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Building Novel Action-Outcome Mappings for Sequential Motor Skills
Building Novel Action-Outcome Mappings for Sequential Motor Skills
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152937
- ISBN
- 9798346759720
- DDC
- 152
- 서명/저자
- Building Novel Action-Outcome Mappings for Sequential Motor Skills
- 발행사항
- [Sl] : Princeton University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 149 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
- 주기사항
- Advisor: Taylor, Jordan A.;Daw, Nathaniel D.
- 학위논문주기
- Thesis (Ph.D.)--Princeton University, 2024.
- 초록/해제
- 요약Understanding how humans acquire novel motor skills is a central topic in motor learning research. However, much of the work in this field has focused on adaptation experiments, leaving other key aspects of de novo skill acquisition less explored. For many de novo skills, individuals must learn new associations between discrete actions and arbitrary outcomes. This is evident in digital devices like video games, where pressing buttons on a controller can make a character jump or run. These action-outcome mappings are fundamental to the formation of the new skill. Therefore, understanding how they are learned and consolidated is essential for advancing our knowledge of motor skill acquisition and its application to various domains, from gaming to real-world tool use.In Chapter 2, using a task of grid navigation, I study how these action-outcome mappings are acquired and examine the role of training variability in the formation of generalizable mappings. Crucially, when a novel mapping is being learned, it often occurs within the context of sequential decision-making, allowing the interaction of motor learning and planning. In Chapter 3, I investigate this interaction with the aim of bridging the gap between motor sequence learning and planning research. Finally, in Chapter 4, I study the effectiveness of external contextual cues in the learning of multiple mappings, which have proven unsuccessful in standard motor adaptation experiments. The behavioral results from each chapter of this dissertation are complemented by computational models that integrate algorithms from reinforcement learning, tree search, and Bayesian learning. These models aim to provide insights into the cognitive processes underlying participants' performance.
- 일반주제명
- Experimental psychology
- 일반주제명
- Psychobiology
- 일반주제명
- Bioinformatics
- 일반주제명
- Quantitative psychology
- 일반주제명
- Cognitive psychology
- 키워드
- Motor learning
- 키워드
- Motor skills
- 키워드
- Novel mapping
- 기타저자
- Princeton University Psychology
- 기본자료저록
- Dissertations Abstracts International. 86-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152937
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■007cr#unu||||||||
■020 ▼a9798346759720
■035 ▼a(MiAaPQ)AAI31563831
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a152
■1001 ▼aVelazquez Vargas, Carlos Alan.▼0(orcid)0000-0001-7010-1219
■24510▼aBuilding Novel Action-Outcome Mappings for Sequential Motor Skills
■260 ▼a[Sl]▼bPrinceton University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a149 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-06, Section: B.
■500 ▼aAdvisor: Taylor, Jordan A.;Daw, Nathaniel D.
■5021 ▼aThesis (Ph.D.)--Princeton University, 2024.
■520 ▼aUnderstanding how humans acquire novel motor skills is a central topic in motor learning research. However, much of the work in this field has focused on adaptation experiments, leaving other key aspects of de novo skill acquisition less explored. For many de novo skills, individuals must learn new associations between discrete actions and arbitrary outcomes. This is evident in digital devices like video games, where pressing buttons on a controller can make a character jump or run. These action-outcome mappings are fundamental to the formation of the new skill. Therefore, understanding how they are learned and consolidated is essential for advancing our knowledge of motor skill acquisition and its application to various domains, from gaming to real-world tool use.In Chapter 2, using a task of grid navigation, I study how these action-outcome mappings are acquired and examine the role of training variability in the formation of generalizable mappings. Crucially, when a novel mapping is being learned, it often occurs within the context of sequential decision-making, allowing the interaction of motor learning and planning. In Chapter 3, I investigate this interaction with the aim of bridging the gap between motor sequence learning and planning research. Finally, in Chapter 4, I study the effectiveness of external contextual cues in the learning of multiple mappings, which have proven unsuccessful in standard motor adaptation experiments. The behavioral results from each chapter of this dissertation are complemented by computational models that integrate algorithms from reinforcement learning, tree search, and Bayesian learning. These models aim to provide insights into the cognitive processes underlying participants' performance.
■590 ▼aSchool code: 0181.
■650 4▼aExperimental psychology
■650 4▼aPsychobiology
■650 4▼aBioinformatics
■650 4▼aQuantitative psychology
■650 4▼aCognitive psychology
■653 ▼aMotor learning
■653 ▼aMotor skills
■653 ▼aAdaptation experiments
■653 ▼aNovel mapping
■653 ▼aMotor sequence learning
■690 ▼a0623
■690 ▼a0632
■690 ▼a0349
■690 ▼a0633
■690 ▼a0715
■71020▼aPrinceton University▼bPsychology.
■7730 ▼tDissertations Abstracts International▼g86-06B.
■790 ▼a0181
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164234▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
