Learning of action through adaptive combination of motor primitives

By Kurt A. Thoroughman, Reza Shadmehr. Nature, 2000

1,054 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Control and Systems Engineering, Social Psychology

View Paper: https://www.semanticscholar.org/paper/d1082d770f276aaec38f158bb97bd083ba1c7cb9

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Citations in the corpus

A computational neuroanatomy for motor control (2008, background): “People produce similarly curved trajectories when they move in such fields (Thoroughman and Shadmehr 2000).”
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, positive): “Typically, learning the mapping from sensory inputs X to a motor command U has been conceptualized as a mapping U = P i wigiðXÞ, where gið,Þ are so-called basis functions and wi are the adjustable parameters of the mapping [2, 6, 15, 16].”
Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2012, background): “Since errors drive motor learning (Emken et al., 2007; Emken & Reinkensmeyer, 2005; Patton et al., 2006; Reisman et al., 2007; Thoroughman & Shadmehr, 2000; van Beers, 2009), error augmentation seems to be promising by definition.”
Learned dynamics of reaching movements generalize from dominant to nondominant arm. (2003, background): “It is also of theoretical interest because generalization is a powerful tool that can be used to uncover tuning properties of elements of neural computation (Poggio 1990; Thoroughman and Shadmehr 2000).”
Sensory prediction errors drive cerebellum-dependent adaptation of reaching. (2007, methodology): “STATE-SPACE MODEL OF TRIAL-TO-TRIAL LEARNING The third measure was derived from a recently developed state-space model of reach adaptation and generalization (Donchin et al. 2003; Thoroughman and Shadmehr 2000).”
Explicit and Implicit Contributions to Learning in a Sensorimotor Adaptation Task (2014, background): “Behavior in adaptation paradigms has been characterized within the framework of state-space models, in which a gradient descent process reduces error in a continuous and monotonic manner (Thoroughman and Shadmehr, 2000; Zarahn et al., 2008).”
Motor Adaptation as a Process of Reoptimization (2008, positive): “Thoroughman and Shadmehr (2000) reported that with adaptation, hand paths curved out beyond the baseline trajectory, suggesting an overcompensation in the forces that subjects produced.”
Does the motor control system use multiple models and context switching to cope with a variable environment? (2002, positive): “It has been demonstrated that catch trials interfere with learning (Thoroughman and Shadmehr 2000).”
Learning from Sensory and Reward Prediction Errors during Motor Adaptation (2011, background): “For example, generalization patterns during reach adaptation in force fields appear consistent with an encoding in which the neurons have activity fields that resemble those in the primary motor cortex [13,30].”
Principles of sensorimotor learning (2011, background): “Error-based learning is the driving force behind many well-studied adaptation paradigms, including prism adaptation 41 , saccade adaptation 42 , reaching in force fields 43,44 , visuomotor adaptation 45 and grip force adaptation 46 .”