Mariano Schain

Information-making processes in the speaker's brain drive human conversations forward

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.

Deep speech-to-text models capture the neural basis of spontaneous speech in everyday conversations

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.

Correspondence between the layered structure of deep language models and temporal structure of natural language processing in the human brain

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.

Brain embeddings with shared geometry to artificial contextual embeddings, as a code for representing language 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.

Thinking ahead: prediction in context as a keystone of language in humans and machines

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.