Bobbi Aubrey

The "Podcast" ECoG dataset for modeling neural activity during natural language comprehension

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

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.

ALIGNING BRAINS INTO A SHARED SPACE IMPROVES THEIR ALIGNMENT TO LARGE LANGUAGE MODELS

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

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.

Moment-by-moment tracking of naturalistic learning and its underlying hippocampo-cortical interactions

The paper tracks memory formation and retrieval during story listening, showing neural correlates of predictive recall and hippocampo-cortical interactions.

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.