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The Journal of Neuroscience : the Official Journal of the Society for Neuroscience|January 29, 2016
Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement LearningBradley B Doll, Kevin G Bath, Nathaniel D Daw, et al.Plos Computational Biology|June 19, 2019
Hierarchical Bayesian inference for concurrent model fitting and comparison for group studiesPayam Piray, Amir Dezfouli, Tom Heskes, et al.Nature Communications|August 17, 2021
Linear reinforcement learning in planning, grid fields, and cognitive controlPayam Piray, Nathaniel D DawPlos Computational Biology|July 2, 2020
A simple model for learning in volatile environmentsPayam Piray, Nathaniel D DawCurrent Opinion in Neurobiology|March 28, 2006
The computational neurobiology of learning and rewardNathaniel D Daw, Kenji DoyaNature Communications|October 21, 2024
Computational processes of simultaneous learning of stochasticity and volatility in humansPayam Piray, Nathaniel D DawNature Communications|August 12, 2025
Reconciling flexibility and efficiency: medial entorhinal cortex represents a compositional cognitive mapPayam Piray, Nathaniel D DawNature Communications|November 16, 2021
A model for learning based on the joint estimation of stochasticity and volatilityPayam Piray, Nathaniel D DawThe Journal of Neuroscience : the Official Journal of the Society for Neuroscience|April 8, 2011
Signals in human striatum are appropriate for policy update rather than value predictionJian Li, Nathaniel D DawPhilosophical Transactions of the Royal Society of London. Series B, Biological Sciences|October 1, 2014
The algorithmic anatomy of model-based evaluationNathaniel D Daw, Peter DayanPageof 35