What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Neuroscience
- Statistics
Neuroscience, Cognitive psychology, Reinforcement learning, Artificial intelligence and Dopamine are his primary areas of study.
His work deals with themes such as Temporal difference learning, Classical conditioning, Orbitofrontal cortex, Cognition and Incentive salience, which intersect with Cognitive psychology.
Peter Dayan has researched Temporal difference learning in several fields, including PVLV and Neural substrate.
His study in Reinforcement learning is interdisciplinary in nature, drawing from both Convergence, Simple, Reinforcement and Markov chain.
His work on Artificial neural network, Representation and Connectionism as part of general Artificial intelligence study is frequently linked to Function, bridging the gap between disciplines.
Peter Dayan has included themes like Dorsal raphe nucleus, Neurotransmitter and Serotonin in his Dopamine study.
His most cited work include:
- A Neural Substrate of Prediction and Reward (6422 citations)
- Technical Note : \cal Q -Learning (3063 citations)
- Technical Note Q-Learning (2848 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Neuroscience, Cognitive psychology, Reinforcement learning and Machine learning.
In most of his Artificial intelligence studies, his work intersects topics such as Pattern recognition.
His Neuroscience study frequently links to adjacent areas such as Serotonin.
His Cognitive psychology study combines topics from a wide range of disciplines, such as Cognition, Control and Normative.
His Reinforcement learning research is multidisciplinary, incorporating elements of Cognitive science, Reinforcement and Action.
He studies Dopaminergic, a branch of Dopamine.
He most often published in these fields:
- Artificial intelligence (26.20%)
- Neuroscience (23.71%)
- Cognitive psychology (24.42%)
What were the highlights of his more recent work (between 2018-2021)?
- Cognitive psychology (24.42%)
- Reinforcement learning (19.96%)
- Artificial intelligence (26.20%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Cognitive psychology, Reinforcement learning, Artificial intelligence, Neuroscience and Control.
His Cognitive psychology research includes themes of Value, Internalizing psychopathology, Ventromedial prefrontal cortex, Contingency and Volatility.
His Reinforcement learning research is multidisciplinary, incorporating perspectives in Representation, Cognition, Structure, Range and Outcome.
His Artificial intelligence research focuses on Machine learning and how it connects with Model free.
His studies link Anticipation with Neuroscience.
In his study, Decision problem is inextricably linked to Statistics, which falls within the broad field of Control.
Between 2018 and 2021, his most popular works were:
- Retrospective model-based inference guides model-free credit assignment (30 citations)
- Retrospective model-based inference guides model-free credit assignment (30 citations)
- Altered learning under uncertainty in unmedicated mood and anxiety disorders (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Neuroscience
His primary scientific interests are in Reinforcement learning, Cognitive psychology, Artificial intelligence, Function and Control.
His Reinforcement learning study combines topics in areas such as Inference, Anxiety, Action selection, Optogenetics and Outcome.
His research in Cognitive psychology tackles topics such as Brain activity and meditation which are related to areas like Value.
The various areas that Peter Dayan examines in his Artificial intelligence study include Machine learning and Action.
His research integrates issues of Relevance, Sophistication, Pattern recognition, Cognitive map and Normative in his study of Control.
Functional magnetic resonance imaging is a primary field of his research addressed under Neuroscience.
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