Shailee Jain

The paper provides a natural language fMRI dataset with BOLD responses and tools for voxelwise encoding models to study language comprehension.

This paper introduces a framework to improve deep learning model interpretability in language processing by simulating brain responses and identifying temporal context effects in human cortex.

This paper introduces a non-invasive brain-computer interface that decodes continuous natural language from fMRI data, demonstrating it can reconstruct speech and silent videos without requiring subject cooperation.

The paper uses voxelwise encoding models to show cerebellar language responses are primarily conceptual and socially biased compared to cortex.

This paper develops interpretable multi-timescale models to predict fMRI responses to natural speech, revealing how different brain regions encode information at varying temporal scales.

Encoding models using contextual representations from an LSTM language model improve human fMRI brain response prediction over non-contextual word embeddings in nearly every brain area and map cortical context sensitivity.