encoding-models

This paper investigates how language model representations predict neural activity during naturalistic language comprehension across MEG, ECoG, and other recordings. The findings demonstrate that language model features serve as useful neural predictors, but caution against overinterpreting predictive success as evidence for shared neural organization.

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.