Nathaniel D. Daw
Nathaniel D. Daw
Huo Professor of Computational and Theoretical Neuroscience, Princeton University
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Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control
ND Daw, Y Niv, P Dayan
Nature neuroscience 8 (12), 1704-1711, 2005
Cortical substrates for exploratory decisions in humans
ND Daw, JP O'doherty, P Dayan, B Seymour, RJ Dolan
Nature 441 (7095), 876-879, 2006
Model-based influences on humans' choices and striatal prediction errors
ND Daw, SJ Gershman, B Seymour, P Dayan, RJ Dolan
Neuron 69 (6), 1204-1215, 2011
The importance of mixed selectivity in complex cognitive tasks
M Rigotti, O Barak, MR Warden, XJ Wang, ND Daw, EK Miller, S Fusi
Nature 497 (7451), 585-590, 2013
States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning
J Gläscher, N Daw, P Dayan, JP O'Doherty
Neuron 66 (4), 585-595, 2010
Tonic dopamine: opportunity costs and the control of response vigor
Y Niv, ND Daw, D Joel, P Dayan
Psychopharmacology 191 (3), 507-520, 2007
Opponent interactions between serotonin and dopamine
ND Daw, S Kakade, P Dayan
Neural networks 15 (4-6), 603-616, 2002
Decision theory, reinforcement learning, and the brain
P Dayan, ND Daw
Cognitive, Affective, & Behavioral Neuroscience 8 (4), 429-453, 2008
The computational neurobiology of learning and reward
ND Daw, K Doya
Current opinion in neurobiology 16 (2), 199-204, 2006
A computational substrate for incentive salience
SM McClure, ND Daw, PR Montague
Trends in neurosciences 26 (8), 423-428, 2003
Disorders of compulsivity: a common bias towards learning habits
V Voon, K Derbyshire, C Rück, MA Irvine, Y Worbe, J Enander, ...
Molecular psychiatry 20 (3), 345-352, 2015
Differential encoding of losses and gains in the human striatum
B Seymour, N Daw, P Dayan, T Singer, R Dolan
Journal of Neuroscience 27 (18), 4826-4831, 2007
Bayesian theories of conditioning in a changing world
AC Courville, ND Daw, DS Touretzky
Trends in cognitive sciences 10 (7), 294-300, 2006
Trial-by-trial data analysis using computational models
ND Daw
Decision making, affect, and learning: Attention and performance XXIII 23 (1), 2011
Serotonin and dopamine: unifying affective, activational, and decision functions
R Cools, K Nakamura, ND Daw
Neuropsychopharmacology 36 (1), 98-113, 2011
Working-memory capacity protects model-based learning from stress
AR Otto, CM Raio, A Chiang, EA Phelps, ND Daw
Proceedings of the National Academy of Sciences 110 (52), 20941-20946, 2013
Reinforcement learning signals in the human striatum distinguish learners from nonlearners during reward-based decision making
T Schönberg, ND Daw, D Joel, JP O'Doherty
Journal of Neuroscience 27 (47), 12860-12867, 2007
The ubiquity of model-based reinforcement learning
BB Doll, DA Simon, ND Daw
Current opinion in neurobiology 22 (6), 1075-1081, 2012
Reward-learning and the novelty-seeking personality: a between-and within-subjects study of the effects of dopamine agonists on young Parkinson's patients
N Bódi, S Kéri, H Nagy, A Moustafa, CE Myers, N Daw, G Dibó, A Takats, ...
Brain 132 (9), 2385-2395, 2009
Self-evaluation of decision-making: A general Bayesian framework for metacognitive computation.
SM Fleming, ND Daw
Psychological review 124 (1), 91, 2017
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