請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90837完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳恩賜 | zh_TW |
| dc.contributor.advisor | Joshua Oon Soo Goh | en |
| dc.contributor.author | 方一欣 | zh_TW |
| dc.contributor.author | Yi-Xin Miranda Fang | en |
| dc.date.accessioned | 2023-10-03T17:50:38Z | - |
| dc.date.available | 2023-11-10 | - |
| dc.date.copyright | 2023-10-03 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-06-26 | - |
| dc.identifier.citation | Aminoff, E. M., Kveraga, K., & Bar, M. (2013). The role of the parahippocampal cortex in cognition. Trends in Cognitive Sciences, 17(8), 379–390. https://doi.org/10.1016/j.tics.2013.06.009
Andrews, J. S., Desai, U., Kirson, N. Y., Enloe, C. J., Ristovska, L., King, S., Birnbaum, H. G., Fleisher, A. S., Ye, W., & Kahle‐Wrobleski, K. (2017). Functional limitations and health care resource utilization for individuals with cognitive impairment without dementia: Findings from a United States population‐based survey. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 6(1), 65–74. https://doi.org/10.1016/j.dadm.2016.11.005 Anguera, J. A., Schachtner, J. N., Simon, A. J., Volponi, J., Javed, S., Gallen, C. L., & Gazzaley, A. (2021). Long-term maintenance of multitasking abilities following video game training in older adults. Neurobiology of Aging, 103, 22–30. https://doi.org/10.1016/j.neurobiolaging.2021.02.023 Au, J., Gibson, B. C., Bunarjo, K., Buschkuehl, M., & Jaeggi, S. M. (2020). Quantifying the Difference Between Active and Passive Control Groups in Cognitive Interventions Using Two Meta-analytical Approaches. Journal of Cognitive Enhancement, 4(2), 192–210. https://doi.org/10.1007/s41465-020-00164-6 Averbeck, B., & O’Doherty, J. P. (2022). Reinforcement-learning in fronto-striatal circuits. Neuropsychopharmacology, 47(1), Article 1. https://doi.org/10.1038/s41386-021-01108-0 Ball, K., Berch, D. B., Helmers, K. F., Jobe, J. B., Leveck, M. D., Marsiske, M., Morris, J. N., Rebok, G. W., Smith, D. M., Tennstedt, S. L., Unverzagt, F. W., & Willis, S. L. (2002). Effects of Cognitive Training Interventions With Older Adults. JAMA : The Journal of the American Medical Association, 288(18), 2271–2281. BioImage Suite Web. (2020, August 25). MNI2TAL. MNI2TAL. https://bioimagesuiteweb.github.io/webapp/mni2tal.html Bonnechère, B., Klass, M., Langley, C., & Sahakian, B. J. (2021). Brain training using cognitive apps can improve cognitive performance and processing speed in older adults. Scientific Reports, 11, 12313. https://doi.org/10.1038/s41598-021-91867-z Bottemanne, H., & Friston, K. J. (2021). An active inference account of protective behaviours during the COVID-19 pandemic. Cognitive, Affective & Behavioral Neuroscience, 21(6), 1117–1129. https://doi.org/10.3758/s13415-021-00947-0 Brehmer, Y., Kalpouzos, G., Wenger, E., & Lövdén, M. (2014). Plasticity of brain and cognition in older adults. Psychological Research, 78(6), 790–802. https://doi.org/10.1007/s00426-014-0587-z Buitenweg, J. I. V., van de Ven, R. M., Prinssen, S., Murre, J. M. J., & Ridderinkhof, K. R. (2017). Cognitive Flexibility Training: A Large-Scale Multimodal Adaptive Active-Control Intervention Study in Healthy Older Adults. Frontiers in Human Neuroscience, 11, 529. https://doi.org/10.3389/fnhum.2017.00529 Chen, A. G., Benrimoh, D., Parr, T., & Friston, K. J. (2020). A Bayesian Account of Generalist and Specialist Formation Under the Active Inference Framework. Frontiers in Artificial Intelligence, 3, 