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MODELING MOTOR MEMORY TO ENHANCE MULTIPLE TASK LEARNING
by
Jeong-Yoon Lee
________________________________________________________________________
A Dissertation Presented to the
FACULTY OF THE USC GRADUATESCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
May 2011
Copyright 2011 Jeong-Yoon Lee
Object Description
| Title | Modeling motor memory to enhance multiple task learning |
| Author | Lee, Jeong-Yoon |
| Author email | jeongyol@usc.edu; ethielee@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2011-03-17 |
| Date submitted | 2011 |
| Restricted until | Unrestricted |
| Date published | 2011-04-29 |
| Advisor (committee chair) | Schweighofer, Nicolas |
| Advisor (committee member) |
Schaal, Stefan Sanger, Terence Winstein, Carolee |
| Abstract | Although recent computational modeling research has advanced our understanding of motor learning, previous studies focused on single-task motor learning and did not account for multiple task motor learning which is the norm in sports, music, professional skill development, and neuro-rehabilitation. In this dissertation, we took the combined approach of theoretical analysis, computational modeling, and behavioral experiments to understand the mechanisms of multi-task motor learning, and based on this understanding, to optimize multi-task motor learning. We first suggested a parallel architecture of motor memory in multi-task motor learning: By examining systematically how possible architectures account for experimental results, we showed that the human brain engages a fast-learning-fast-forgetting learning process in parallel with multiple slow-learning-slow-forgetting learning processes. We then investigated how practice schedules and the integrity of short-term memory affect long-term learning: Based on our model, we found that for healthy individuals with intact short-term memory, random practices schedule lead to better long term learning than blocked practice schedules. However for individuals post-stroke with deficits in short-term memory, the effect of practice schedules in long-term learning was mitigated. We finally derived optimal schedules for multi-task motor learning by applying optimal control theory to our computational model of multi-task motor learning. We found that alternating schedules are optimal only if tasks have equal difficulties. If differences in difficulties between tasks increase, our algorithms provide optimal schedules that have the potential to enhance long-term learning in multi-task motor learning. |
| Keyword | computational model; motor learning; stroke rehabilitation; optimal schedule |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m3807 |
| Rights | Lee, Jeong-Yoon |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Lee-4057 |
| Archival file | uscthesesreloadpub_Volume14/etd-Lee-4057.pdf |
Description
| Title | Page 1 |
| Full text | MODELING MOTOR MEMORY TO ENHANCE MULTIPLE TASK LEARNING by Jeong-Yoon Lee ________________________________________________________________________ A Dissertation Presented to the FACULTY OF THE USC GRADUATESCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) May 2011 Copyright 2011 Jeong-Yoon Lee |
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