Uri Hasson

This study shows that the multiple-demand network encodes content-sensitive linguistic representations during naturalistic comprehension, independent of cognitive demand.

The paper develops an agent-based model of early language acquisition using the First 1,000 Days dataset, showing that children's language learning can be simulated with a learning agent that discovers speech units and words, mirroring developmental trajectories.

The 1kD project collects and analyzes ultra-dense longitudinal data of infant development, revealing stable household-specific lexical structures not captured by sparse sampling.

The paper investigates how music-evoked reactivation in the default mode network during film viewing relates to improved recall of naturalistic events the next day.

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

The paper investigates neural state patterns during memory search, showing hippocampal-neocortical communication facilitates retrieval of event-related information.

This study compares how LLMs and the brain process narrative context, introducing an incremental LLM that better predicts DMN neural activity.

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.

This paper analyzes the functional specialization of transformer-based language models and finds similarities with the human brain's cortical language network.

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.

This study examines how neural synchronization between speakers and listeners during storytelling reflects a 'herding effect' where listeners follow the speaker's brain activity patterns, enhancing narrative engagement.

This study evaluates the test-retest reliability of OPM-MEG for whole-brain functional connectivity, showing high group-level repeatability and individual robustness across theta, alpha, and beta frequency ranges.

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.

The paper investigates how the brain updates memory representations of naturalistic events when new information changes understanding of the past, showing dynamic neural changes in the default mode network.

The paper uses fMRI to show a fixed sequence of response lags between brain networks during narrative comprehension, consistent across stories and supported by a computational model.

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.

This study uses real-time fMRI neurofeedback to influence interpretations of a naturalistic narrative, finding that higher decoding accuracy correlates with successful interpretation shifts.

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.

This study uses a neural network model to show that selective retrieval and encoding of episodic memories improves prediction performance by reducing the risk of irrelevant memory retrieval.

The paper uses fMRI and hidden Markov models to show that brain regions involved in narrative event segmentation also represent high-level event structure in music.

This paper uses fine-tuned deep language models to model differences in narrative interpretation based on listeners' perspectives, showing alignment with neural responses and cognitive networks.

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

This study shows that neural coupling between teachers and students during lectures correlates with learning outcomes, using fMRI and intersubject correlation.

This study introduces a neural measure called neural alignment to predict learning outcomes by comparing students' brain activity to experts and peers in a computer science course.

This study investigates how the brain integrates and segregates information during narrative processing, showing that neural patterns specific to storylines are reinstated during transitions and that motif reinstatement correlates with better integration of events.

The paper introduces a benchmark dataset of fMRI data from human subjects listening to naturalistic stories for evaluating language and narrative comprehension models.

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.

This study quantifies surprise during naturalistic sports viewing and identifies its behavioral, physiological, and neural correlates using basketball analytics and human observers.