A computational neuroanatomy for motor control

By Reza Shadmehr, John W. Krakauer. 2008

1,211 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Neurology, Social Psychology

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Citations in the corpus

Speech Production as State Feedback Control (2011, background): “Since then, the problem of motor control has been formulated in state-space terms like those discussed above (Arbib, 1981), and observer-based SFC models of reaching motor control have been advanced to explain how people optimize their movements (Todorov and Jordan, 2002; Todorov, 2004; Guigon et al”
Noninvasive cortical stimulation enhances motor skill acquisition over multiple days through an effect on consolidation (2009, background): “Within the computational framework of optimal feedback control, it could be posited that adaptation is mediated through changes in a forward model, whereas skill represents a slower process of acquiring an optimal feedback control policy (26).”
Computational mechanisms of sensorimotor control. (2011, neutral): “…lines of research have suggested that the posterior parietal cortex is involved in state estimation (Desmurget et al., 2001; Wolpert et al., 1998a) through receiving predicted information (see the section ''Forward Models and Predictive Control'') via the cerebellum (Shadmehr and Krakauer, 2008).”
Consensus Paper: The Cerebellum's Role in Movement and Cognition (2013, background): “Its role in the temporal organization, coordination, and finessing of actions can be conceptualized in terms of adaptive filtering and internal modeling of the body, learned through adaptive processes dependent on sensory prediction errors [192 – 196].”
Dissociating the roles of the cerebellum and motor cortex during adaptive learning: the motor cortex retains what the cerebellum learns. (2011, methodology): “Trial-by-trial analysis of this epoch (ANOVARM: GROUP [SHAM, CB, M1] 3 TRIAL [8]) revealed a significant main effect of GROUP (F2,27 = 8; P = 0.001), TRIAL (F7,189 = 8; P = 0.0005) and GROUP 3 TRIAL interaction (F14,189 = 2; P = 0.01).”
Multimodal integration in granule cells as a basis for associative plasticity and sensory prediction in a cerebellum-like circuit. (2010, background): “…evidence from clinical, human behavioral, theoretical, and physiological studies suggest a role for the cerebellum in predicting sensory inputs or states of the motor system (Bastian, 2006; Dean et al., 2002; Ebner and Pasalar, 2008; Miall et al., 1993; Paulin, 2005; Shadmehr and Krakauer, 2008).”
Error correction, sensory prediction, and adaptation in motor control. (2010, background): “In the current computational view of motor control (e.g., Shadmehr and Krakauer (2008)), the cerebellum may be a forward model (Pasalar et al., 2006) that uses efferent copy to predict consequences of motor commands and to correct the movement as it is being generated (Fig.”
Consensus Paper: Towards a Systems-Level View of Cerebellar Function: the Interplay Between Cerebellum, Basal Ganglia, and Cortex (2016, background): “Current theories of motor control suggest that the anatomical connection between the cerebellum and PPC plays a critical role in maintaining accurate voluntary movements [34].”
Neural correlates of action: Comparing meta‐analyses of imagery, observation, and execution (2018, neutral): “, 2003), and there is evidence that the basal ganglia are involved in assessing the energetic costs associated with movements (Shadmehr and Krakauer, 2008).”
Learning from Sensory and Reward Prediction Errors during Motor Adaptation (2011, methodology): “Based on this theory, our model of the optimal learner was composed of two components: reinforcement learning for action selection, and state estimation for identifying the sensory consequences of motor commands [27].”
Action and behavior: a free-energy formulation (2010, background): “In conventional motor control theory (Wolpert and Miall 1996; Todorov and Jordan 2002; Shadmehr and Krakauer 2008), there are two models, an inverse model and a forward model.”
Reinforcement Learning or Active Inference? (2009, background): “If these ideas are valid; then they suggest that value-learning, reinforcement learning, dynamic programming and expected utility theory may be incomplete metaphors for how complex biological systems actually operate and speak to a fundamental role for perception in action; see [58–60] and [61].”
A quantitative meta-analysis and review of motor learning in the human brain (2013, background): “It is, however, notable that Shadmehr and Krakauer (2008) primarily considered sensorimotor tasks, while Robertson (2007)was specifically reviewing SRTT variants.”
Sensorimotor Integration in Speech Processing: Computational Basis and Neural Organization (2011, positive): “Our proposed SFC model itself integrates work in psycho-and neurolinguistics with a recently outlined SFC model of speech production (Ventura et al., 2009), which itself derives from recent work on SFC systems in the visuo-manual domain (Shadmehr and Krakauer, 2008).”
Contributions of the basal ganglia and functionally related brain structures to motor learning. (2009, neutral): “[98] Shadmehr R, Krakauer JW.”