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The Journal of Neurophysiology Vol. 87 No. 5 May 2002, pp. 2434-2440
Copyright ©2002 by the American Physiological Society
Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, University College London, London WC1N 3BG, United Kingdom
Hamilton, Antonia F. de C. and
Daniel M. Wolpert.
Controlling the Statistics of Action: Obstacle Avoidance. J. Neurophysiol. 87: 2434-2440, 2002. Task
optimization in the presence of signal-dependent noise (TOPS) has been
proposed as a general framework for planning goal-directed movements.
Within this framework, the motor command is assumed to be corrupted by
signal-dependent noise, which leads to a distribution of possible
movements. A task can then be equated with optimizing some function of
the statistics of this distribution. We found the optimal trajectory
for obstacle avoidance by minimizing the mean-squared error at the end
of the movement while keeping the probability of collision with the
obstacle below a fixed limit. The optimal paths accurately predicted
the empirical trajectories. This demonstrates that controlling the
statistics of movements in the presence of signal-dependent noise may
be a fundamental and unifying principle of goal-directed movements.
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