Yael Niv

About

Yael Niv is a Professor in the Department of Psychology at Princeton University and serves as the Director of Graduate Studies. She is affiliated with the Princeton Neuroscience Institute. Her research focuses on the neural and computational processes underlying reinforcement learning and decision-making. Her lab studies how animals and humans learn from trial and error to predict future events and act to maximize reward and minimize punishment, emphasizing the interaction of attention and memory processes with reinforcement learning to create representations that facilitate efficient learning of new tasks. Niv's work employs model-based experimentation using computational models to formulate precise hypotheses, design experiments, and analyze results. She aims to provide normative explanations of behavior through models that offer a principled understanding of the computational algorithms used by brain mechanisms and their potential optimality. A recent focus of her lab is computational cognitive neuropsychiatry, where her team uses computational tools to better diagnose, understand, and treat psychiatric illnesses such as depression, OCD, schizophrenia, and addiction. Her research is conducted under the auspices of the Rutgers-Princeton Center for Computational Cognitive Neuropsychiatry.

Research topics

• Computer Science
• Cognitive psychology
• Psychology
• Neuroscience
• Cognitive science
• Machine Learning
• Artificial Intelligence
• Biology
• Knowledge management
• Social psychology

Selected publications

• Reward prediction errors create event boundaries in memory

  Cognition · 2020 · 122 citations

  DOI

• The case against economic values in the orbitofrontal cortex (or anywhere else in the brain).

  Behavioral Neuroscience · 2021 · 118 citations

  Senior authorCorresponding

  Much of traditional neuroeconomics proceeds from the hypothesis that value is reified in the brain, that is, that there are neurons or brain regions whose responses serve the discrete purpose of encoding value. This hypothesis is supported by the finding that the activity of many neurons covaries with subjective value as estimated in specific tasks, and has led to the idea that the primary function of the orbitofrontal cortex is to compute and signal economic value. Here we consider an alternati…

  DOI

• Human Representation Learning

  Annual Review of Neuroscience · 2021 · 67 citations

  Senior authorCorresponding

  The central theme of this review is the dynamic interaction between information selection and learning. We pose a fundamental question about this interaction: How do we learn what features of our experiences are worth learning about? In humans, this process depends on attention and memory, two cognitive functions that together constrain representations of the world to features that are relevant for goal attainment. Recent evidence suggests that the representations shaped by attention and memory…

  DOI

• Schemas, reinforcement learning and the medial prefrontal cortex

  Nature reviews. Neuroscience · 2025-01-07 · 44 citations

  reviewSenior author
  PublisherDOI

• Affect-congruent attention modulates generalized reward expectations

  PLoS Computational Biology · 2023-12-21 · 13 citations

  articleOpen accessSenior authorCorresponding

  Positive and negative affective states are respectively associated with optimistic and pessimistic expectations regarding future reward. One mechanism that might underlie these affect-related expectation biases is attention to positive- versus negative-valence features (e.g., attending to the positive reviews of a restaurant versus its expensive price). Here we tested the effects of experimentally induced positive and negative affect on feature-based attention in 120 participants completing a co…

  PublisherDOI

Recent grants

• Quantifying the latent-cause inference process in humans

  NIH · $444k · 2019–2022

• Orbitofrontal cortex as a cognitive map of task states

  NIH · $1.8M · 2016–2022

• Neural and computational mechanisms of selective attention in decision making

  NIH · $1.1M · 2012–2017

Frequent coauthors

• Daniel Bennett

  Monash University

  43 shared

• Peter Dayan

  Max Planck Institute for Biological Cybernetics

  39 shared

• Geoffrey Schoenbaum

  National Institute on Drug Abuse

  39 shared

• Nathaniel D. Daw

  Princeton University

  29 shared

• Samuel J. Gershman

  Harvard University

  26 shared

• Nicolas W. Schuck

  Max Planck Institute for Human Development

  26 shared

• Angela Radulescu

  University of Minnesota Medical Center

  24 shared

• Zohar Levi

  Tel Aviv University

  24 shared

Labs

• Niv LabPI

Awards & honors

• Graduate Mentoring Award

Similar researchers at Princeton University

• Jonathan Cohenh-162
• Sabine Kastnerh-80
• Uri Hassonh-74