N. Daw

发表

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

P. Dayan, J. Gläscher, N. Daw, 2010, Neuron.

A computational substrate for incentive salience

Samuel M. McClure, N. Daw, P. Montague, 2003, Trends in Neurosciences.

Bayesian theories of conditioning in a changing world

Aaron C. Courville, N. Daw, D. Touretzky, 2006, Trends in Cognitive Sciences.

Tonic dopamine: opportunity costs and the control of response vigor

P. Dayan, N. Daw, Y. Niv, 2007, Psychopharmacology.

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

P. Dayan, N. Daw, Y. Niv, 2005, Nature Neuroscience.

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

N. Daw, S. Gershman, 2017, Annual review of psychology.

Model-based influences on humans’ choices and striatal prediction errors

P. Dayan, N. Daw, R. Dolan, 2011, Neuron.

The Curse of Planning: Dissecting Multiple Reinforcement-Learning Systems by Taxing the Central Executive

A. Markman, N. Daw, A. R. Otto, 2013 .

Decision theory, reinforcement learning, and the brain

P. Dayan, N. Daw, 2008, Cognitive, affective & behavioral neuroscience.

The successor representation in human reinforcement learning

M. Botvinick, N. Daw, S. Gershman, 2016, Nature Human Behaviour.

Integrating memories to guide decisions

N. Daw, D. Shohamy, 2015, Current Opinion in Behavioral Sciences.

Reinforcement learning models of the dopamine system and their behavioral implications

N. Daw, D. Touretzky, 2003 .

Opponent interactions between serotonin and dopamine

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

Reinforcement Learning Signals in the Human Striatum Distinguish Learners from Nonlearners during Reward-Based Decision Making

N. Daw, J. O'Doherty, D. Joel, 2007, The Journal of Neuroscience.

Serotonin and Dopamine: Unifying Affective, Activational, and Decision Functions

N. Daw, Kae Nakamura, R. Cools, 2011, Neuropsychopharmacology.

Serotonin Selectively Modulates Reward Value in Human Decision-Making

P. Dayan, N. Daw, R. Dolan, 2012, The Journal of Neuroscience.

The computational neurobiology of learning and reward

K. Doya, N. Daw, 2006, Current Opinion in Neurobiology.

Model Uncertainty in Classical Conditioning

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

Choice values

P. Dayan, N. Daw, Y. Niv, 2006, Nature Neuroscience.

CONNECTIONS BETWEEN C N OMPUTATIONAL AND NEUROBIOLOGICAL PERSPECTIVES ON DECISION MAKING Decision theory, reinforcement learning, and the brain

P. Dayan, N. Daw, 2008 .

The algorithmic anatomy of model-based evaluation

P. Dayan, N. Daw, 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

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 .

Deciding How To Decide: Self-Control and Meta-Decision Making

N. Daw, Y-Lan Boureau, Peter Sokol-Hessner, 2015, Trends in Cognitive Sciences.

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

N. Daw, M. Kosinski, E. Phelps, 2016, eLife.

Learning the opportunity cost of time in a patch-foraging task

N. Daw, S. Constantino, 2015, Cognitive, Affective, & Behavioral Neuroscience.

Disorders of compulsivity: a common bias towards learning habits

P. Dayan, N. Daw, T. Robbins, 2014, Molecular Psychiatry.

Rethinking Extinction

N. Daw, Y. Niv, E. Phelps, 2015, Neuron.

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..

Surprise! Neural correlates of Pearce–Hall and Rescorla–Wagner coexist within the brain

Guillem R. Esber, N. Daw, M. Roesch, 2012, The European journal of neuroscience.

Meta-Learning of Structured Task Distributions in Humans and Machines

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

Actions , Policies , Values , and the Basal Ganglia

P. Dayan, N. Daw, Y. Niv, 2005 .

Representation and Timing in Theories of the Dopamine System

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

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

Neural Correlates of Forward Planning in a Spatial Decision Task in Humans

Dylan A. Simon, N. Daw, D. Simon, 2011, The Journal of Neuroscience.