69. https://doi.org/10.3389/frai.2020.00069 Chih-Yu Chao, Wan-Rue Lin, & Joshua Oon Soo Goh. (2021). Cognitive Training of Active Inference in Older Adults [Master’s Thesis, National Taiwan University]. In 臺灣大學腦與心智科學研究所學位論文 (Issue 2021年). https://doi.org/10.6342/NTU202100463 Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204. https://doi.org/10.1017/S0140525X12000477 Couch, E., Lawrence, V., Co, M., & Prina, M. (2020). Outcomes tested in non-pharmacological interventions in mild cognitive impairment and mild dementia: A scoping review. BMJ Open, 10(4), e035980. https://doi.org/10.1136/bmjopen-2019-035980 David Wechsler. (1997a). Wechsler Adult Intelligence Scale – 3rd Edition (WAIS-III). Pearson. David Wechsler. (1997b). Wechsler Memory Scale – 3rd Edition (WMS-III). Pearson. Deary, I. J., Corley, J., Gow, A. J., Harris, S. E., Houlihan, L. M., Marioni, R. E., Penke, L., Rafnsson, S. B., & Starr, J. M. (2009). Age-associated cognitive decline. British Medical Bulletin, 92(1), 135–152. https://doi.org/10.1093/bmb/ldp033 Deschamps, I., Baum, S. R., & Gracco, V. L. (2014). On the role of the supramarginal gyrus in phonological processing and verbal working memory: Evidence from rTMS studies. Neuropsychologia, 53, 39–46. https://doi.org/10.1016/j.neuropsychologia.2013.10.015 Fellman, D., Salmi, J., Ritakallio, L., Ellfolk, U., Rinne, J. O., & Laine, M. (2020). Training working memory updating in Parkinson’s disease: A randomised controlled trial. Neuropsychological Rehabilitation, 30(4), 673–708. https://doi.org/10.1080/09602011.2018.1489860 Friston, K., Da Costa, L., Hafner, D., Hesp, C., & Parr, T. (2021). Sophisticated Inference. Neural Computation, 33(3), 713–763. https://doi.org/10.1162/neco_a_01351 Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., O’Doherty, J., & Pezzulo, G. (2016). Active inference and learning. Neuroscience & Biobehavioral Reviews, 68, 862–879. https://doi.org/10.1016/j.neubiorev.2016.06.022 Graf, C. (2008). The Lawton instrumental activities of daily living scale. The American Journal of Nursing, 108(4), 52–62; quiz 62–63. https://doi.org/10.1097/01.NAJ.0000314810.46029.74 Hartwigsen, G., & Siebner, H. R. (2015). Joint Contribution of Left Dorsal Premotor Cortex and Supramarginal Gyrus to Rapid Action Reprogramming. Brain Stimulation, 8(5), 945–952. https://doi.org/10.1016/j.brs.2015.04.011 Hayes, J., & Stewart, I. (2016). Comparing the effects of derived relational training and computer coding on intellectual potential in school-age children. British Journal of Educational Psychology, 86(3), 397–411. https://doi.org/10.1111/bjep.12114 Heinzel, S., Lorenz, R. C., Pelz, P., Heinz, A., Walter, H., Kathmann, N., Rapp, M. A., & Stelzel, C. (2016). Neural correlates of training and transfer effects in working memory in older adults. NeuroImage, 134, 236–249. https://doi.org/10.1016/j.neuroimage.2016.03.068 Herlin, B., Navarro, V., & Dupont, S. (2021). The temporal pole: From anatomy to function—A literature appraisal. Journal of Chemical Neuroanatomy, 113, 101925. https://doi.org/10.1016/j.jchemneu.2021.101925 Horne, K. S., Filmer, H. L., Nott, Z. E., Hawi, Z., Pugsley, K., Mattingley, J. B., & Dux, P. E. (2021). Evidence against benefits from cognitive training and transcranial direct current stimulation in healthy older adults. Nature Human Behaviour, 5(1), 146–158. https://doi.org/10.1038/s41562-020-00979-5 Hultsch, D. F., Hertzog, C., Small, B. J., & Dixon, R. A. (1999). Use it or lose it: Engaged lifestyle as a buffer