Uri Hasson

About

Professor Uri Hasson is a faculty member in the Department of Psychology at Princeton University and is affiliated with the Princeton Neuroscience Institute. His research investigates the neural basis of brain-to-brain human communication, natural language processing, and language acquisition in children within real-world contexts. His lab studies neural responses to natural stimuli, such as audio-visual narratives, and has developed models to better understand how cognition manifests in everyday life. Inspired by advances in deep learning, the Hasson Lab aims to create new theoretical frameworks and computational tools to model neural processes involved in cognition, particularly focusing on natural language processing during open-ended conversations. His work involves analyzing intracranial EEG data collected from epileptic patients engaged in natural conversations, seeking to understand shared computational principles and differences between human brain processing and deep neural network models. His research explores whether deep language models can serve as cognitive models to explain natural language processing in the human brain, with findings suggesting that these models provide a biologically feasible framework for studying language neural mechanisms. Professor Hasson has been recognized for his contributions, including being featured in Princeton Alumni Weekly and receiving the NIH Pioneer Award.

Research topics

• Artificial Intelligence
• Computer Science
• Psychology
• Natural Language Processing
• Linguistics
• Neuroscience
• Cognitive psychology
• Communication
• Cognitive science
• Speech recognition

Selected publications

• Shared computational principles for language processing in humans and deep language models

  Nature Neuroscience · 2022 · 442 citations

  Senior authorCorresponding

  Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressiv…

  PublisherOA PDFDOI

• Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks

  Neuron · 2020 · 355 citations

  1st authorCorresponding
  DOI

• Behavioral, Physiological, and Neural Signatures of Surprise during Naturalistic Sports Viewing

  Neuron · 2020 · 157 citations

  DOI

• The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension

  Scientific Data · 2021 · 153 citations

  Senior authorCorresponding

  The "Narratives" collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardiz…

  DOI

• A shared model-based linguistic space for transmitting our thoughts from brain to brain in natural conversations

  Neuron · 2024-08-02 · 61 citations

  articleOpen accessSenior author

  Effective communication hinges on a mutual understanding of word meaning in different contexts. We recorded brain activity using electrocorticography during spontaneous, face-to-face conversations in five pairs of epilepsy patients. We developed a model-based coupling framework that aligns brain activity in both speaker and listener to a shared embedding space from a large language model (LLM). The context-sensitive LLM embeddings allow us to track the exchange of linguistic information, word by…

  PublisherDOI

Recent grants

• Neural dynamics supporting integration and recall over long timescales during natural continuous input

  NIH · $528k · 2017–2021

• Brain-to-brain dynamical Coupling: A New framework for the communication of social knowledge

  NIH · $2.0M · 2017–2024

• Speaker-listener coupling: a novel neural approach for assessing communication

  NIH · $4.5M · 2016–2024

Frequent coauthors

• Kenneth A. Norman

  Princeton University

  98 shared

• Samuel A. Nastase

  Princeton University

  63 shared

• Janice Chen

  Johns Hopkins University

  46 shared

• Rafael Malach

  Weizmann Institute of Science

  44 shared

• Bobbi Aubrey

  Princeton University

  38 shared

• Christopher J. Honey

  Johns Hopkins University

  35 shared

• Zaid Zada

  Princeton University

  32 shared

• Erez Simony

  Holon Institute of Technology

  31 shared

Education

• PhD, Neurobiology

  Weizmann Institute of Science

  2004

Awards & honors

• NIH Pioneer Award

Similar researchers at Princeton University

• Jonathan Cohenh-162
• Yael Nivh-81
• Sabine Kastnerh-80