Title :
Decomposition of Internal Models in Motor Learning under Mixed Dynamic Environments
Author :
Ito, Koji ; Imai, Tsutomu ; Tomi, Naoki ; Kondo, Toshiyuki
Author_Institution :
Dept. of Computational Intelligence & Syst. Sci., Tokyo Inst. of Technol., Yokohama
Abstract :
In daily life, we utilize many kinds of tools to achieve various tasks. The tool connects the body with the environment. In order to realize the task quickly, smoothly or efficiently, it is required to adjust the kinematic and dynamic relations among the body, tool and environment according to the task. Recent studies have shown that humans can acquire a neural representation of the relation between motor command and movement, i.e. learn an internal model of the environment dynamics. Then, we can compensate for the mechanical perturbation in a feedforward manner. The present paper discusses whether humans can identify one side of dynamics from the mixed environment dynamics in the case where humans have experienced either of them
Keywords :
biomechanics; neurophysiology; dynamic relations; internal models decomposition; kinematic relations; mixed dynamic environments; motor command; motor learning; movement; neural representation; Adaptation model; Agriculture; Biological system modeling; Computational intelligence; Context modeling; Humans; Indium tin oxide; Kinematics; Predictive models; Switches; dynamic environment; force field; internal model; motor adaptation; reaching motion;
Conference_Titel :
SICE-ICASE, 2006. International Joint Conference
Conference_Location :
Busan
Print_ISBN :
89-950038-4-7
Electronic_ISBN :
89-950038-5-5
DOI :
10.1109/SICE.2006.315223