Extraversion differentiates between model-based and model-free strategies in a reinforcement learning task

N. Daw, A. Skatova, P. Chan, 2013, Front. Hum. Neurosci..

Working-memory capacity protects model-based learning from stress

Alice Y. Chiang, N. Daw, A. R. Otto, 2013, Proceedings of the National Academy of Sciences.

Valence-dependent influence of serotonin depletion on model-based choice strategy

N. Daw, T. Robbins, Y. Worbe, 2015, Molecular Psychiatry.

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

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

Motivational effects on behavior: Towards a reinforcement learning model of rates of responding

P. Dayan, N. Daw, Y. Niv, 2005 .

Striatal Activity Underlies Novelty-Based Choice in Humans

N. Daw, R. Dolan, B. Seymour, 2008, Neuron.

Dissociating hippocampal and striatal contributions to sequential prediction learning

N. Daw, A. Bornstein, 2012, The European journal of neuroscience.

Differential Encoding of Losses and Gains in the Human Striatum

P. Dayan, N. Daw, R. Dolan, 2007, The Journal of Neuroscience.

Model-based choices involve prospective neural activity

Dylan A. Simon, N. Daw, D. Shohamy, 2015, Nature Neuroscience.

Signals in Human Striatum Are Appropriate for Policy Update Rather than Value Prediction

N. Daw, Jian Li, 2011, The Journal of Neuroscience.

Multiple Systems for Value Learning

N. Daw, J. O'Doherty, J. O’Doherty, 2014 .

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

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

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

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

Human Reinforcement Learning Subdivides Structured Action Spaces by Learning Effector-Specific Values

N. Daw, S. Gershman, Bijan Pesaran, 2009, The Journal of Neuroscience.

The ubiquity of model-based reinforcement learning

N. Daw, B. B. Doll, D. Simon, 2012, Current Opinion in Neurobiology.

Operant behavior suggests attentional gating of dopamine system inputs

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

The pigeon as particle filter

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

Reduced model-based decision-making in schizophrenia.

M. Botvinick, N. Daw, D. Barch, 2016, Journal of abnormal psychology.

Dopamine selectively remediates 'model-based' reward learning: a computational approach.

N. Daw, D. Shohamy, M. Sharp, 2016, Brain : a journal of neurology.

Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning

N. Daw, M. Frank, B. B. Doll, 2016, The Journal of Neuroscience.

Taking Psychiatry Research Online

N. Daw, C. Gillan, 2016, Neuron.

From Creatures of Habit to Goal-Directed Learners

Catherine A. Hartley, N. Daw, A. R. Otto, 2016, Psychological science.

Model-based learning protects against forming habits

N. Daw, A. R. Otto, E. Phelps, 2015, Cognitive, Affective, & Behavioral Neuroscience.

A Dual Role for Prediction Error in Associative Learning

Karl J. Friston, N. Daw, A. McIntosh, 2008, Cerebral cortex.

Dynamic Estimation of Task-Relevant Variance in Movement under Risk

M. Landy, N. Daw, Julia Trommershäuser, 2012, The Journal of Neuroscience.

Fronto-striatal organization: Defining functional and microstructural substrates of behavioural flexibility

N. Daw, T. Robbins, E. Bullmore, 2016, Cortex.

Motivation and value influences in the relative balance of goal-directed and habitual behaviours in obsessive-compulsive disorder

N. Daw, T. Robbins, Y. Worbe, 2015, Translational Psychiatry.

Reward and choice encoding in terminals of midbrain dopamine neurons depends on striatal target

Ilana B. Witten, Thomas J. Davidson, Nathan F. Parker, 2016, Nature Neuroscience.

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..

Hippocampal Contributions to Model-Based Planning and Spatial Memory

N. Daw, N. Burgess, D. Shohamy, 2019, Neuron.

Generalization of value in reinforcement learning by humans

N. Daw, D. Shohamy, G. E. Wimmer, 2012, The European journal of neuroscience.

Model-based reinforcement learning as cognitive search : Neurocomputational theories

N. Daw, 2012 .

Timing and Partial Observability in the Dopamine System

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

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

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

Action selection in multi-effector decision making

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

The Temporal Dynamics of Opportunity Costs: A Normative Account of Cognitive Fatigue and Boredom

Jonathan D. Cohen, N. Daw, Mayank Agrawal, 2020, bioRxiv.

