Leonard Niekerken

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.

Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language

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.

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.