of cognitive decline in aging? Psychology and Aging, 14(2), 245–263. https://doi.org/10.1037/0882-7974.14.2.245 Humphreys, G. F., Lambon Ralph, M. A., & Simons, J. S. (2021). A Unifying Account of Angular Gyrus Contributions to Episodic and Semantic Cognition. Trends in Neurosciences, 44(6), 452–463. https://doi.org/10.1016/j.tins.2021.01.006 Kelly, M. E., Loughrey, D., Lawlor, B. A., Robertson, I. H., Walsh, C., & Brennan, S. (2014). The impact of cognitive training and mental stimulation on cognitive and everyday functioning of healthy older adults: A systematic review and meta-analysis. Ageing Research Reviews, 15, 28–43. https://doi.org/10.1016/j.arr.2014.02.004 Keramati, M., Dezfouli, A., & Piray, P. (2011). Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes. PLoS Computational Biology, 7(5), e1002055. https://doi.org/10.1371/journal.pcbi.1002055 Küper, K., Gajewski, P. D., Frieg, C., & Falkenstein, M. (2017). A Randomized Controlled ERP Study on the Effects of Multi-Domain Cognitive Training and Task Difficulty on Task Switching Performance in Older Adults. Frontiers in Human Neuroscience, 11, 184. https://doi.org/10.3389/fnhum.2017.00184 Lee, A. C. H., Robbins, T. W., Graham, K. S., & Owen, A. M. (2002). “Pray or Prey?” Dissociation of Semantic Memory Retrieval from Episodic Memory Processes Using Positron Emission Tomography and a Novel Homophone Task. NeuroImage, 16(3, Part A), 724–735. https://doi.org/10.1006/nimg.2002.1101 Lee, B., Cai, W., Young, C. B., Yuan, R., Ryman, S., Kim, J., Santini, V., Henderson, V. W., Poston, K. L., & Menon, V. (2022). Latent brain state dynamics and cognitive flexibility in older adults. Progress in Neurobiology, 208, 102180. https://doi.org/10.1016/j.pneurobio.2021.102180 Lee, H. K., Kent, J. D., Wendel, C., Wolinsky, F. D., Foster, E. D., Merzenich, M. M., & Voss, M. W. (2020). Home-Based, Adaptive Cognitive Training for Cognitively Normal Older adults: Initial Efficacy Trial. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 75(6), 1144–1154. https://doi.org/10.1093/geronb/gbz073 Liebenthal, E., Ellingson, M. L., Spanaki, M. V., Prieto, T. E., Ropella, K. M., & Binder, J. R. (2003). Simultaneous ERP and fMRI of the auditory cortex in a passive oddball paradigm. NeuroImage, 19(4), 1395–1404. https://doi.org/10.1016/S1053-8119(03)00228-3 McParlin, Z., Cerritelli, F., Rossettini, G., Friston, K. J., & Esteves, J. E. (2022). Therapeutic Alliance as Active Inference: The Role of Therapeutic Touch and Biobehavioural Synchrony in Musculoskeletal Care. Frontiers in Behavioral Neuroscience, 16, 897247. https://doi.org/10.3389/fnbeh.2022.897247 Media Laboratory at the Massachusetts Institute of Technology. (2005, May). Scratch. https://scratch.mit.edu/ Morris, L. S., Kundu, P., Dowell, N., Mechelmans, D. J., Favre, P., Irvine, M. A., Robbins, T. W., Daw, N., Bullmore, E. T., Harrison, N. A., & Voon, V. (2016). Fronto-striatal organization: Defining functional and microstructural substrates of behavioural flexibility. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 74, 118–133. https://doi.org/10.1016/j.cortex.2015.11.004 Murman, D. (2015). The Impact of Age on Cognition. Seminars in Hearing, 36(03), 111–121. https://doi.org/10.1055/s-0035-1555115 Murphy, C., Jefferies, E., Rueschemeyer, S.