What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Neuroscience
- Statistics
Daniel M. Wolpert mostly deals with Motor control, Neuroscience, Sensory system, Motor system and Motor learning.
The various areas that he examines in his Motor control study include Artificial neural network, Cerebellum and Internal model.
His Neuroscience study incorporates themes from Electronic engineering and Computational model.
The concepts of his Motor system study are interwoven with issues in Illusion and Object dynamics.
His research in Motor learning intersects with topics in Computational neuroscience, Artificial intelligence, Developmental psychology, Control theory and Cognitive science.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Computer vision.
His most cited work include:
- An Internal Model for Sensorimotor Integration (2488 citations)
- Signal-dependent noise determines motor planning (1927 citations)
- Noise in the nervous system. (1793 citations)
What are the main themes of his work throughout his whole career to date?
Daniel M. Wolpert focuses on Artificial intelligence, Neuroscience, Motor learning, Motor control and Cognitive psychology.
In his work, Bayes' theorem and Probabilistic logic is strongly intertwined with Machine learning, which is a subfield of Artificial intelligence.
His Motor learning study also includes fields such as
- Communication that intertwine with fields like Control theory,
- Sensorimotor control most often made with reference to Cognitive science.
Daniel M. Wolpert has researched Motor control in several fields, including Artificial neural network, Control and Internal model.
Daniel M. Wolpert has included themes like Visual perception, Prior probability, Cognition and Motion in his Cognitive psychology study.
His work deals with themes such as Perception, Audiology, Stimulus, Sensation and Set, which intersect with Sensory system.
He most often published in these fields:
- Artificial intelligence (25.58%)
- Neuroscience (23.92%)
- Motor learning (25.91%)
What were the highlights of his more recent work (between 2014-2021)?
- Motor learning (25.91%)
- Cognitive psychology (22.92%)
- Artificial intelligence (25.58%)
In recent papers he was focusing on the following fields of study:
Daniel M. Wolpert mainly investigates Motor learning, Cognitive psychology, Artificial intelligence, Set and Neuroscience.
His biological study spans a wide range of topics, including Object dynamics, Motor skill and Reinforcement.
His Cognitive psychology research incorporates themes from Motion, Gaze, Consciousness, Bayesian inference and Selection.
His Artificial intelligence research is multidisciplinary, relying on both Visual perception, Sensorimotor control, Computer vision and Pattern recognition.
His Set research is multidisciplinary, incorporating elements of Motor behavior, Cognition and Sensory system.
Daniel M. Wolpert studies Motor control, a branch of Neuroscience.
Between 2014 and 2021, his most popular works were:
- A common mechanism underlies changes of mind about decisions and confidence (128 citations)
- Effective reinforcement learning following cerebellar damage requires a balance between exploration and motor noise. (86 citations)
- Decision-making in sensorimotor control. (77 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Neuroscience
- Statistics
The scientist’s investigation covers issues in Motor learning, Cognitive psychology, Sensorimotor control, Artificial intelligence and Human–computer interaction.
His Motor learning research includes themes of Cerebellum, Reinforcement and Reinforcement learning.
In his study, Bounded function is strongly linked to Motion, which falls under the umbrella field of Cognitive psychology.
His work carried out in the field of Sensorimotor control brings together such families of science as Cognitive science, Control and Selection.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Perception and Pattern recognition.
His Human–computer interaction study which covers Motor system that intersects with Object, Communication and Feed forward.
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