Kenneth Norman

About

Kenneth Norman is a Professor in Computational and Theoretical Neuroscience at Princeton University, affiliated with the Princeton Neuroscience Institute. His research focuses on using computational models to explore how the brain gives rise to learning and memory phenomena. Norman's lab employs neuroimaging studies to decode thoughts as individuals learn and remember, investigating questions such as the learning rules that govern memory modification, the role of sleep in learning, how memories are time-stamped, and methods for intentionally forgetting memories. His work involves developing new machine learning methods for analyzing distributed neural activity patterns, including data mining algorithms to isolate fMRI and EEG signatures of specific thoughts and memories. Norman's research also includes creating real-time neurofeedback techniques that adapt studies based on participants' thoughts. His contributions extend to advancing understanding of mental illness through collaborative research and developing innovative analysis tools for neuroimaging data.

Research topics

• Psychology
• Natural Language Processing
• Artificial Intelligence
• Computer Science
• Neuroscience
• Cognitive psychology
• Linguistics
• Machine Learning
• Mathematics
• Cognitive science

Selected publications

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

  Nature Neuroscience · 2022 · 442 citations

  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

• Structured Event Memory: A neuro-symbolic model of event cognition.

  Psychological Review · 2020 · 172 citations

  (SEM) model of event cognition, which accounts for human abilities in event segmentation, memory, and generalization. SEM is derived from a probabilistic generative model of event dynamics defined over structured symbolic scenes. By embedding symbolic scene representations in a vector space and parametrizing the scene dynamics in this continuous space, SEM combines the advantages of structured and neural network approaches to high-level cognition. Using probabilistic reasoning over this generati…

  DOI

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

  Neuron · 2020 · 157 citations

  Senior authorCorresponding
  DOI

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

  Scientific Data · 2021 · 153 citations

  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

• Reward prediction errors create event boundaries in memory

  Cognition · 2020 · 122 citations

  DOI

Recent grants

• Computational, Neural, and Behavioral Studies of Competition-Dependent Learning

  NIH · $7.2M · 2004–2030

• NRSA Training Grant in Quantitative Neuroscience

  NIH · $7.1M · 2002–2029

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

  NIH · $528k · 2017–2021

Frequent coauthors

• Nicholas B. Turk‐Browne

  Yale University

  99 shared

• Uri Hasson

  Neuroscience Institute

  98 shared

• Jonathan D. Cohen

  Princeton University

  56 shared

• Alex Nguyen

  Concordia University

  34 shared

• Victoria J. H. Ritvo

  Princeton University

  32 shared

• Samuel J. Gershman

  Harvard University

  31 shared

• Sebastian Michelmann

  Princeton University

  28 shared

• Samuel A. Nastase

  Princeton University

  28 shared

Labs

• Princeton Computational Memory LabPI

  Principal Investigator Ken Norman Huo Professor in Computational and Theoretical Neuroscience Professor of Psychology and Neuroscience Ph.D., Harvard University (1999) M.A., Harvard University (199…

Awards & honors

• Psychonomic Society Mid-Career Award

Similar researchers at Princeton University

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• Sabine Kastnerh-80