-A., Sormaz, M., Wang, H., Margulies, D. S., & Smallwood, J. (2018). Distant from input: Evidence of regions within the default mode network supporting perceptually-decoupled and conceptually-guided cognition. NeuroImage, 171, 393–401. https://doi.org/10.1016/j.neuroimage.2018.01.017 National Development Council. (2022, August). Population Projection for the R.O.C. (Taiwan). https://pop-proj.ndc.gov.tw/main_en/dataSearch.aspx?uid=78&pid=78 Nguyen, L., Murphy, K., & Andrews, G. (2019). Cognitive and neural plasticity in old age: A systematic review of evidence from executive functions cognitive training. Ageing Research Reviews, 53, 100912. https://doi.org/10.1016/j.arr.2019.100912 Noice, H., & Noice, T. (2009). An Arts Intervention for Older Adults Living in Subsidized Retirement Homes. Neuropsychology, Development, and Cognition. Section B, Aging, Neuropsychology and Cognition, 16(1), 56–79. https://doi.org/10.1080/13825580802233400 Pannese, E. (2011). Morphological changes in nerve cells during normal aging. Brain Structure and Function, 216(2), 85–89. https://doi.org/10.1007/s00429-011-0308-y Park, D. C., & Reuter-Lorenz, P. (2012). The Adaptive Brain: Aging and Neurocognitive Scaffolding. 28. Patterson, T. L., Goldman, S., McKibbin, C. L., Hughs, T., & Jeste, D. V. (2001). UCSD Performance-Based Skills Assessment: Development of a new measure of everyday functioning for severely mentally ill adults. Schizophrenia Bulletin, 27(2), 235–245. https://doi.org/10.1093/oxfordjournals.schbul.a006870 Paulus, M. P., Feinstein, J. S., Leland, D., & Simmons, A. N. (2005). Superior temporal gyrus and insula provide response and outcome-dependent information during assessment and action selection in a decision-making situation. NeuroImage, 25(2), 607–615. https://doi.org/10.1016/j.neuroimage.2004.12.055 R Core Team. (2020). R: A Language and Environment for Statistical Computing (version 4.0.2). R Foundation for Statistical Computing. Raichlen, D. A., Bharadwaj, P. K., Nguyen, L. A., Franchetti, M. K., Zigman, E. K., Solorio, A. R., & Alexander, G. E. (2020). Effects of simultaneous cognitive and aerobic exercise training on dual-task walking performance in healthy older adults: Results from a pilot randomized controlled trial. BMC Geriatrics, 20, 83. https://doi.org/10.1186/s12877-020-1484-5 Ramstead, M. J., Kirchhoff, M. D., & Friston, K. J. (2020). A tale of two densities: Active inference is enactive inference. Adaptive Behavior, 28(4), 225–239. Ren, J., Huang, F., Zhou, Y., Zhuang, L., Xu, J., Gao, C., Qin, S., & Luo, J. (2020). The function of the hippocampus and middle temporal gyrus in forming new associations and concepts during the processing of novelty and usefulness features in creative designs. NeuroImage, 214, 116751. https://doi.org/10.1016/j.neuroimage.2020.116751 Riddle, D. R. (Ed.). (2007). Brain Aging: Models, Methods, and Mechanisms. CRC Press/Taylor & Francis. http://www.ncbi.nlm.nih.gov/books/NBK1834/ Rockland, K. S., & Graves, W. W. (2023). The angular gyrus: A special issue on its complex anatomy and function. Brain Structure and Function, 228(1), 1–5. https://doi.org/10.1007/s00429-022-02596-6 Roheger, M., Kalbe, E., Corbett, A., Brooker, H., & Ballard, C. (2020). Predictors of changes after reasoning training in healthy adults. Brain and Behavior, 10(12), e01861. https://doi.org/10.1002/brb3.1861 Rolls, E. T. (2004). The functions of the orbitofrontal cortex. Brain and Cognition, 55(1), 11–29. https://doi.org/10.1016/S0278-2626(03)00277-X Salthouse, T. A. (2019). Trajectories of normal cognitive aging. Psychology and Aging, 34(1), 17–24. https://doi.org/10.1037/pag0000288 Seghier, M. L. (2013). The angular gyrus: Multiple functions and multiple