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.
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