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.
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.
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.
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.
By Carrot or by Stick: Cognitive Reinforcement Learning in Parkinsonism
Michael J. Frank;Lauren C. Seeberger;Randall C. O'Reilly.
Frontal theta as a mechanism for cognitive control
James F. Cavanagh;Michael J. Frank.
Trends in Cognitive Sciences (2014)
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)
Hold your horses: impulsivity, deep brain stimulation, and medication in parkinsonism.
Michael J. Frank;Johan Samanta;Johan Samanta;Ahmed A. Moustafa;Scott J. Sherman.
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)
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)
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)
Hold your horses: a dynamic computational role for the subthalamic nucleus in decision making
Michael J. Frank.
Neural Networks (2006)
Anatomy of a decision: striato-orbitofrontal interactions in reinforcement learning, decision making, and reversal.
Michael J. Frank;Eric D. Claus.
Psychological Review (2006)
From reinforcement learning models to psychiatric and neurological disorders
Tiago V Maia;Michael J Frank.
Nature Neuroscience (2011)
Profile was last updated on December 6th, 2021.
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