2026 Nathaniel D. Daw: Neuroscience Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Neuroscience	95	893	799	479	420	224	40382

Discipline name

D-Index

World Ranking

Current World Ranking

National Ranking

Current National Ranking

Publications

Citations

Neuroscience

893

799

479

420

224

40382

Research.com Recognitions

2003 - Fellow of the American Association for the Advancement of Science (AAAS)

Overview

Nathaniel D. Daw is affiliated with Princeton University in the United States. Their research spans several interconnected fields, primarily focusing on neuroscience and psychology. The main fields of study include Neuroscience and Psychology, with prominent subfields such as Cognitive Neuroscience, Artificial Intelligence, Experimental and Cognitive Psychology, Cellular and Molecular Neuroscience, and Clinical Psychology.

The scientist's research interests cover a range of topics, including:

Neural dynamics and brain function
Memory and Neural Mechanisms
Neural and Behavioral Psychology Studies
Mental Health Research Topics
Neuroscience and Neuropharmacology Research
Eating Disorders and Behaviors
Functional Brain Connectivity Studies

Frequent co-authors in their publications include Jonathan D. Cohen (12 collaborations), Marcelo G. Mattar (11), Ari E. Kahn (9), Daphna Shohamy (9), and Thomas L. Griffiths (9). This diversity of collaborators reflects the interdisciplinary approach adopted in their research efforts.

Daw has contributed substantially to several publication venues, with repeated appearances in:

bioRxiv (Cold Spring Harbor Laboratory) - 32 publications
Nature Communications - 8 publications
arXiv (Cornell University) - 7 publications
PLoS Computational Biology - 5 publications
Biological Psychiatry - 4 publications

The following recent papers highlight their contributions, showing a range of topics and venues:

"Experience replay is associated with efficient nonlocal learning," 2021, Science
"A model for learning based on the joint estimation of stochasticity and volatility," 2021, Nature Communications
"The temporal dynamics of opportunity costs: A normative account of cognitive fatigue and boredom," 2021, Psychological Review
"Formalizing planning and information search in naturalistic decision-making," 2021, Nature Neuroscience
"A simple model for learning in volatile environments," 2020, PLoS Computational Biology

In recognition of their contributions to the scientific community, Nathaniel D. Daw was named a Fellow of the American Association for the Advancement of Science (AAAS) in 2003.

Best Publications

Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control

Nathaniel D Daw;Yael Niv;Yael Niv;Peter Dayan

3042
Citations
Cortical substrates for exploratory decisions in humans

Nathaniel D. Daw;John P. O'Doherty;Peter Dayan;Ben Seymour

2663
Citations
Model-based influences on humans' choices and striatal prediction errors.

Nathaniel D. Daw;Samuel J. Gershman;Ben Seymour;Peter Dayan

2100
Citations
The importance of mixed selectivity in complex cognitive tasks

Mattia Rigotti;Omri Barak;Omri Barak;Melissa R. Warden;Melissa R. Warden;Xiao Jing Wang;Xiao Jing Wang

1844
Citations
States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning

Jan Gläscher;Nathaniel Douglass Daw;Peter Dayan;John P. O'Doherty;John P. O'Doherty

1438
Citations
Tonic dopamine: opportunity costs and the control of response vigor.

Yael Niv;Yael Niv;Nathaniel D. Daw;Daphna Joel;Peter Dayan

1314
Citations
Opponent interactions between serotonin and dopamine

Nathaniel D. Daw;Sham Kakade;Peter Dayan

1017
Citations
Decision theory, reinforcement learning, and the brain

Peter Dayan;Nathaniel D. Daw

753
Citations
Disorders of compulsivity: a common bias towards learning habits

Valerie Voon;K Derbyshire;C Rück;MA Irvine

674
Citations
The computational neurobiology of learning and reward

Nathaniel Douglass Daw;Kenji Doya

673
Citations
Characterizing a psychiatric symptom dimension related to deficits in goal-directed control

Claire M Gillan;Claire M Gillan;Michal Kosinski;Robert Whelan;Elizabeth A Phelps;Elizabeth A Phelps;Elizabeth A Phelps

620
Citations
Self-evaluation of decision-making: A general Bayesian framework for metacognitive computation.

Stephen M. Fleming;Nathaniel D. Daw

613
Citations
Trial-by-trial data analysis using computational models

Nathaniel D. Daw

587
Citations
Bayesian theories of conditioning in a changing world

Aaron C. Courville;Nathaniel D. Daw;David S. Touretzky

573
Citations
Working-memory capacity protects model-based learning from stress

A. Ross Otto;Candace M. Raio;Alice Chiang;Elizabeth A. Phelps;Elizabeth A. Phelps;Elizabeth A. Phelps

558
Citations
Specialized coding of sensory, motor and cognitive variables in VTA dopamine neurons

Ben Engelhard;Joel Finkelstein;Julia Cox;Weston Fleming

555
Citations
Differential encoding of losses and gains in the human striatum

Ben Seymour;Nathaniel Daw;Peter Dayan;Tania Singer

552
Citations
Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework.

Samuel J Gershman;Nathaniel D Daw

551
Citations
A computational substrate for incentive salience

Samuel M. McClure;Nathaniel Douglass Daw;P. Read Montague

540
Citations
Serotonin and dopamine: unifying affective, activational, and decision functions.

Roshan Cools;Kae Nakamura;Nathaniel D. Daw

516
Citations