Nathaniel Daw

About

Nathaniel Daw is the Huo Professor in Computational and Theoretical Neuroscience at the Department of Psychology within Princeton University, affiliated with the Princeton Neuroscience Institute. His lab studies how people and animals learn from trial and error, rewards, and punishments to make decisions, integrating computational, neural, and behavioral perspectives. The research focuses on understanding how subjects manage computationally demanding decision situations, such as choice under uncertainty or in tasks requiring sequential decisions like spatial navigation or strategic games such as chess. Daw's work draws on algorithms from machine learning to develop detailed, quantitative hypotheses about how the brain approaches these problems. Current projects include investigating how the brain controls its own decision-making processes, such as making higher-level decisions about when to deliberate or act, and exploring how these processes relate to issues of self-control and psychiatric disorders involving compulsion. His research aims to elucidate the neural mechanisms underlying decision-making and self-regulation, contributing to a deeper understanding of cognitive functions and mental health.

Research topics

• Computer Science
• Artificial Intelligence
• Biology
• Machine Learning
• Psychology
• Neuroscience
• Econometrics
• Cognitive science
• Cognitive psychology
• Economics

Selected publications

• Experience replay is associated with efficient nonlocal learning

  Science · 2021 · 201 citations

  To make effective decisions, people need to consider the relationship between actions and outcomes. These are often separated by time and space. The neural mechanisms by which disjoint actions and outcomes are linked remain unknown. One promising hypothesis involves neural replay of nonlocal experience. Using a task that segregates direct from indirect value learning, combined with magnetoencephalography, we examined the role of neural replay in human nonlocal learning. After receipt of a reward…

  DOI

• A model for learning based on the joint estimation of stochasticity and volatility

  Nature Communications · 2021 · 162 citations

  Senior authorCorresponding

  Previous research has stressed the importance of uncertainty for controlling the speed of learning, and how such control depends on the learner inferring the noise properties of the environment, especially volatility: the speed of change. However, learning rates are jointly determined by the comparison between volatility and a second factor, moment-to-moment stochasticity. Yet much previous research has focused on simplified cases corresponding to estimation of either factor alone. Here, we intr…

  DOI

• Formalizing planning and information search in naturalistic decision-making

  Nature Neuroscience · 2021 · 119 citations

  Decisions made by mammals and birds are often temporally extended. They require planning and sampling of decision-relevant information. Our understanding of such decision-making remains in its infancy compared with simpler, forced-choice paradigms. However, recent advances in algorithms supporting planning and information search provide a lens through which we can explain neural and behavioral data in these tasks. We review these advances to obtain a clearer understanding for why planning and cu…

  PublisherOA PDFDOI

• Discovering Symbolic Cognitive Models from Human and Animal Behavior

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-06 · 12 citations

  preprintOpen access

  Symbolic models play a key role in cognitive science, expressing computationally precise hypotheses about how the brain implements a cognitive process. Identifying an appropriate model typically requires a great deal of effort and ingenuity on the part of a human scientist. Here, we adapt FunSearch Romera-Paredes et al. (2024), a recently developed tool that uses Large Language Models (LLMs) in an evolutionary algorithm, to automatically discover symbolic cognitive models that accurately capture…

  PublisherDOI

• Reconciling flexibility and efficiency: medial entorhinal cortex represents a compositional cognitive map

  Nature Communications · 2025-08-12 · 7 citations

  articleOpen accessSenior author

  The influential concept of cognitive maps envisions that the brain builds mental representations of objects, barriers, and goals. Computational models show how these representations guide goal-directed behavior, such as planning novel routes to maximize rewards. One key feature of flexible cognitive representations is compositionality, the ability to build complex structures by recombining simpler parts. However, how this applies to neural representations of cognitive maps and map-based planning…

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01MH087882

  NIH · $1.8M · 2015

• CRCNS: Computational and neural mechanisms of memory-guided decisions

  NIH · $1.7M · 2014–2020

• CRCNS Research Proposal: Collaborative Research: Prioritization of memory reactivation for decision-making

  NSF · $391k · 2018–2022

Frequent coauthors

• Peter Dayan

  Max Planck Institute for Biological Cybernetics

  45 shared

• Daphna Shohamy

  Columbia University

  31 shared

• David S. Touretzky

  Carnegie Mellon University

  30 shared

• Yael Niv

  Princeton University

  29 shared

• Stephen M. Fleming

  University College London

  28 shared

• Ben Seymour

  University of Oxford

  26 shared

• Raymond J. Dolan

  National Hospital for Neurology and Neurosurgery

  22 shared

• Marcelo G. Mattar

  New York University

  22 shared

Similar researchers at Princeton University

• Sanjeev Arorah-84
• Jia Dengh-61
• Carlos Brodyh-59