What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Quantum mechanics
- Neuroscience
His primary scientific interests are in Statistical physics, Body movement, Phase transition, Control theory and Order.
His work deals with themes such as Switching time, Observable and Dynamics, which intersect with Statistical physics.
His work on Motor control as part of general Control theory research is frequently linked to Selective control, bridging the gap between disciplines.
Order is integrated with Non-equilibrium thermodynamics and Biological motion in his study.
His studies deal with areas such as Synergetics, Phase and Component as well as Non-equilibrium thermodynamics.
His Movement research incorporates themes from Cognitive science, Stationary point, Stability and Dynamics.
His most cited work include:
- The uncontrolled manifold concept: identifying control variables for a functional task. (1040 citations)
- Dynamic pattern generation in behavioral and neural systems (947 citations)
- The dynamics of embodiment: a field theory of infant perseverative reaching. (686 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Perception, Computer vision, Control theory and Cognition.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Movement and Dynamics.
Gregor Schöner interconnects Motion, Statistical physics, Communication and Action in the investigation of issues within Perception.
Gregor Schöner combines subjects such as Representation and Obstacle avoidance, Mobile robot with his study of Computer vision.
His Cognition research is multidisciplinary, incorporating elements of Cognitive psychology and Cognitive science.
Gregor Schöner integrates Motor control with Body movement in his study.
He most often published in these fields:
- Artificial intelligence (39.09%)
- Perception (17.70%)
- Computer vision (15.64%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (39.09%)
- Perception (17.70%)
- Cognitive science (11.52%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Perception, Cognitive science, Cognition and Movement.
His Artificial intelligence research includes elements of Pattern recognition, Computer vision and Dynamics.
His Perception research integrates issues from Robotics, Ground and Intentionality, Direction of fit.
His studies in Cognitive science integrate themes in fields like Action, Autonomous agent, Memorization and Embodied cognition.
His Cognition study integrates concerns from other disciplines, such as Object-oriented programming, Cognitive psychology and Neural processing.
His Movement research is multidisciplinary, relying on both QUIET, Pendulum and Vestibular system.
Between 2013 and 2021, his most popular works were:
- Dynamic Thinking : A Primer on Dynamic Field Theory (47 citations)
- Dynamic interactions between visual working memory and saccade target selection. (37 citations)
- Motor equivalence during multi-finger accurate force production (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Quantum mechanics
- Neuroscience
His main research concerns Artificial intelligence, Control theory, Torque, Dynamical systems theory and Movement.
His Artificial intelligence study incorporates themes from Natural language processing, Computer vision and Action.
His research integrates issues of Robot, Visual perception and Trajectory in his study of Computer vision.
His work on Inverted pendulum as part of his general Control theory study is frequently connected to Joint, thereby bridging the divide between different branches of science.
Gregor Schöner has included themes like Neural activity, Recurrent neural network, Attractor and Reductionism in his Dynamical systems theory study.
His Movement study combines topics from a wide range of disciplines, such as Variation, Obstacle, Identifiability, Statistical model and Pattern recognition.
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