Signal-dependent noise determines motor planning

By C. Harris, D. M. Wolpert. 1998

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Citations in the corpus

Parabolic movement primitives and cortical states: merging optimality with geometric invariance (2009, background): “control (Todorov and Jordan 2002; Scott 2004), movement accuracy ( Harris and Wolpert 1998; Osu et al. 2004), or a trade-off between movement accuracy and duration (Harris and Wolpert 2006; Tanaka et al. 2006).”
Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG. (2002, background): “This hypothesis of stiffness control may also be at odds with Harris and Wolpert (1998), because stiffness increases require larger motor commands, which in turn introduce higher levels of noise that causes larger trajectory errors.”
Evidence for the Flexible Sensorimotor Strategies Predicted by Optimal Feedback Control (2007, background): “Asinpreviousstochasticoptimalcontrolmodels (HarrisandWolpert,1998;Todorov,2002;TodorovandJordan, 2002b),takingintoaccounttheempiricallyestablishedsignal-dependentnatureofmotornoise(SuttonandSykes,1967; Schmidtetal.,1979;Todorov,2002;Hamiltonetal.,2004)turns outtobeimportant.”
Stochastic Optimal Control and Estimation Methods Adapted to the Noise Characteristics of the Sensorimotor System (2005, methodology): “…shown, however, that smoothness does not have to be explicitly enforced by the cost function; open-loop minimization of end-point error was found sufficient to produce realistic trajectories , provided that the multiplicative nature of motor noise is taken into account (Harris & Wolpert, 1998).”
Modelling three‐dimensional trajectories by using Bézier curves with application to hand motion (2007, background): “See Harris and Wolpert (1998), Todorov and Jordan (2002) and Osborne et al. (2005).”
Motor Adaptation as a Process of Reoptimization (2008, unknown): “Noise is signal dependent with an SD that grows with the size of the motor commands (Harris and Wolpert, 1998; Jones et al., 2002; van Beers et al., 2004).”
Bayesian decision theory in sensorimotor control. (2006, unknown): “More recently evidence has been presented that the precision of the movement, rather than smoothness, defines the cost function [ 37 ].”
Computational approaches to motor control. (2001, background): “The minimum variance model of motor planning assumes that humans plan movements that minimize the transmission of error, caused by biological noise, as reflected in the variance of the end-point position [46].”
The Bayesian brain: the role of uncertainty in neural coding and computation. (2004, background): “ments, given the signal-dependent of motor noise [ 8 ], and when costs and gains for different are independently subjects adjust their aim points”