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Author(s): Bullock, D. | Grossberg, S. |
Year: 1991
Citation: Human Movement Science, 10, 3-53
Abstract: This article describes two neural network modules that form part of an emerging theory of how adaptive control of goal-directed sensory-motor skills is achieved by humans and other animals. The Vector-1ntegration-To-Endpoint (VITE) model suggests how synchronous multi-joint trajec¬tories are generated and performed at variable speeds. The Factorization-of-LEngth-and-TEnsion (FLETE) model suggests how outflow movement commands from a VITE model may be performed at variable force levels without a loss of positional accuracy. The invariance of positional control under speed and force rescaling sheds new light upon a familiar strategy of motor skill development: skill learning begins with performance at low speed and low limb compliance and proceeds to higher speeds and compliances. The VITE model helps to explain many neural and behavioral data about trajectory formation, including data about neural coding within the posterior parietal cortex, motor cortex, and globus pallidus, and behavioral properties such as Wood worth's Law, Fitts' Law, peak acceleration as a function of movement amplitude and duration, isotonic arm movement properties before and after arm-deafferentation, central error correction properties of isometric contractions, motor priming without overt action, velocity amplification during target switching, velocity profile invariance across different movement distances, changes in velocity profile asymmetry across different movement durations, staggered onset times for controlling linear trajectories with synchronous offset times, changes in the ratio of maximum to average velocity during discrete versus serial movements, and shared properties of [...] Bullock, D. and S. Grossberg, 1991. Adaptive neural networks for control of movement trajectories invariant under speed and force rescaling (Target article). Human Movement Science 10, 3-53.
Topics:
Biological Learning,
Models:
Other,
