Nathaniel D. Daw

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Prioritized memory access explains planning and hippocampal replay

Marcelo G. Mattar, Nathaniel D. Daw, 2017 .

Behavioral considerations suggest an average reward TD model of the dopamine system

David S. Touretzky, Nathaniel D. Daw, N. Daw, 2000, Neurocomputing.

Pre-term infants exhibit impaired prediction and learning in Audio-Visual association paradigm

Sagi Jaffe-Dax, Nathaniel D. Daw, Lauren L. Emberson, 2017, CogSci.

Novel methods for measuring the cost of cognitive control in a patch foraging task and a demand selection task with Stroop

Jonathan D. Cohen, Nathaniel D. Daw, Laura Bustamante, 2018, CogSci.

Embedded compilation for multimedia applications

Seth Copen Goldstein, Nathaniel D. Daw, Dennis Strelow, 2000, Proceedings 2000 IEEE Symposium on Field-Programmable Custom Computing Machines (Cat. No.PR00871).

Suboptimal Criterion Learning in Static and Dynamic Environments

Elyse H. Norton, Michael S. Landy, Nathaniel D. Daw, 2017, PLoS Comput. Biol..

Selective impairment of prediction error signaling in human dorsolateral but not ventral striatum in Parkinson's disease patients: evidence from a model-based fMRI study

Nathaniel D. Daw, John P. O'Doherty, Tom Schonberg, 2010, NeuroImage.

Opponent interactions between serotonin and dopamine

Sham M. Kakade, Nathaniel D. Daw, Peter Dayan, 2002, Neural Networks.

Mechanisms of overharvesting in patch foraging

Jonathan D. Cohen, Nathaniel D. Daw, Robert Wilson, 2017, CogSci.

Boredom, Information-Seeking and Exploration

Jonathan D. Cohen, Nathaniel D. Daw, Robert Wilson, 2016, CogSci.

Action selection in multi-effector decision making

Bijan Pesaran, Nathaniel D. Daw, Seth Madlon-Kay, 2013, NeuroImage.

Author response: Offline replay supports planning in human reinforcement learning

Kenneth A. Norman, Nathaniel D. Daw, Ida Momennejad, 2018 .

Moving Beyond Where and What to How: Using Models and fMRI to Understand Brain-Behavior Relations

Bradley C. Love, John P. Spencer, Nathaniel D. Daw, 2011, CogSci.

A common model explaining flexible decision making, grid fields and cognitive control

Nathaniel D. Daw, Payam Piray, N. Daw, 2019 .

Semi-rational models of conditioning: The case of trial order

Peter Dayan, Aaron C. Courville, Nathaniel D. Daw, 2012 .

Local analysis of behaviour in the adjusting-delay task for assessing choice of delayed reinforcement

Nathaniel D. Daw, Trevor W. Robbins, Rudolf N. Cardinal, 2002, Neural Networks.

Long-Term Reward Prediction in TD Models of the Dopamine System

David S. Touretzky, Nathaniel D. Daw, N. Daw, 2002, Neural Computation.

David S. Touretzky, Aaron C. Courville, Nathaniel D. Daw, 2004, NIPS.

Model Uncertainty in Classical Conditioning

David S. Touretzky, Geoffrey J. Gordon, Aaron C. Courville, 2003, NIPS.

The importance of mixed selectivity in complex cognitive tasks

Xiao-Jing Wang, Earl K. Miller, Nathaniel D. Daw, 2013, Nature.

Cognitive Control Predicts Use of Model-based Reinforcement Learning

Nathaniel D. Daw, Anya Skatova, A. Ross Otto, 2014, Journal of Cognitive Neuroscience.

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

Nathaniel D. Daw, Payam Piray, N. Daw, 2020, Nature Communications.

How fast to work: Response vigor, motivation and tonic dopamine

Peter Dayan, Nathaniel D. Daw, Yael Niv, 2005, NIPS.

Predictive representations can link model-based reinforcement learning to model-free mechanisms

Samuel Gershman, Nathaniel D. Daw, Matthew Botvinick, 2017, bioRxiv.

Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies

Tom Heskes, Nathaniel D. Daw, Michael J. Frank, 2018, bioRxiv.

Cortical and Hippocampal Correlates of Deliberation during Model-Based Decisions for Rewards in Humans

Nathaniel D. Daw, Aaron M. Bornstein, N. Daw, 2013, PLoS Comput. Biol..

Timing and Partial Observability in the Dopamine System

David S. Touretzky, Aaron C. Courville, Nathaniel D. Daw, 2002, NIPS.

Cognitive Control Mode Predicts Behavioral Expression of Model-Based Reinforcement-Learning

Nathaniel D. Daw, Anya Skatova, A. Ross Otto, 2014, CogSci.

Representation and Timing in Theories of the Dopamine System

David S. Touretzky, Aaron C. Courville, Nathaniel D. Daw, 2006, Neural Computation.

