Embedded Compilation for Multimedia Applications. - Nathaniel D. Daw; Seth Copen Goldstein; Dennis Strelow (FCCM, 2000)

Behavioral considerations suggest an average reward TD model of the dopamine system. - Nathaniel D. Daw; David S. Touretzky (Neurocomputing, 2000)

Operant behavior suggests attentional gating of dopamine system inputs. - Nathaniel D. Daw; David S. Touretzky (Neurocomputing, 2001)

Timing and Partial Observability in the Dopamine System. - Nathaniel D. Daw; Aaron C. Courville; David S. Touretzky (NIPS, 2002)

Long-Term Reward Prediction in TD Models of the Dopamine System. - Nathaniel D. Daw; David S. Touretzky (Neural Comput., 2002)

Local analysis of behaviour in the adjusting-delay task for assessing choice of delayed reinforcement. - Rudolf N. Cardinal; Nathaniel D. Daw; Trevor W. Robbins; Barry J. Everitt (Neural Networks, 2002)

Opponent interactions between serotonin and dopamine. - Nathaniel D. Daw; Sham M. Kakade; Peter Dayan (Neural Networks, 2002)

Model Uncertainty in Classical Conditioning. - Aaron C. Courville; Nathaniel D. Daw; Geoffrey J. Gordon; David S. Touretzky (NIPS, 2003)

Similarity and Discrimination in Classical Conditioning: A Latent Variable Account. - Aaron C. Courville; Nathaniel D. Daw; David S. Touretzky (NIPS, 2004)

How fast to work: Response vigor, motivation and tonic dopamine. - Yael Niv; Nathaniel D. Daw; Peter Dayan (NIPS, 2005)

Representation and Timing in Theories of the Dopamine System. - Nathaniel D. Daw; Aaron C. Courville; David S. Touretzky (Neural Comput., 2006)

The misbehavior of value and the discipline of the will. - Peter Dayan; Yael Niv; Ben Seymour; Nathaniel D. Daw (Neural Networks, 2006)

The rat as particle filter. - Nathaniel D. Daw; Aaron C. Courville (NIPS, 2007)

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. - Tom Schonberg; John P. O'Doherty; Daphna Joel; Rivka Inzelberg; Yoram Segev; Nathaniel D. Daw (NeuroImage, 2010)

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; John P. O'Doherty (CogSci, 2011)

Environmental statistics and the trade-off between model-based and TD learning in humans. - Dylan A. Simon; Nathaniel D. Daw (NIPS, 2011)

Grid Cells, Place Cells, and Geodesic Generalization for Spatial Reinforcement Learning. - Nicholas J. Gustafson; Nathaniel D. Daw (PLoS Comput. Biol., 2011)

Neural Computations Supporting Cognition: Rumelhart Prize Symposium in Honor of Peter Dayan. - Kenji Doya; John P. O'Doherty; Alexandre Pouget; Peter Bossaerts; Nathaniel D. Daw; Yael Niv (CogSci, 2012)

The importance of mixed selectivity in complex cognitive tasks. - Mattia Rigotti; Omri Barak; Melissa R. Warden; Xiao-Jing Wang; Nathaniel D. Daw; Earl K. Miller; Stefano Fusi (Nat., 2013)

Action selection in multi-effector decision making. - Seth Madlon-Kay; Bijan Pesaran; Nathaniel D. Daw (NeuroImage, 2013)

Cortical and Hippocampal Correlates of Deliberation during Model-Based Decisions for Rewards in Humans. - Aaron M. Bornstein; Nathaniel D. Daw (PLoS Comput. Biol., 2013)

Testing Whether Humans Have an Accurate Model of Their Own Motor Uncertainty in a Speeded Reaching Task. - Hang Zhang 028; Nathaniel D. Daw; Laurence T. Maloney (PLoS Comput. Biol., 2013)

Cognitive Control Mode Predicts Behavioral Expression of Model-Based Reinforcement-Learning. - A. Ross Otto; Anya Skatova; Seth Madlon-Kay; Nathaniel D. Daw (CogSci, 2014)

Cognitive Control Predicts Use of Model-based Reinforcement Learning. - A. Ross Otto; Anya Skatova; Seth Madlon-Kay; Nathaniel D. Daw (J. Cogn. Neurosci., 2015)

Boredom, Information-Seeking and Exploration. - Andra Geana; Robert Wilson; Nathaniel D. Daw; Jonathan Cohen 003 (CogSci, 2016)

Information-Seeking, Learning and the Marginal Value Theorem: A Normative Approach to Adaptive Exploration. - Andra Geana; Robert Wilson; Nathaniel D. Daw; Jonathan Cohen 003 (CogSci, 2016)

Pre-term infants exhibit impaired prediction and learning in Audio-Visual association paradigm. - Sagi Jaffe-Dax; Alex M. Boldin; Nathaniel D. Daw; Lauren L. Emberson (CogSci, 2017)

