Jerry Tang

This study uses HD-DOT to encode and decode semantic representations from naturalistic movies, showing it can capture high-dimensional semantic maps similar to fMRI.

The paper investigates how temporal prediction shapes neural speech representations, finding that brain responses to natural speech become progressively straighter along the cortical hierarchy.

This study decodes concept representations in aphasia using fMRI to generate continuous word sequences, showing that concept representations are largely preserved despite aphasia.

This study introduces cross-participant encoding models for rapid cortical mapping, showing improved accuracy and efficiency compared to within-participant models.

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 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 shows that cortical regions near visual systems combine visual and linguistic information for concepts, while language regions use mostly linguistic information, with concrete concepts containing more visual information.