Operant behavior suggests attentional gating of dopamine system inputs

David S. Touretzky, Nathaniel D. Daw, N. Daw, 2001, Neurocomputing.

Offline Replay Supports Planning: fMRI Evidence from Reward Revaluation

Kenneth A. Norman, Nathaniel D. Daw, Ida Momennejad, 2017, bioRxiv.

Meta-Learning of Compositional Task Distributions in Humans and Machines pdf

Thomas L. Griffiths, Jonathan D. Cohen, Nathaniel D. Daw, 2020, ArXiv.

Testing Whether Humans Have an Accurate Model of Their Own Motor Uncertainty in a Speeded Reaching Task

Hang Zhang, Nathaniel D. Daw, Laurence T. Maloney, 2011, PLoS Comput. Biol..

Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm

Peter Dayan, Nathaniel D. Daw, Ben Seymour, 2022 .

Neural Computations Supporting Cognition: Rumelhart Prize Symposium in Honor of Peter Dayan

Alexandre Pouget, Kenji Doya, Nathaniel D. Daw, 2012, CogSci.

Experience replay supports non-local learning

Marcelo G. Mattar, Nathaniel D. Daw, Raymond J Dolan, 2020, bioRxiv.

A Computational Role for Top–Down Modulation from Frontal Cortex in Infancy

Sagi Jaffe-Dax, Nathaniel D. Daw, Lauren L. Emberson, 2019, Journal of Cognitive Neuroscience.

Rational Arbitration of Hippocampal Replay

Jonathan D. Cohen, Mayank Agrawal, Marcelo G. Mattar, 2019, 2019 Conference on Cognitive Computational Neuroscience.

Meta-Learning of Structured Task Distributions in Humans and Machines

Thomas L. Griffiths, Jonathan D. Cohen, Nathaniel D. Daw, 2020, ICLR.

The rat as particle filter

Aaron C. Courville, Nathaniel D. Daw, N. Daw, 2007, NIPS.

Memory mechanisms predict sampling biases in sequential decision tasks

Deborah Talmi, Marcelo G. Mattar, Nathaniel D. Daw, 2018 .

Human Representation of Visuo-Motor Uncertainty as Mixtures of Orthogonal Basis Distributions

Nathaniel D. Daw, Laurence T. Maloney, Hang Zhang, 2015, Nature Neuroscience.

Environmental statistics and the trade-off between model-based and TD learning in humans

Nathaniel D. Daw, Dylan A. Simon, N. Daw, 2011, NIPS.

Self-Evaluation of Decision-Making: A General Bayesian Framework for Metacognitive Computation

Nathaniel D. Daw, Stephen M. Fleming, N. Daw, 2017, Psychological review.

Trial-by-trial data analysis using computational models

Nathaniel D. Daw, N. Daw, 2011 .

Grid Cells, Place Cells, and Geodesic Generalization for Spatial Reinforcement Learning

Nathaniel D. Daw, Nicholas J. Gustafson, N. Daw, 2011, PLoS Comput. Biol..

Linear reinforcement learning: Flexible reuse of computation in planning, grid fields, and cognitive control

Nathaniel D. Daw, Payam Piray, 2020 .

Information-Seeking, Learning and the Marginal Value Theorem: A Normative Approach to Adaptive Exploration

Jonathan D. Cohen, Nathaniel D. Daw, Robert Wilson, 2016, CogSci.

Value Learning through Reinforcement

Nathaniel D. Daw, Philippe N. Tobler, N. Daw, 2014 .

Intolerance to uncertainty is associated with diminished exploration

Nathaniel D. Daw, Jennifer Lenow, Elizabeth Phelps, 2017, CogSci.

Meta-Learning of Structured Task Distributions in Humans and Machines

Thomas L. Griffiths, Nathaniel D. Daw, Ishita Dasgupta, 2021, ICLR.

Deficient Goal-Directed Control in a Population Characterized by Extreme Goal Pursuit

Daphna Shohamy, Nathaniel D. Daw, Karin Foerde, 2019, Journal of Cognitive Neuroscience.

Role of Human Ventromedial Prefrontal Cortex in Learning and Recall of Enhanced Extinction

Nathaniel D. Daw, Marijn C. W. Kroes, Joseph E. Dunsmoor, 2019, The Journal of Neuroscience.

Dopamine and inference about timing

David S. Touretzky, Aaron C. Courville, Nathaniel D. Daw, 2002, Proceedings 2nd International Conference on Development and Learning. ICDL 2002.

Increased locus coeruleus tonic activity causes disengagement from a patch-foraging task

Nathaniel D. Daw, Amitai Shenhav, Gary Aston-Jones, 2017, Cognitive, Affective, & Behavioral Neuroscience.

Testing whether humans have an accurate model of their own motor uncertainty in a speeded reaching task

Nathaniel D. Daw, Laurence T. Maloney, Hang Zhang, 2011 .

Motivation and learning.

Nathaniel D. Daw, N. Daw, 1969, Canadian Medical Association journal.