Amanda LeBel

The paper investigates how attention modulates visual-semantic representations in the cerebellum using fMRI and encoding models, finding significant tuning shifts in specific cerebellar regions.

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

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.

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.