Models of generalization in motor control

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.

Bibliographic Details
Main Author: Matsuoka, Yoky, 1971-
Other Authors: Emilio Bizzi and Rodney Brooks.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2005
Subjects:
Online Access:http://hdl.handle.net/1721.1/9634
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author Matsuoka, Yoky, 1971-
author2 Emilio Bizzi and Rodney Brooks.
author_facet Emilio Bizzi and Rodney Brooks.
Matsuoka, Yoky, 1971-
author_sort Matsuoka, Yoky, 1971-
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description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.
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spelling mit-1721.1/96342020-07-14T22:06:26Z Models of generalization in motor control Matsuoka, Yoky, 1971- Emilio Bizzi and Rodney Brooks. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998. Includes bibliographical references (p. 207-215). Motor learning for humans is based on the capacity of the central nervous system (CNS) to perform computation and build an internal model for a task. This thesis investigates the CNS's ability to generalize a learned motor skill throughout neighboring spatial locations, its ability to divide the spatial general­ization with variation of context, and proposes models of how these generalizations might be implemented. The investigation involved human psychophysics and simulations. The experi­mental paradigm was to study human neuromuscular adaptation to viscous force perturbation. When external perturbations were applied to the hand during a reach­ing task, the movement became distorted. This distortion motivated the CNS to produce counterbalancing forces, which resulted in the modification of the internal model for the task. Experimental results indicated that the introduction of interfering perturbations near the trained location disturbed the learned skill. In addition, if the same move­ment was perturbed in two opposite directions in sequence, neither of the forces are learned. Conversely, the adaptation to two opposite forces was possible within the same space when the forces were applied to two contextually distinguished movements. This was possible only when these movements were interleaved fairly regularly. During the adaptation to a difficult task, such as contextual distinction in the same spatial location, humans often used other strategies to avoid learning the actual paradigm. These strategies allowed subjects to perform the task -- without changing their internal models appropriately, and thus this was also investigated as a part of the learning process. Finally, a multiple function model was constructed which allowed multiple contex­tually dependent functions to co-exist within one state space. The sensory feedback affected all functions, however, only one function was active to output a motor com­mand. This model supported the experimental data presented. The results of the psychophysical experiments as well as an explanation of the simulations and models that were developed will be presented in this thesis. by Yoky Matsuoka. Ph.D. 2005-08-19T19:05:09Z 2005-08-19T19:05:09Z 1998 1998 Thesis http://hdl.handle.net/1721.1/9634 42345205 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 215 p. 13991557 bytes 13991315 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology
spellingShingle Electrical Engineering and Computer Science
Matsuoka, Yoky, 1971-
Models of generalization in motor control
title Models of generalization in motor control
title_full Models of generalization in motor control
title_fullStr Models of generalization in motor control
title_full_unstemmed Models of generalization in motor control
title_short Models of generalization in motor control
title_sort models of generalization in motor control
topic Electrical Engineering and Computer Science
url http://hdl.handle.net/1721.1/9634
work_keys_str_mv AT matsuokayoky1971 modelsofgeneralizationinmotorcontrol