2003 - Fellow of the American Association for the Advancement of Science (AAAS)
Nathaniel D. Daw mostly deals with Neuroscience, Reinforcement learning, Cognitive psychology, Reinforcement and Prefrontal cortex. His work on Dopamine, Striatum, Brain mapping and Dopaminergic as part of general Neuroscience research is frequently linked to Function, thereby connecting diverse disciplines of science. His work deals with themes such as Developmental psychology, Artificial neural network and Cognitive resource theory, which intersect with Reinforcement learning.
His Cognitive psychology study combines topics in areas such as Functional magnetic resonance imaging, Orbitofrontal cortex and Visual cortex. His Reinforcement research is multidisciplinary, relying on both Risk analysis, Cognitive neuroscience and Artificial intelligence. Nathaniel D. Daw has researched Prefrontal cortex in several fields, including Neurophysiology, Sensory system and Elementary cognitive task.
His primary areas of investigation include Cognitive psychology, Neuroscience, Reinforcement learning, Artificial intelligence and Cognition. The study incorporates disciplines such as Developmental psychology, Mechanism, Functional magnetic resonance imaging and Action in addition to Cognitive psychology. The Dopamine, Striatum, Brain mapping and Prefrontal cortex research Nathaniel D. Daw does as part of his general Neuroscience study is frequently linked to other disciplines of science, such as Mean squared prediction error, therefore creating a link between diverse domains of science.
Nathaniel D. Daw has included themes like Tonic, Parkinson's disease and Perseveration in his Dopamine study. His Reinforcement learning research incorporates elements of Working memory, Cognitive science, Reinforcement and Computational model. Nathaniel D. Daw interconnects Control and Hippocampus in the investigation of issues within Cognition.
The scientist’s investigation covers issues in Cognitive psychology, Reinforcement learning, Artificial intelligence, Neuroscience and Cognition. His work on Encoding as part of general Cognitive psychology study is frequently connected to Time constrained, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His work in Reinforcement learning tackles topics such as Action which are related to areas like Sentence and Credit assignment.
His research investigates the connection between Artificial intelligence and topics such as Machine learning that intersect with problems in Null and Meta learning. Nathaniel D. Daw performs integrative study on Neuroscience and Mean squared prediction error. As a part of the same scientific study, Nathaniel D. Daw usually deals with the Cognition, concentrating on Control and frequently concerns with Value and Statistics.
The scientist’s investigation covers issues in Cognition, Neuroscience, Reinforcement learning, Cognitive psychology and Control. His research in the fields of Free recall overlaps with other disciplines such as Wisconsin Card Sorting Test. His work often combines Neuroscience and Calcium imaging studies.
As a member of one scientific family, Nathaniel D. Daw mostly works in the field of Reinforcement learning, focusing on Action and, on occasion, Sentence. His work in the fields of Cognitive psychology, such as Encoding, overlaps with other areas such as Context model. His studies in Control integrate themes in fields like Perception and Elementary cognitive task.
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Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control
Nathaniel D Daw;Yael Niv;Yael Niv;Peter Dayan.
Nature Neuroscience (2005)
Cortical substrates for exploratory decisions in humans
Nathaniel D. Daw;John P. O'Doherty;Peter Dayan;Ben Seymour.
Model-based influences on humans' choices and striatal prediction errors.
Nathaniel D. Daw;Samuel J. Gershman;Ben Seymour;Peter Dayan.
The importance of mixed selectivity in complex cognitive tasks
Mattia Rigotti;Omri Barak;Omri Barak;Melissa R. Warden;Melissa R. Warden;Xiao Jing Wang;Xiao Jing Wang.
States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning
Jan Gläscher;Nathaniel Douglass Daw;Peter Dayan;John P. O'Doherty;John P. O'Doherty.
Tonic dopamine: opportunity costs and the control of response vigor.
Yael Niv;Yael Niv;Nathaniel D. Daw;Daphna Joel;Peter Dayan.
Opponent interactions between serotonin and dopamine
Nathaniel D. Daw;Sham Kakade;Peter Dayan.
Neural Networks (2002)
The computational neurobiology of learning and reward
Nathaniel Douglass Daw;Kenji Doya.
Current Opinion in Neurobiology (2006)
Decision theory, reinforcement learning, and the brain
Peter Dayan;Nathaniel D. Daw.
Cognitive, Affective, & Behavioral Neuroscience (2008)
A computational substrate for incentive salience
Samuel M. McClure;Nathaniel Douglass Daw;P. Read Montague.
Trends in Neurosciences (2003)
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