability to predict neural activity from ECoG recordings, despite fMRI's lower temporal resolution. The findings suggest that abundant 'slow' fMRI data can enhance models for 'fast' ECoG data.