Mechanisms of overharvesting in patch foraging. - Gary Kane; Aaron M. Bornstein; Robert Wilson; Amitai Shenhav; Nathaniel D. Daw; Jonathan Cohen 003 (CogSci, 2017)

Intolerance to uncertainty is associated with diminished exploration. - Jennifer Lenow; Nathaniel D. Daw; Elizabeth A. Phelps (CogSci, 2017)

Suboptimal Criterion Learning in Static and Dynamic Environments. - Elyse H. Norton; Stephen M. Fleming; Nathaniel D. Daw; Michael S. Landy (PLoS Comput. Biol., 2017)

Predictive representations can link model-based reinforcement learning to model-free mechanisms. - Evan M. Russek; Ida Momennejad; Matthew M. Botvinick; Samuel J. Gershman; Nathaniel D. Daw (PLoS Comput. Biol., 2017)

Novel methods for measuring the cost of cognitive control in a patch foraging task and a demand selection task with Stroop. - Laura Bustamante; Augustus Baker; Allison Burton; Amitai Shenhav; Chloe Hoeber; Nathaniel D. Daw; Jonathan Cohen 003 (CogSci, 2018)

Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies. - Payam Piray; Amir Dezfouli; Tom Heskes; Michael J. Frank; Nathaniel D. Daw (PLoS Comput. Biol., 2019)

Meta-Learning of Compositional Task Distributions in Humans and Machines. - Sreejan Kumar; Ishita Dasgupta 001; Jonathan D. Cohen 003; Nathaniel D. Daw; Thomas L. Griffiths 001 (CoRR, 2020)

A Computational Role for Top-Down Modulation from Frontal Cortex in Infancy. - Sagi Jaffe-Dax; Alex M. Boldin; Nathaniel D. Daw; Lauren L. Emberson (J. Cogn. Neurosci., 2020)

A simple model for learning in volatile environments. - Payam Piray; Nathaniel D. Daw (PLoS Comput. Biol., 2020)

Meta-Learning of Structured Task Distributions in Humans and Machines. - Sreejan Kumar; Ishita Dasgupta 001; Jonathan D. Cohen 003; Nathaniel D. Daw; Thomas L. Griffiths 001 (ICLR, 2021)

Deficient Goal-Directed Control in a Population Characterized by Extreme Goal Pursuit. - Karin Foerde; Nathaniel D. Daw; Teresa Rufin; B. Timothy Walsh; Daphna Shohamy; Joanna E. Steinglass (J. Cogn. Neurosci., 2021)

Leveraging psychometrics of rational inattention to estimate individual differences in the capacity for cognitive control. - Ham Huang; Ivan Grahek; Laura Bustamante; Nathaniel D. Daw; Andrew Caplin; Sebastian Musslick (CogSci, 2022)

Using natural language and program abstractions to instill human inductive biases in machines. - Sreejan Kumar; Carlos G. Correa; Ishita Dasgupta 001; Raja Marjieh; Michael Y. Hu; Robert D. Hawkins; Jonathan D. Cohen 003; Nathaniel D. Daw; Karthik Narasimhan; Tom Griffiths 001 (NeurIPS, 2022)

Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. - Sreejan Kumar; Ishita Dasgupta 001; Raja Marjieh; Nathaniel D. Daw; Jonathan D. Cohen 003; Thomas L. Griffiths 001 (CoRR, 2022)

Predictive and Interpretable: Combining Artificial Neural Networks and Classic Cognitive Models to Understand Human Learning and Decision Making. - Maria K. Eckstein; Christopher Summerfield; Nathaniel D. Daw; Kevin J. Miller (CogSci, 2023)

Exploring the hierarchical structure of human plans via program generation. - Carlos G. Correa; Sophia Sanborn; Mark K. Ho; Frederick Callaway; Nathaniel D. Daw; Thomas L. Griffiths 001 (CoRR, 2023)

Humans decompose tasks by trading off utility and computational cost. - Carlos G. Correa; Mark K. Ho; Frederick Callaway; Nathaniel D. Daw; Thomas L. Griffiths 001 (PLoS Comput. Biol., 2023)

Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. - Sreejan Kumar; Ishita Dasgupta 001; Nathaniel D. Daw; Jonathan D. Cohen 003; Thomas L. Griffiths 001 (PLoS Comput. Biol., 2023)

Inferring System and Optimal Control Parameters of Closed-Loop Systems from Partial Observations. - Victor Geadah; Juncal Arbelaiz; Harrison Ritz; Nathaniel D. Daw; Jonathan D. Cohen 003; Jonathan W. Pillow (CDC, 2024)

Program-Based Strategy Induction for Reinforcement Learning. - Carlos G. Correa; Thomas L. Griffiths 001; Nathaniel D. Daw (CoRR, 2024)

The role of training variability for model-based and model-free learning of an arbitrary visuomotor mapping. - Carlos A. Velazquez-Vargas; Nathaniel D. Daw; Jordan A. Taylor (PLoS Comput. Biol., 2024)