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Changing Movement Patterns Using Rei...

Lin, Tzu-Hsiang.

Changing Movement Patterns Using Reinforcement Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Changing Movement Patterns Using Reinforcement Learning.
作者:	Lin, Tzu-Hsiang.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2020
面頁冊數:	99 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
附註:	Advisor: Ranganathan, Rajiv.
Contained By:	Dissertations Abstracts International82-02B.
標題:	Kinesiology.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28086447
ISBN:	9798664735048

Changing Movement Patterns Using Reinforcement Learning.
Lin, Tzu-Hsiang.

Changing Movement Patterns Using Reinforcement Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 99 p.

Source: Dissertations Abstracts International, Volume: 82-02, Section: B.

Thesis (Ph.D.)--Michigan State University, 2020.

This item must not be sold to any third party vendors.

Humans interact with the world by generating movements, which make it important to understand the process of motor learning. There are two aspects of motor learning: (1) an improvement in task performance (e.g., learning to throw farther), and (2) a change in the movement pattern (e.g., learning to throw with an improved coordination or technique even if there is no change in task performance). Most studies on motor learning focus on the first aspect of task performance; however, the second aspect of movement pattern is also important and ubiquitous in our daily life - for example, we learn a better movement pattern to carry heavy objects to prevent injuries or the patients re-learn to perform movements in the rehab setting. In this dissertation, I designed a learning protocol that provided reinforcement feedback to guide participants to learn alternative movement patterns to perform the same task. Reinforcement feedback provides participants with a signal to start exploring different movement patterns but does not provide direct information about the desired movement pattern. Therefore the key question of this dissertation was to address the issue of how to schedule the reinforcement feedback to shift participants toward an alternative movement pattern in tasks requiring coordination of multiple body segments. In experiment one, I tested how providing ‘online’ reinforcement feedback (i.e. feedback provided during the movement) could shift the participants to alternative movement patterns. In experiment two, I tested how providing ‘terminal’ reinforcement feedback (i.e. feedback provided at the end of the movement) could shift participants toward alternative movement patterns, and if an adaptive method that adjusts reinforcement based on prior performance had better learning outcomes. In summary, I found: (1) reinforcement feedback can be used to change movement patterns in task requiring coordination of multiple body segments, although it is less successful when compared to its use in simpler tasks (2) online reinforcement feedback resulted in quick changes toward the desired movement pattern, and the amount of practice was the primary factor that determined retention, and (3) terminal reinforcement feedback resulted in less change toward the desired movement pattern, and an adaptive algorithm was needed to achieve better learning outcomes. These results contribute to the fields of motor learning and computational motor neuroscience to understand how the central nervous system uses feedback to change movement patterns, and can be applied to the fields of skill acquisition or motor rehabilitation to help people learn motor skills.

ISBN: 9798664735048Subjects--Topical Terms:

203232
Kinesiology.
Subjects--Index Terms:

Motor learning

Changing Movement Patterns Using Reinforcement Learning.
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520 $a Humans interact with the world by generating movements, which make it important to understand the process of motor learning. There are two aspects of motor learning: (1) an improvement in task performance (e.g., learning to throw farther), and (2) a change in the movement pattern (e.g., learning to throw with an improved coordination or technique even if there is no change in task performance). Most studies on motor learning focus on the first aspect of task performance; however, the second aspect of movement pattern is also important and ubiquitous in our daily life - for example, we learn a better movement pattern to carry heavy objects to prevent injuries or the patients re-learn to perform movements in the rehab setting. In this dissertation, I designed a learning protocol that provided reinforcement feedback to guide participants to learn alternative movement patterns to perform the same task. Reinforcement feedback provides participants with a signal to start exploring different movement patterns but does not provide direct information about the desired movement pattern. Therefore the key question of this dissertation was to address the issue of how to schedule the reinforcement feedback to shift participants toward an alternative movement pattern in tasks requiring coordination of multiple body segments. In experiment one, I tested how providing ‘online’ reinforcement feedback (i.e. feedback provided during the movement) could shift the participants to alternative movement patterns. In experiment two, I tested how providing ‘terminal’ reinforcement feedback (i.e. feedback provided at the end of the movement) could shift participants toward alternative movement patterns, and if an adaptive method that adjusts reinforcement based on prior performance had better learning outcomes. In summary, I found: (1) reinforcement feedback can be used to change movement patterns in task requiring coordination of multiple body segments, although it is less successful when compared to its use in simpler tasks (2) online reinforcement feedback resulted in quick changes toward the desired movement pattern, and the amount of practice was the primary factor that determined retention, and (3) terminal reinforcement feedback resulted in less change toward the desired movement pattern, and an adaptive algorithm was needed to achieve better learning outcomes. These results contribute to the fields of motor learning and computational motor neuroscience to understand how the central nervous system uses feedback to change movement patterns, and can be applied to the fields of skill acquisition or motor rehabilitation to help people learn motor skills.
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