title: Controlling the statistics of action: obstacle avoidance.
abstract: 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.
authors.affiliation: Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, University College London, London WC1N 3BG, United Kingdom. a.hamilton@ion.ucl.ac.uk
authors.forename: Antonia F de C
authors.initials: AF
authors.lastname: Hamilton
authors.forename: Daniel M
authors.initials: DM
authors.lastname: Wolpert
mesh_terms: D001362:Avoidance Learning / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009068:Movement / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*