An error-tuned model for sensorimotor learning.

ABSTRACT: Current models of sensorimotor control posit that motor commands are generated by combining multiple modules which may consist of internal models, motor primitives or motor synergies. The mechanisms which select modules based on task requirements and modify their output during learning are therefore critical to our understanding of sensorimotor control. Here we develop a novel modular architecture for multi-dimensional tasks in which a set of fixed primitives are each able to compensate for errors in a single direction in the task space. The contribution of the primitives to the motor output is determined by both top-down contextual information and bottom-up error information. We implement this model for a task in which subjects learn to manipulate a dynamic object whose orientation can va

SUBMITTER: Ingram JN 

PROVIDER: S-EPMC5749863 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature