Persistence of motor adaptation during constrained, multi-joint, arm movements.

By R. Scheidt, David J. Reinkensmeyer, M. Conditt, W. Rymer, Ferdinando A. Mussa-Ivaldi. Journal of Neurophysiology, 2000

440 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Physical Therapy, Sports Therapy and Rehabilitation

View Paper: https://www.semanticscholar.org/paper/c3399194e1b88b1c557369d49d7ec99587cc03b8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Etienne Burdet
2. John W. Krakauer
3. David W. Franklin
4. Reza Shadmehr
5. Mitsuo Kawato
6. Jeremy L. Emken
7. David J. Reinkensmeyer
8. Ferdinando A. Mussa-Ivaldi

In Cognitive Neuroscience

1. Reza Shadmehr
2. John W. Krakauer
3. David W. Franklin
4. Etienne Burdet
5. Jeremy L. Emken
6. David J. Reinkensmeyer
7. Mitsuo Kawato
8. Steven C. Cramer

In Physical Therapy, Sports Therapy and Rehabilitation

1. Ferdinando A. Mussa-Ivaldi
2. David W. Franklin
3. David J. Reinkensmeyer
4. Jeremy L. Emken
5. Steven C. Cramer
6. Reza Shadmehr
7. Etienne Burdet

Citations in the corpus

• Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2012, background): “This freedom may facilitate motor learning (Scheidt et al., 2000) and can vary from completely unconstrained movements in terms of position and timing errors (e.g., provided by zero impedance control; Blaya & Herr, 2004) to completely restricted movements as provided by position con-trol.”
• Interacting Adaptive Processes with Different Timescales Underlie Short-Term Motor Learning (2006, methodology): “In this experiment we also used the robot arm to create a virtual force channel in order to clamp lateral errors to zero on selected trials [26].”
• CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm (2008, background): “1C) is reduced as performance improves this algorithm could also accurately predict the slow decrease in learned lateral force when a subject is unaware that a lateral perturbing force has been replaced by a virtual channel (Scheidt et al., 2000).”
• Error correction, sensory prediction, and adaptation in motor control. (2010, background): “The idea that the process of memory formation proceeds on multiple timescales is, of course, not new as multiple timescales have long been observed in learning curves and forgetting curves (Rubin and Wenzel, 1996;Scheidt et al., 2000;Wixted, 2004).”
• Motor Adaptation as a Process of Reoptimization (2008, neutral): “For example, in channel trials in which we and others have measured the forces that subjects produce, the force trajectory is at most 80 – 82% of the imposed field (Scheidt et al., 2000; Hwang et al., 2006; Smith et al., 2006).”
• Robotics, motor learning, and neurologic recovery. (2004, background): “(33) applied a viscous force field perpendicular to the reaching direction to investigate this question, then monitored deadaptation A nn u.”
• Evidence for a dynamic-dominance hypothesis of handedness (2001, background): “(Shadmehr and Mussa-Ivaldi 1994; Sainburg et al. 1995; Gandolfo et al. 1996; Shadmehr and Brashers-Krug 1997; Sainburg et al. 1999; Scheidt et al. 2000; Shadmehr and Moussavi 2000) Aftereffects have also been demonstrated following adaptation to distortion visual feedback displays (Baily 1972;…”
• Use-Dependent and Error-Based Learning of Motor Behaviors (2010, neutral): “4A) (Scheidt et al., 2000; Wagner and Smith, 2008).”
• Does the motor control system use multiple models and context switching to cope with a variable environment? (2002, positive): “For further details about the robotic manipulandum, see Shadmehr and Mussa-Ivaldi (1994), Conditt and Mussa-Ivaldi (1999), and Scheidt et al. (2000).”