Zaid Zada

This study uses fMRI and HMM to show that narrative context modulates brain state dynamics and behavioral responses during story comprehension.

This study uses fMRI hyperscanning to show overlapping neural networks for speech production and comprehension during real-time conversations, extending beyond the language network into social cognition areas.

The paper introduces a dataset of ECoG recordings from nine participants listening to a podcast, along with linguistic features and tutorials for analysis.

This study shows that cortical language areas are connected through a hierarchy of linguistic features, with higher-order regions using more abstract language features.

This study shows that speakers' brains generate information-rich words through extra neural computation, evidenced by stronger neural activity before articulation, and that LLMs similarly require deeper computation for improbable words.

The paper shows that linguistic embedding spaces can capture neural alignment during conversations, revealing shared meaning between speakers and listeners.

The paper shows that larger language models better capture linguistic structure and predict neural activity, with performance peaking in earlier layers as model size increases.

The paper shows that aligning brains into a shared space improves LLM-based encoding model performance and denoising of individual brain responses.

The paper uses a deep speech-to-text model to predict neural responses during spontaneous conversations, revealing a distributed cortical hierarchy for speech and language processing.

This study finds a correspondence between the layered structure of deep language models and the temporal structure of neural activity during language processing in the human brain.

The paper shows that brain embeddings from the IFG share geometric structure with DLM contextual embeddings, enabling zero-shot encoding and decoding of unseen words.

In nine epilepsy patients, cortical activity carries next-word information up to 800 ms before onset, tracks GPT-2 surprise after onset, and fits GPT-2 contextual embeddings better than static ones.