Multiple Forms of Value Learning and the Function of Dopamine

B. Balleine, N. Daw, J. O'Doherty, 2009 .

Are we of two minds?

N. Daw, 2018, Nature Neuroscience.

Multiple memory systems as substrates for multiple decision systems

N. Daw, D. Shohamy, B. B. Doll, 2015, Neurobiology of Learning and Memory.

Dual-System Learning Models and Drugs of Abuse

Dylan A. Simon, N. Daw, D. Simon, 2012 .

Cortical substrates for exploratory decisions in humans

P. Dayan, N. Daw, J. O'Doherty, 2006, Nature.

Depression: a decision-theoretic analysis.

P. Dayan, N. Daw, Q. Huys, 2015, Annual review of neuroscience.

Representation of aversive prediction errors in the human periaqueductal gray

Daphna Shohamy, Tor D Wager, G Elliott Wimmer, 2014, Nature Neuroscience.

Independent Neural Computation of Value from Other People's Confidence

Arndis Simonsen, Nathaniel D Daw, Daniel Campbell-Meiklejohn, 2017, The Journal of Neuroscience.

Differential roles of human striatum and amygdala in associative learning

N. Daw, G. Schoenbaum, E. Phelps, 2011, Nature Neuroscience.

Dissociable Effects of Dopamine and Serotonin on Reversal Learning

B. Franke, N. Daw, R. Cools, 2013, Neuron.

Advanced Reinforcement Learning

N. Daw, 2014 .

Computational approaches to fMRI analysis

Kai Li, Theodore L. Willke, Uri Hasson, 2017, Nature Neuroscience.

Reminders of past choices bias decisions for reward in humans

Mel W. Khaw, N. Daw, D. Shohamy, 2017, Nature Communications.

What’s past is present: Reminders of past choices bias decisions for reward in humans

N. Daw, D. Shohamy, A. Bornstein, 2017, bioRxiv.

Specialized and spatially organized coding of sensory, motor, and cognitive variables in midbrain dopamine neurons

Ilana B. Witten, D. Tank, N. Daw, 2018 .

Perception, Action, and Utility: The Tangled Skein

N. Daw, S. Gershman, 2012 .

Dopamine: at the intersection of reward and action

N. Daw, 2007, Nature Neuroscience.

Reward prediction error does not explain movement selectivity in DMS-projecting dopamine neurons

Ilana B. Witten, Nathan F. Parker, N. Daw, 2018, bioRxiv.

Reward prediction error does not explain movement selectivity in DMS-projecting dopamine neurons

N. Daw, I. Witten, M. Mattar, 2018, bioRxiv.

Behavioral Neuroscience

Nathaniel Daw, N. Daw, X. Zhuang, 2022 .

Multiplicity of control in the basal ganglia: computational roles of striatal subregions

N. Daw, A. Bornstein, 2011, Current Opinion in Neurobiology.

Surviving threats: neural circuit and computational implications of a new taxonomy of defensive behaviour

Joseph E LeDoux, N. Daw, 2018, Nature Reviews Neuroscience.

Anterior Prefrontal Cortex Contributes to Action Selection through Tracking of Recent Reward Trends

Christopher K. Kovach, N. Daw, J. O'Doherty, 2012, The Journal of Neuroscience.

NeuroResource Hippocampal Contribution s to Model-Based Planning and Spatial Memory

Michael R. Meager, N. Daw, N. Burgess, 2019 .

Two Sides of the Same Coin: The Hippocampus as a Common Neural Substrate for Model-Based Planning and Spatial Memory

Michael R. Meager, N. Daw, N. Burgess, 2018, bioRxiv.

Of goals and habits

N. Daw, 2015, Proceedings of the National Academy of Sciences.

More Than the Sum of Its Parts: A Role for the Hippocampus in Configural Reinforcement Learning

N. Daw, D. Shohamy, B. B. Doll, 2018, Neuron.

Episodic Memory Encoding Interferes with Reward Learning and Decreases Striatal Prediction Errors

Erin Kendall Braun, N. Daw, D. Shohamy, 2014, The Journal of Neuroscience.

