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dc.contributor.advisor吳恩賜zh_TW
dc.contributor.advisorJoshua Oon Soo Gohen
dc.contributor.author方一欣zh_TW
dc.contributor.authorYi-Xin Miranda Fangen
dc.date.accessioned2023-10-03T17:50:38Z-
dc.date.available2023-11-10-
dc.date.copyright2023-10-03-
dc.date.issued2023-
dc.date.submitted2023-06-26-
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90837-
dc.description.abstract認知訓練被視為具有延緩年齡相關認知衰退的潛力。然而,傳統的認知訓練主要著重在引導受試者重複練習低階認知歷程,例如注意力、記憶或抑制任務。在此臨床註冊試驗(編號 NCT05341232)中,我們欲探究針對高階認知歷 程「主動推理」對高齡者認知健康的影響。本研究透過為期12週的樂高機器人 程式課(LRPI),介入主動推理,此歷程涉及整合多元訊息來執行觀察、預測和行動的試誤過程,其中更需要協調統合各個低階認知過程。我們假設針對主動推理介入,將可能導致神經的整體活化和未特別訓練的認知功能改善。本研究中,參與者被隨機分配到實驗組或主動對照組,並使用視覺規則推理任務 (VRIT)作為功能性磁振造影的任務,以調查在推理過程中的行為和大腦活動變化。同時,前後測尚包含神經心理學測驗,用以評估實驗中未特別訓練的認知功能。迄今為止,每組8名參與者已完成介入,初步數據顯示,實驗組的訓練可能在老年人的推理能力產生潛在的效果,並且對行為和神經層面均有改善。然而,由於樣本數較小,需要進一步的研究以充分評估訓練效果。zh_TW
dc.description.abstractCognitive training has been proposed as a promising approach to address age- related cognitive decline. However, past studies have primarily focused on repetitive engagement of lower-level cognitive processes, such as attention, memory, or inhibitory tasks. To address this gap, we conducted a 12-week Lego Robot Programming Intervention (LRPI) targeting higher-level cognitive processes, specifically active inference, in a registered clinical trial (no. NCT05341232). Active inference involves integrating multiple sources of information to perform a trial-and- error process of observation, prediction, and action, and requires the coordination of lower-level cognitive processes. We hypothesized that targeting active inference would lead to general neural activation and improvements in untrained cognitive functions. Participants were randomly assigned to the experimental or active control group, and the Visual Rule Inference Task (VRIT) was used during fMRI scanning to investigate changes in brain activity and behaviors during inference processing. Pre- and post- intervention neuropsychological tests were also conducted to assess improvement in untargeted cognitive functions. To date, 8 participants in each group have completed the intervention, and preliminary data suggests that our experimental training may have promising effect on inference processing in older adults at both behavioral and neural levels. However, due to the small sample size, further research is needed to adequately evaluate the training effects.en
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dc.description.tableofcontents誌謝 i
中文摘要 ii
Abstract iii
Contents v
List of Figures vi
List of Tables vii
Chapter 1 Introduction 1
Chapter 2 Method 9
Participants 9
Experimental Procedure 10
Lego Robot Programming(LRP) Intervention Protocol 11
Outcome Measurement 13
Data Analysis 17
Chapter 3 Results 22
Descriptive and demographic data 22
VRIT behavioral results 22
VRIT imaging results 27
NPT results and others 29
Chapter 4 Discussion 31
VRIT behavioral results 31
VRIT imaging results 33
NPT results and others 37
Limitations 38
Future Analysis Direction 40
Conclusion 41
References 80
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dc.language.isoen-
dc.subject主動推理zh_TW
dc.subject功能性磁振造影zh_TW
dc.subject樂高機器人程式zh_TW
dc.subject認知訓練zh_TW
dc.subject高齡者zh_TW
dc.subjectfMRIen
dc.subjectolder adultsen
dc.subjectcognitive trainingen
dc.subjectLEGO robot programmingen
dc.subjectactive inferenceen
dc.title以樂高程式設計介入高齡者認知健康zh_TW
dc.titleA Lego Robot Programming Intervention for Enhancing Older Adults Cognitive Healthen
dc.typeThesis-
dc.date.schoolyear111-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee毛慧芬;余家斌;吳建德zh_TW
dc.contributor.oralexamcommitteeHui-fen Mao;Chia-Pin Yu;Chien-Te Wuen
dc.subject.keyword高齡者,認知訓練,樂高機器人程式,主動推理,功能性磁振造影,zh_TW
dc.subject.keywordolder adults,cognitive training,LEGO robot programming,active inference,fMRI,en
dc.relation.page86-
dc.identifier.doi10.6342/NTU202301153-
dc.rights.note未授權-
dc.date.accepted2023-06-27-
dc.contributor.author-college醫學院-
dc.contributor.author-dept腦與心智科學研究所-
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