H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Neuroscience H-index 81 Citations 27,898 176 World Ranking 563 National Ranking 313
Psychology H-index 84 Citations 29,317 183 World Ranking 688 National Ranking 442

Overview

What is he best known for?

The fields of study he is best known for:

  • Neuroscience
  • Cognition
  • Artificial intelligence

Michael J. Frank focuses on Neuroscience, Cognition, Reinforcement learning, Prefrontal cortex and Reinforcement. His Neuroscience study frequently links to adjacent areas such as Subthalamic nucleus. His work on Working memory and Continuous performance task as part of general Cognition study is frequently linked to Mechanism, bridging the gap between disciplines.

Michael J. Frank combines subjects such as Catechol-O-methyl transferase, Probabilistic logic, Cognitive science and Adaptive learning with his study of Reinforcement learning. His studies examine the connections between Prefrontal cortex and genetics, as well as such issues in Computational model, with regards to Attention deficit hyperactivity disorder and Stimulant. Michael J. Frank interconnects Stimulus, Cognitive psychology, Disease and Outpatient clinic in the investigation of issues within Reinforcement.

His most cited work include:

  • By Carrot or by Stick: Cognitive Reinforcement Learning in Parkinsonism (1500 citations)
  • Frontal theta as a mechanism for cognitive control (912 citations)
  • Hold your horses: impulsivity, deep brain stimulation, and medication in parkinsonism. (818 citations)

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

Michael J. Frank mostly deals with Neuroscience, Reinforcement learning, Cognitive psychology, Cognition and Prefrontal cortex. His Neuroscience research includes themes of Parkinson's disease and Subthalamic nucleus. His Reinforcement learning research includes elements of Stimulus, Reinforcement, Electroencephalography and Developmental psychology.

His Electroencephalography research is multidisciplinary, relying on both Frontal lobe and Brain mapping. The various areas that Michael J. Frank examines in his Cognitive psychology study include Contrast, Schizophrenia, Expected value, Probabilistic logic and Surprise. His work in Cognition covers topics such as Computational model which are related to areas like Cognitive science.

He most often published in these fields:

  • Neuroscience (39.11%)
  • Reinforcement learning (35.89%)
  • Cognitive psychology (30.65%)

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

  • Reinforcement learning (35.89%)
  • Cognitive psychology (30.65%)
  • Artificial intelligence (12.10%)

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

His primary areas of study are Reinforcement learning, Cognitive psychology, Artificial intelligence, Neuroscience and Artificial neural network. His Reinforcement learning study frequently involves adjacent topics like Stimulus. His Cognitive psychology study combines topics in areas such as Contrast, Electroencephalography, Cognitive effort, Schizophrenia and Surprise.

The Artificial intelligence study combines topics in areas such as Machine learning and State. His research integrates issues of Credit assignment and Subthalamic nucleus in his study of Neuroscience. His Sulpiride study combines topics from a wide range of disciplines, such as Sequential sampling and Cognition.

Between 2018 and 2021, his most popular works were:

  • Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work. (43 citations)
  • Positive reward prediction errors during decision-making strengthen memory encoding. (36 citations)
  • Statistical context dictates the relationship between feedback-related EEG signals and learning (24 citations)

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

  • Cognition
  • Artificial intelligence
  • Neuroscience

His scientific interests lie mostly in Reinforcement learning, Cognitive psychology, Neuroscience, Artificial intelligence and Depression. The study of Reinforcement learning is intertwined with the study of Motivational deficit in a number of ways. His biological study deals with issues like Surprise, which deal with fields such as Contrast, Speed learning and Memory consolidation.

Much of his study explores Neuroscience relationship to Subthalamic nucleus. The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and State. The Depression study which covers Perception that intersects with Cognition and Major depressive disorder.

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.

Top Publications

By Carrot or by Stick: Cognitive Reinforcement Learning in Parkinsonism

Michael J. Frank;Lauren C. Seeberger;Randall C. O'Reilly.
Science (2004)

1864 Citations

Frontal theta as a mechanism for cognitive control

James F. Cavanagh;Michael J. Frank.
Trends in Cognitive Sciences (2014)

1155 Citations

Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia

Randall C. O'Reilly;Michael J. Frank.
Neural Computation (2006)

1044 Citations

Hold your horses: impulsivity, deep brain stimulation, and medication in parkinsonism.

Michael J. Frank;Johan Samanta;Johan Samanta;Ahmed A. Moustafa;Scott J. Sherman.
Science (2007)

1025 Citations

Triangulating a Cognitive Control Network Using Diffusion-Weighted Magnetic Resonance Imaging (MRI) and Functional MRI

Adam R. Aron;Tim E. Behrens;Steve Smith;Michael J. Frank.
The Journal of Neuroscience (2007)

962 Citations

Dynamic Dopamine Modulation in the Basal Ganglia: A Neurocomputational Account of Cognitive Deficits in Medicated and Nonmedicated Parkinsonism

Michael J. Frank.
Journal of Cognitive Neuroscience (2005)

949 Citations

Interactions between frontal cortex and basal ganglia in working memory: a computational model.

Michael J. Frank;Bryan Loughry;Randall C. O’Reilly.
Cognitive, Affective, & Behavioral Neuroscience (2001)

939 Citations

Hold your horses: a dynamic computational role for the subthalamic nucleus in decision making

Michael J. Frank.
Neural Networks (2006)

668 Citations

Anatomy of a decision: striato-orbitofrontal interactions in reinforcement learning, decision making, and reversal.

Michael J. Frank;Eric D. Claus.
Psychological Review (2006)

636 Citations

From reinforcement learning models to psychiatric and neurological disorders

Tiago V Maia;Michael J Frank.
Nature Neuroscience (2011)

623 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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