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The Role of Variability in Learning Generalization: A Computational Modeling Approach
The Role of Variability in Learning Generalization: A Computational Modeling Approach
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153021
- ISBN
- 9798342710312
- DDC
- 153
- 서명/저자
- The Role of Variability in Learning Generalization: A Computational Modeling Approach
- 발행사항
- [Sl] : Indiana University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 123 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
- 주기사항
- Advisor: Goldstone, Robert L.
- 학위논문주기
- Thesis (Ph.D.)--Indiana University, 2024.
- 초록/해제
- 요약The impact of training variability on generalization has been a long-standing topic in the study of human learning, with conflicting evidence about its potential benefits. This dissertation addresses these ambiguities by examining the effects of varied versus constant training in visuomotor skill learning through a combination of experimental and computational modeling approaches. Across two projects, we systematically compare varied training (multiple items) to constant training (single item) in a projectile-throwing task. Empirical findings reveal both positive and negative impacts of variability, highlighting the complex interplay between training conditions and generalization performance. To provide a theoretical account of these findings, this dissertation employs both instance-based and connectionist computational modeling approaches. The instance-based modeling approach introduced in Project 1 provides a theoretically justifiable method of quantifying and controlling for similarity between training and testing conditions, while also demonstrating that varied training may induce broader generalization in the similarity function relating training and test items. In Project 2, the Extrapolation-Association Model (EXAM) provided the best account of the testing data across all experiments, capturing the constant groups' ability to extrapolate to novel regions despite limited training experience, while also revealing potential detriments of varied training for simple extrapolation tasks. These results challenge simplistic notions about the universality of variability benefits in training and emphasize the need for tailored approaches that consider both the structure of the task environment and the prior knowledge of the learners.
- 일반주제명
- Cognitive psychology
- 일반주제명
- Neurosciences
- 일반주제명
- Psychology
- 일반주제명
- Experimental psychology
- 키워드
- Generalization
- 키워드
- Learning
- 기타저자
- Indiana University Psychological & Brain Sciences
- 기본자료저록
- Dissertations Abstracts International. 86-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■040 ▼aMiAaPQ▼cMiAaPQ
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■1001 ▼aGorman, Thomas E.▼0(orcid)0000-0001-5366-5442
■24510▼aThe Role of Variability in Learning Generalization: A Computational Modeling Approach
■260 ▼a[Sl]▼bIndiana University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a123 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: B.
■500 ▼aAdvisor: Goldstone, Robert L.
■5021 ▼aThesis (Ph.D.)--Indiana University, 2024.
■520 ▼aThe impact of training variability on generalization has been a long-standing topic in the study of human learning, with conflicting evidence about its potential benefits. This dissertation addresses these ambiguities by examining the effects of varied versus constant training in visuomotor skill learning through a combination of experimental and computational modeling approaches. Across two projects, we systematically compare varied training (multiple items) to constant training (single item) in a projectile-throwing task. Empirical findings reveal both positive and negative impacts of variability, highlighting the complex interplay between training conditions and generalization performance. To provide a theoretical account of these findings, this dissertation employs both instance-based and connectionist computational modeling approaches. The instance-based modeling approach introduced in Project 1 provides a theoretically justifiable method of quantifying and controlling for similarity between training and testing conditions, while also demonstrating that varied training may induce broader generalization in the similarity function relating training and test items. In Project 2, the Extrapolation-Association Model (EXAM) provided the best account of the testing data across all experiments, capturing the constant groups' ability to extrapolate to novel regions despite limited training experience, while also revealing potential detriments of varied training for simple extrapolation tasks. These results challenge simplistic notions about the universality of variability benefits in training and emphasize the need for tailored approaches that consider both the structure of the task environment and the prior knowledge of the learners.
■590 ▼aSchool code: 0093.
■650 4▼aCognitive psychology
■650 4▼aNeurosciences
■650 4▼aPsychology
■650 4▼aExperimental psychology
■653 ▼aFunction learning
■653 ▼aGeneralization
■653 ▼aLearning
■653 ▼aSimilarity models
■653 ▼aTraining variability
■690 ▼a0633
■690 ▼a0621
■690 ▼a0317
■690 ▼a0623
■71020▼aIndiana University▼bPsychological & Brain Sciences.
■7730 ▼tDissertations Abstracts International▼g86-04B.
■790 ▼a0093
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164599▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
