Learning to move amid uncertainty.

By R. Scheidt, Jonathan B. Dingwell, Ferdinando A. Mussa-Ivaldi. Journal of Neurophysiology, 2001

451 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/7b41a1ce13fc7c873bf5d64cd245c6a3e4057464

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. Reza Shadmehr
2. Opher Donchin
3. Maurice A. Smith
4. David J. Reinkensmeyer
5. Emilio Bizzi
6. Jeremy L. Emken
7. Steven C. Cramer
8. Jordan A. Taylor

In Cognitive Neuroscience

1. Reza Shadmehr
2. Jeremy L. Emken
3. David J. Reinkensmeyer
4. Opher Donchin
5. Steven C. Cramer
6. Joseph T. Francis
7. Maurice A. Smith
8. Emilio Bizzi

In Social Psychology

1. David J. Reinkensmeyer
2. Jeremy L. Emken
3. Opher Donchin
4. Reza Shadmehr
5. Steven C. Cramer
6. Joseph T. Francis
7. Maurice A. Smith
8. Emilio Bizzi

Citations in the corpus

• Interacting Adaptive Processes with Different Timescales Underlie Short-Term Motor Learning (2006, background): “randomly generated disturbance sequences [4,5] and to assess”
• A theory for how sensorimotor skills are learned and retained in noisy and nonstationary neural circuits (2013, background): “This assumption, standard for neural networks, is supported by the rapid error reduction observed in sensorimotor adaptation experiments (13, 14) and the autocorrelation of errors seen during unperturbed movements (15).”
• Motor Task Variation Induces Structural Learning (2009, unknown): “In such randomly varying situations, previous studies suggest that subjects cannot represent these multiple transformations and only learn the average [20–25].”
• Computational mechanisms of sensorimotor control. (2011, unknown): “Other situations, such as those that are unstable (Burdet et al., 2001) or unpredictable (Scheidt et al., 2001; Takahashi et al., 2001), add to uncertainty about the environment.”
• Does the motor control system use multiple models and context switching to cope with a variable environment? (2002, unknown): “A single model can explain the " learning attempt " in the data The influence of the previous movement was observed by Thoroughman and Shadmehr (2000) and by Scheidt et al. (2001).”
• Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic amplification (2005, methodology): “The equation is similar to ones described previously to model adaptation during reaching to a viscous force field [15], [16] except that it does not assume that .”
• Computational principles of sensorimotor control that minimize uncertainty and variability (2007, unknown): “Ifthefieldstrengthoneach movementisdrawnfromaGaussiandistribution,sub-jectslearntocompensateforthemeanforceexperienced overthepreviousfewtrials,whileincreasingstiffness tocounteracttheeffectsofvariabilityaboutthemean (Scheidt et al. 2001; Takahashi et al. 2001).”
• Robotics, motor learning, and neurologic recovery. (2004, methodology): “When the gain of a viscous curl field was varied from reach to reach according to a normal distribution, the nervous system formed an internal model that estimated the mean gain of the field, although the estimate was low in some cases (12, 31).”
• Quantifying Generalization from Trial-by-Trial Behavior of Adaptive Systems that Learn with Basis Functions: Theory and Experiments in Human Motor Control (2003, background): “There is a long tradition of modeling the relationship between past and current behavior using linear models (Falmagne et al., 1975; Kowler et al., 1984; Smeets and Brenner, 1995; Thoroughman and Shadmehr, 2000; Scheidt et al., 2001; Witney et al., 2001).”