D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Neuroscience D-index 79 Citations 28,720 190 World Ranking 950 National Ranking 511

Research.com Recognitions

Awards & Achievements

2003 - Fellow of the American Association for the Advancement of Science (AAAS)

Overview

What is he best known for?

The fields of study he is best known for:

  • Neuroscience
  • Artificial intelligence
  • Cognition

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 most cited work include:

  • Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control (1670 citations)
  • Cortical substrates for exploratory decisions in humans (1552 citations)
  • Model-based influences on humans' choices and striatal prediction errors. (966 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Cognitive psychology (42.80%)
  • Neuroscience (46.19%)
  • Reinforcement learning (36.86%)

What were the highlights of his more recent work (between 2018-2021)?

  • Cognitive psychology (42.80%)
  • Reinforcement learning (36.86%)
  • Artificial intelligence (21.61%)

In recent papers he was focusing on the following fields of study:

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.

Between 2018 and 2021, his most popular works were:

  • Specialized coding of sensory, motor and cognitive variables in VTA dopamine neurons (125 citations)
  • Hippocampal Contributions to Model-Based Planning and Spatial Memory (50 citations)
  • The opportunity cost of time modulates cognitive effort. (29 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Cognition
  • Neuroscience

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

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

Nathaniel D Daw;Yael Niv;Yael Niv;Peter Dayan.
Nature Neuroscience (2005)

2519 Citations

Cortical substrates for exploratory decisions in humans

Nathaniel D. Daw;John P. O'Doherty;Peter Dayan;Ben Seymour.
Nature (2006)

2221 Citations

Model-based influences on humans' choices and striatal prediction errors.

Nathaniel D. Daw;Samuel J. Gershman;Ben Seymour;Peter Dayan.
Neuron (2011)

1516 Citations

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.
Nature (2013)

1192 Citations

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.
Neuron (2010)

1163 Citations

Tonic dopamine: opportunity costs and the control of response vigor.

Yael Niv;Yael Niv;Nathaniel D. Daw;Daphna Joel;Peter Dayan.
Psychopharmacology (2007)

1067 Citations

Opponent interactions between serotonin and dopamine

Nathaniel D. Daw;Sham Kakade;Peter Dayan.
Neural Networks (2002)

902 Citations

The computational neurobiology of learning and reward

Nathaniel Douglass Daw;Kenji Doya.
Current Opinion in Neurobiology (2006)

619 Citations

Decision theory, reinforcement learning, and the brain

Peter Dayan;Nathaniel D. Daw.
Cognitive, Affective, & Behavioral Neuroscience (2008)

593 Citations

A computational substrate for incentive salience

Samuel M. McClure;Nathaniel Douglass Daw;P. Read Montague.
Trends in Neurosciences (2003)

506 Citations

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Max Planck Institute for Biological Cybernetics

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Karl J. Friston

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Geoffrey Schoenbaum

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National Institutes of Health

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John P. O'Doherty

John P. O'Doherty

California Institute of Technology

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Quentin J. M. Huys

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University College London

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Timothy E.J. Behrens

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