Computational principles of movement neuroscience

By D. M. Wolpert, Zoubin Ghahramani. Nature Neuroscience, 2000

2,122 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/53e5c18b0fdd93c536d840478cb85d76908b6ece

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Citations in the corpus

What Do Mirror Neurons Contribute to Human Social Cognition (2008, background): “If we give up the assumption of strict congruence between the motor and perceptual properties of MNs, the question arises: what brain structures does the agent share with a conspecific observing him? What computational theorists of action (Blakemore and Decety, 2001; Blakemore et al., 2001; Miall, 2”
Multiple Model-Based Reinforcement Learning (2002, positive): “The use of the softmax function for module selection and combination was originally proposed for a tracking control paradigm as the multiple paired forward-inverse models (MPFIM) (Wolpert & Kawato, 1998; Wolpert, Mi-all, & Kawato, 1998; Haruno, Wolpert, & Kawato, 1999 reformulated as modular selecti”
The Observation and Execution of Actions Share Motor and Somatosensory Voxels in all Tested Subjects: Single-Subject Analyses of Unsmoothed fMRI Data (2008, background): “Current theories of motor control assume that an efficient planning and execution of actions requires 2 types of internal models (Wolpert and Ghahramani 2000).”
The somatic marker hypothesis: a critical evaluation. (2006, neutral): “This is similar to accounts of how motor control of the periphery is regulated by advance modelling (e.g. Wolpert and Ghahramani, 2000).”
Perceptual basis of bimanual coordination (2001, background): “Thus there is no need to translate such a ‘motoric’ into a ‘perceptual’ representation or vice versa, using ‘internal models’ (ref”
A minimal architecture for joint action (2010, positive): “Predictions about the immediate future are achieved through motor simulation whereby internal models specify how actions will affect the environment and what sensory consequences they will have (Wolpert & Ghahramani, 2000).”
Dynamic intra- and interhemispheric interactions during unilateral and bilateral hand movements assessed with fMRI and DCM (2008, background): “Depending on various intrinsic and extrinsic factors, changes in the excitability of one area orchestrate excitability of other areas that belong to the same network (Fox et al., 2005; Wolpert and Ghahramani, 2000).”
Conscious intention and brain activity. (2001, background): “This is known in psychology as the problem of motor equivalence (Hebb, 1949), and as kinematic redundancy in the specific case of computational motor control (Wolpert and Ghahramani, 2000).”
Evidence for Multiple, Distinct Representations of the Human Body (2005, background): “&”
A unifying computational framework for motor control and social interaction. (2003, background): “We can view the motor system as forming a loop in which motor commands cause muscle contractions, with consequent sensory feedback, which in turn influences future motor commands (Wolpert & Ghahramani 2000) (figure 1a).”
Touching a rubber hand: feeling of body ownership is associated with activity in multisensory brain areas (2005, background): “It has also been proposed that cerebellar mechanisms are involved in the distinction between self produced sensory signals and externally produced stimuli by detecting mismatches between the predicted sensory consequences of the voluntary movements and the actual sensory feedback (Wolpert and Ghahra”
Towards a dynamic theory of intentions (2009, background): “…illustration, when you move your eye using your eye muscles, the sensory consequences of this movement are anticipated and the displacement 6 See, for instance, Wolpert (1997), Jordan & Wolpert (1999) and Wolpert & Ghahramani (2000) for a review of neurocomputational approaches to motor control.”
Coding and use of tactile signals from the fingertips in object manipulation tasks (2009, background): “In general terms, our framework agrees with control theories which imply that control turns desired sensory states into motor commands and prediction maps motor commands into expected sensory consequence”