Chapter 15 – Value Learning through Reinforcement: The Basics of Dopamine and Reinforcement Learning

N. Daw, P. Tobler, 2013 .

Reinforcement learning and higher level cognition: Introduction to special issue

N. Daw, M. Frank, 2009, Cognition.

The cognitive neuroscience of motivation and learning

N. Daw, D. Shohamy, Daphna Shohamy, 2008 .

Anxiety, avoidance, and sequential evaluation

N. Daw, I. Momennejad, S. Zorowitz, 2020, Computational Psychiatry.

Reduced model-based decision-making in gambling disorder

N. Daw, A. R. Otto, A. Bechara, 2019, Scientific Reports.

Anxiety, avoidance, and sequential evaluation

N. Daw, I. Momennejad, S. Zorowitz, 2019, bioRxiv.

Excessive deliberation in social anxiety

N. Daw, Ming Hsu, C. Gillan, 2019, bioRxiv.

The successor representation in human reinforcement learning

M. Botvinick, N. Daw, S. Gershman, 2016, bioRxiv.

Journal of Abnormal Psychology Reduced Model-Based Decision-Making in Schizophrenia

M. Botvinick, N. Daw, D. Barch, 2016 .

Dissociable Effects of Dopamine and Serotonin on Reversal Learning

B. Franke, N. Daw, R. Cools, 2013, Neuron.

A Perceptual Inference Mechanism for Hallucinations Linked to Striatal Dopamine

N. Daw, P. Balsam, A. Abi-Dargham, 2018, Current Biology.

The Irrationality of Categorical Perception

L. Maloney, N. Daw, S. Fleming, 2013, The Journal of Neuroscience.

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

Ilana B. Witten, D. Tank, N. Daw, 2019, Nature.

Reward-learning and the novelty-seeking personality: a between- and within-subjects study of the effects of dopamine agonists on young Parkinson's patients.

M. Gluck, N. Daw, C. Myers, 2009, Brain : a journal of neurology.

A Neural Mechanism for the Opportunity Cost of Time

N. Daw, S. Constantino, S. Varanese, 2017, bioRxiv.

Neural mediators of changes of mind about perceptual decisions

Stephen M Fleming, Nathaniel D Daw, N. Daw, 2018, Nature Neuroscience.

The opportunity cost of time modulates cognitive effort

N. Daw, A. R. Otto, Ross A. Otto, 2017, Neuropsychologia.

The opportunity cost of time modulates cognitive effort

N. Daw, A. R. Otto, 2017, bioRxiv.

A retrieved context model of the emotional modulation of memory.

Deborah Talmi, Nathaniel D Daw, Lynn J Lohnas, 2019, Psychological review.

Sympathetic involvement in time-constrained sequential foraging

Scott T. Grafton, N. Daw, M. Cieslak, 2020, Cognitive, affective & behavioral neuroscience.

Chronic and Acute Stress Promote Overexploitation in Serial Decision Making

N. Daw, E. Phelps, S. Constantino, 2017, 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.

Instructed knowledge shapes feedback-driven aversive learning in striatum and orbitofrontal cortex, but not the amygdala

N. Daw, E. Phelps, Jian Li, 2016, eLife.

Motivational Context Modulates Prediction Error Response in Schizophrenia.

Jared X. Van Snellenberg, Jenna M. Reinen, N. Daw, 2016, Schizophrenia bulletin.

Uncertainty-based competition between prefrontal and striatal systems for behavioural control

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

SerotoninSelectivelyModulatesRewardValueinHuman Decision-Making

N. Daw, 2012 .

T216. Deficient Belief Updating Explains Abnormal Information Seeking Associated With Delusions in Schizophrenia

N. Daw, Guillermo Horga, A. Konova, 2018, Biological Psychiatry.

A retrieved context model of the emotional modulation of memory

N. Daw, D. Talmi, L. Lohnas, 2018, bioRxiv.

Jonathan D. Cohen, Robert C. Wilson, Gary A. Kane, 2019, eLife.

