Principles of sensorimotor learning

By D. M. Wolpert, Jörn Diedrichsen, Randall Flanagan. Nature Reviews Neuroscience, 2011

1,361 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/ebba6d8eb60a0b591167f4f8ede4398df42de50a

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Citations in the corpus

Consequences of the Now-or-Never bottleneck for signed versus spoken languages (2015, methodology): “…local dependencies (Hawkins 2004) Anticipation Predictive processing Fast, top-down visual processing (Bar 2004); forward models in motor control (Wolpert et al. 2011); predictive coding (Clark 2013) Syntactic prediction (Jurafsky 1996); multiple-cue integration (Farmer et al. 2006); visual…”
Moving a Rubber Hand that Feels Like Your Own: A Dissociation of Ownership and Agency (2012, positive): “…events can be learned (Sato and Yasuda, 2005; Sato, 2009; Spengler et al., 2009) and thereafter predicted, and novel internal models can be acquired to facilitate tool use (Imamizu et al.,2000;Wolpert et al., 2011), extending the principles of the comparator model to object-directed actions.”
Pictorial Human Spaces: How Well Do Humans Perceive a 3D Articulated Pose? (2013, methodology): “The synchronization between the two cameras is done automatically by the system.”
Deep predictive policy training using reinforcement learning (2017, methodology): “Another related approach is a biologically inspired method [1] to split a complex behavior into a set of basic motor primitives.”
Intentions and Motor Representations: the Interface Challenge (2017, methodology): “This Bayesian framework has also been applied to motor learning, contributing to important progress in our understanding of the computational aspects of such learning (e.g., Wolpert et al. 2011; Orbán and Wolpert 2011).”
Learning state representation for deep actor-critic control (2016, background): “One important aspect of learning to control is to learn how to efficiently gather the task-relevant sensory information necessary to make informed decisions [6].”
Emergent vision system of autonomous virtual characters (2016, background): “Reinforcement Learning in sensorimotor learning is a process that depends on rewards to guide improvements on motor commands in order to achieve a solution of a task [23].”
A tutorial on task-parameterized movement learning and retrieval (2015, background): “The model shares links with optimal feedback control strategies in which deviations from an average trajectory are corrected only when they interfere with task performance, resulting in a controller satisfying minimal intervention principle [94, 103].”
Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2012, background): “This matches with a theory of optimal motor control, stating that task-irrelevant errors or variability coming from sensory – motor noise should be left untreated to maximize performance (Liu & Todorov, 2007; Todorov, 2004; Todorov & Jordan, 2002; Wei & Körding, 2009; Wolpert et al., 2011).”