Jonathan D. Cohen, Robert C. Wilson, Gary A. Kane, 2018, bioRxiv.

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

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

Memory mechanisms predict sampling biases in sequential decision tasks

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

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

N. Daw, S. Jaffe-Dax, L. Emberson, 2020, Journal of Cognitive Neuroscience.

Suboptimal Criterion Learning in Static and Dynamic Environments

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

Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines

Robert D. Hawkins, T. Griffiths, N. Daw, 2022, 2205.11558.

Dissociable Effects of Dopamine and Serotonin on Reversal Learning

B. Franke, N. Daw, R. Cools, 2013, Neuron.

Uncertainty alters the balance between incremental learning and episodic memory

N. Daw, D. Shohamy, J. Nicholas, 2022, bioRxiv.

Model based control can give rise to devaluation insensitive choice

N. Daw, Neil Garrett, S. Allan, 2022, bioRxiv.

Fast rule switching and slow rule updating in a perceptual categorization task

N. Daw, T. J. Buschman, S. Tafazoli, 2022, bioRxiv.

Erratum: Representation and timing in theories of the dopamine system (Neural Computation (July 2006) 7, (1637))

Aaron C. Courville, Nathaniel D. Daw, David S. Tourtezky, 2006 .

Using Natural Language to Guide Meta-Learning Agents towards Human-like Inductive Biases

Robert D. Hawkins, T. Griffiths, N. Daw, 2022 .

Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning

Jonathan D. Cohen, T. Griffiths, N. Daw, 2022, ArXiv.

The temporal dynamics of opportunity costs: A normative account of cognitive fatigue and boredom.

Jonathan D. Cohen, N. Daw, Mayank Agrawal, 2021, Psychological review.

Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines

Robert D. Hawkins, T. Griffiths, N. Daw, 2022, ArXiv.

Humans decompose tasks by trading off utility and computational cost

Mark K. Ho, T. Griffiths, N. Daw, 2022, PLoS computational biology.

Effort Foraging Task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans and relationship to self-reported motivation and affect

Jonathan D. Cohen, N. Daw, A. Shenhav, 2022, bioRxiv.

sophisticated role in predicting rewards than previously thought.

N. Daw, B. B. Doll, 2016 .

Learning the opportunity cost of time in a patch-foraging task

N. Daw, S. Constantino, 2015, Cognitive, Affective, & Behavioral Neuroscience.

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

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

A vector reward prediction error model explains dopaminergic heterogeneity

Ilana B. Witten, N. Daw, B. Engelhard, 2022, bioRxiv.

Trial-by-trial data analysis using computational models

Nathaniel D. Daw, N. Daw, 2011 .

Coherent neuronal ensembles are rapidly recruited when making a look-reach decision

Bijan Pesaran, Yan T Wong, Yevgeny Novikov, 2015, Nature Neuroscience.

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.

A distinct inferential mechanism for delusions in schizophrenia.

N. Daw, Guillermo Horga, A. Konova, 2019, Brain : a journal of neurology.

Slow escape decisions are swayed by trait anxiety

D. Hassabis, N. Daw, D. Mobbs, 2019, Nature Human Behaviour.

The successor representation in human reinforcement learning

M. Botvinick, N. Daw, S. Gershman, 2016, bioRxiv.

A passive computational marker for individual differences in non-reinforced learning

N. Daw, T. Schonberg, Tom Salomon, 2022, bioRxiv.

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

Ilana B. Witten, D. Tank, N. Daw, 2019, Nature.

Formalizing planning and information search in naturalistic decision-making

N. Daw, A. Redish, E. Procyk, 2021, Nature Neuroscience.

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

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

Cognitive Control Predicts Use of Model-based Reinforcement Learning

N. Daw, A. R. Otto, A. Skatova, 2014, Journal of Cognitive Neuroscience.

Changes in brain and behavior during food-based decision-making following treatment of anorexia nervosa

N. Daw, D. Shohamy, K. Foerde, 2021, Journal of eating disorders.

Restrictive eating across a spectrum from healthy to unhealthy: behavioral and neural mechanisms

N. Daw, D. Shohamy, K. Foerde, 2020, Psychological Medicine.

Experience replay is associated with efficient nonlocal learning

N. Daw, R. Dolan, M. Mattar, 2021, Science.

Offline replay supports planning in human reinforcement learning

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

Prioritized memory access explains planning and hippocampal replay

Marcelo G Mattar, Nathaniel D Daw, N. Daw, 2017, Nature Neuroscience.

Linear reinforcement learning in planning, grid fields, and cognitive control

N. Daw, Payam Piray, 2021, Nature Communications.

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.

The rat as particle filter

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

The importance of mixed selectivity in complex cognitive tasks

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

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

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

Hippocampal Contributions to Model-Based Planning and Spatial Memory

Michael R. Meager, N. Daw, N. Burgess, 2019, Neuron.

In for a penny, in for a pound: examining motivated memory through the lens of retrieved context models

N. Daw, D. Talmi, Deimante Kavaliauskaite, 2021, Learning & memory.

Visuomotor Task Learning and Performance During a Sequential Activity in the Supplementary Motor Area Related to

Dylan A. Simon, Opher Donchin, Nathaniel D. Daw, 2015 .

Context-sensitive valuation and learning

Lindsay E. Hunter, N. Daw, 2021, Current Opinion in Behavioral Sciences.

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.

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

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

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

N. Daw, Payam Piray, 2020 .

Dual credit assignment processes underlie dopamine signals in a complex spatial environment

N. Daw, L. Frank, J. Berke, 2023, Neuron.

Comparison of the Association Between Goal-Directed Planning and Self-reported Compulsivity vs Obsessive-Compulsive Disorder Diagnosis

Christina L. Boisseau, N. Daw, E. Phelps, 2019, JAMA psychiatry.

Interplay between rule learning and rule switching in a perceptual categorization task

N. Daw, T. J. Buschman, S. Tafazoli, 2022 .

Motivation and learning.

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

How cognitive and reactive fear circuits optimize escape decisions in humans

Song Qi, Jiayin Sun, Fangjian Guo, 2017, Proceedings of the National Academy of Sciences of the United States of America.

Increased and biased deliberation in social anxiety

Lindsay E. Hunter, N. Daw, Ming Hsu, 2021, Nature human behaviour.

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

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

Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning

T. Griffiths, N. Daw, Sreejan Kumar, 2022, PLoS Comput. Biol..

A particle filtering account of selective attention during learning

Yael Niv, Angela Radulescu, Nathaniel D. Daw, 2019, 2019 Conference on Cognitive Computational Neuroscience.

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

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

Beyond the Average View of Dopamine

N. Daw, Angela J. Langdon, 2020, Trends in Cognitive Sciences.

Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines

Robert D. Hawkins, Carlos G. Correa, T. Griffiths, 2022, NeurIPS.

Value Learning through Reinforcement

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

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

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

Sympathetic and parasympathetic involvement in time constrained sequential foraging

Scott T. Grafton, Matthew Cieslak, Neil Garrett, 2019, bioRxiv.

Biased belief updating and suboptimal choice in foraging decisions

Neil Garrett, Nathaniel D. Daw, N. Daw, 2019, bioRxiv.

Slow escape decisions are swayed by trait anxiety

D. Hassabis, N. Daw, D. Mobbs, 2019, Nature Human Behaviour.

Low lifetime stress exposure is associated with reduced stimulus-response memory.

N. Daw, E. Phelps, G. Shields, 2017, Learning & memory.

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

Robert C. Wilson, Gary A. Kane, N. Daw, 2017, Cognitive, affective & behavioral neuroscience.

Rat Anterior Cingulate Cortex Continuously Signals Decision Variables in a Patch Foraging Task

Gary A. Kane, N. Daw, G. Aston-Jones, 2021, The Journal of Neuroscience.

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

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

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

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

A simple model for learning in volatile environments

Payam Piray, Nathaniel D Daw, N. Daw, 2020, PLoS Comput. Biol..

Boredom, Information-Seeking and Exploration

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

Neurocomputational mechanisms of confidence in self and others

N. Daw, Dan Bang, S. Fleming, 2022, Nature Communications.

Predictive and Interpretable: Combining Artificial Neural Networks and Classic Cognitive Models to Understand Human Learning and Decision Making

Kevin J. Miller, Maria K. Eckstein, N. Daw, 2023, bioRxiv.

Experience replay supports non-local learning

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

An item response theory analysis of the matrix reasoning item bank (MaRs-IB).

N. Daw, S. Blakemore, G. Chierchia, 2023, Behavior research methods.

Title : 1 A passive computational marker for individual differences in non-reinforced learning 2 3 Running Title

N. Daw, T. Schonberg, Tom Salomon, 2022 .

Unpredictability vs. volatility and the control of learning

N. Daw, Payam Piray, 2020 .

A transparent model for learning in volatile environments

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

Are we of two minds?

N. Daw, 2018, Nature Neuroscience.

Tonic Dopamine Modulates Exploitation of Reward Learning

N. Daw, X. Zhuang, J. Beeler, 2010, Front. Behav. Neurosci..

Signals in Human Striatum Are Appropriate for Policy Update Rather than Value Prediction

N. Daw, Jian Li, 2011, The Journal of Neuroscience.

Model-based learning protects against forming habits

N. Daw, A. R. Otto, E. Phelps, 2015, Cognitive, Affective, & Behavioral Neuroscience.

Episodic retrieval for model-based evaluation in sequential decision tasks

N. Daw, M. Mattar, D. Talmi, 2023 .

Rational Arbitration of Hippocampal Replay

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

NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript NIH Public Access Author Manuscript Neuron. Author manuscript.

P. Dayan, N. Daw, R. Dolan, 2011 .

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.

Neural Correlates of Forward Planning in a Spatial Decision Task in Humans Nih Public Access

Dylan A. Simon, N. Daw, D. Simon, 2022 .

Effort Foraging Task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans

N. Daw, A. Shenhav, L. Bustamante, 2023, bioRxiv.

Magnetic voluntary head-fixation in transgenic rats enables lifetime imaging of hippocampal neurons

N. Daw, B. B. Scott, C. Brody, 2023, bioRxiv.

Neural evidence for the successor representation in choice evaluation

M. Botvinick, N. Daw, S. Gershman, 2021, bioRxiv.

A feature-specific prediction error model explains dopaminergic heterogeneity

Ilana B. Witten, N. Daw, Yotam Sagiv, 2024, bioRxiv.

Offline Replay Supports Planning: fMRI Evidence from Reward Revaluation

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

The ubiquity of model-based reinforcement learning

Dylan A. Simon, N. Daw, B. B. Doll, 2012, Current Opinion in Neurobiology.

Behavioral/systems/cognitive Reinforcement Learning Signals in the Human Striatum Distinguish Learners from Nonlearners during Reward- Based Decision Making

N. Daw, D. Joel, Tom Schönberg, 2022 .

Model-based influences on humans’ choices and striatal prediction errors

N. Daw, Samuel J. Gershman, Ben Seymour, 2011, Neuron.

In for a pound, in for a penny: How the opportunity to gain reward influences the competition for memory resources

N. Daw, D. Talmi, Deimante Kavaliauskaite, 2018, bioRxiv.

Low lifetime stress exposure is associated with reduced stimulus-response memory.

N. Daw, E. Phelps, G. Shields, 2017, Learning & memory.

Dissociable effects of dopamine and serotonin on reversal learning [Correction]

B. Franke, N. Daw, R. Cools, 2013 .

Anterior Prefrontal Cortex Contributes to Action Selection through Tracking of Recent Reward Trends

Christopher K. Kovach, N. Daw, R. Adolphs, 2012, The Journal of Neuroscience.

A closer look at choice

N. Daw, S. Constantino, 2010, Nature Neuroscience.

Polar exploration : An anterior prefrontal substrate for exploratory decisions in humans

P. Dayan, N. Daw, R. Dolan, 2005 .

Multiple Forms of Value Learning and the Function of Dopamine

B. Balleine, N. Daw, J. O’Doherty, 2009 .

Learning generalizable visuomotor mappings for de novo skills

N. Daw, Jordan A. Taylor, Carlos A. Velázquez-Vargas, 2023, bioRxiv.

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

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