subdivisions. The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry, 19(1), 43–61. https://doi.org/10.1177/1073858412440596 Shah, T. M., Weinborn, M., Verdile, G., Sohrabi, H. R., & Martins, R. N. (2017). Enhancing Cognitive Functioning in Healthly Older Adults: A Systematic Review of the Clinical Significance of Commercially Available Computerized Cognitive Training in Preventing Cognitive Decline. Neuropsychology Review, 27(1), 62–80. https://doi.org/10.1007/s11065-016-9338-9 Simons, D. J., Boot, W. R., Charness, N., Gathercole, S. E., Chabris, C. F., Hambrick, D. Z., & Stine-Morrow, E. A. L. (2016). Do “Brain-Training” Programs Work? Psychological Science in the Public Interest, 17(3), 103–186. https://doi.org/10.1177/1529100616661983 Slotnick, S. D., & White, R. C. (2013). The fusiform face area responds equivalently to faces and abstract shapes in the left and central visual fields. NeuroImage, 83, 408–417. https://doi.org/10.1016/j.neuroimage.2013.06.032 Sormaz, M., Murphy, C., Wang, H., Hymers, M., Karapanagiotidis, T., Poerio, G., Margulies, D. S., Jefferies, E., & Smallwood, J. (2018). Default mode network can support the level of detail in experience during active task states. Proceedings of the National Academy of Sciences of the United States of America, 115(37), 9318–9323. https://doi.org/10.1073/pnas.1721259115 Sprague, B. N., Freed, S. A., Webb, C. E., Phillips, C. B., Hyun, J., & Ross, L. A. (2019). The impact of behavioral interventions on cognitive function in healthy older adults: A systematic review. Ageing Research Reviews, 52, 32–52. https://doi.org/10.1016/j.arr.2019.04.002 The MathWorks Inc. (2018). MATLAB version: 9.5.0 (R2018b) (version: 9.5.0 (R2018b)). The MathWorks Inc. https://www.mathworks.com Todd S. Braver & Robert West. (2008). Working Memory, Executive Control, and Aging. In The Handbook of Aging and Cognition (pp. 319–380). Psychology Press. https://doi.org/10.4324/9780203837665-12 Toovey, B. R. W., Kattner, F., & Schubert, T. (2021). Cross-Modal Transfer Following Auditory Task-Switching Training in Old Adults. Frontiers in Psychology, 12, 615518. https://doi.org/10.3389/fpsyg.2021.615518 Tsai, C.-F., Lee, W.-J., Wang, S.-J., Shia, B.-C., Nasreddine, Z., & Fuh, J.-L. (2012). Psychometrics of the Montreal Cognitive Assessment (MoCA) and its subscales: Validation of the Taiwanese version of the MoCA and an item response theory analysis. International Psychogeriatrics, 24(4), 651–658. https://doi.org/10.1017/S1041610211002298 Wan-Rue Lin, Yu-Shiang Su, & Joshua Oon Soo Goh. (2020). Neural correlates underlying passive and active abstract rule inferencing. Weiner, K. S., & Zilles, K. (2016). The anatomical and functional specialization of the fusiform gyrus. Neuropsychologia, 83, 48–62. https://doi.org/10.1016/j.neuropsychologia.2015.06.033 Willis, S. L., & Schaie, K. W. (1986). Training the Elderly on the Ability Factors of Spatial Orientation and Inductive Reasoning. World Health Organization. (2022, October 1). Ageing and health. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health | - |
| dc.identifier.uri | http://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.abstract | Cognitive 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 |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-03T17:50:38Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-10-03T17:50:38Z (GMT). No. of bitstreams: 0 | en |
| 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 | - |
| dc.language.iso | en | - |
| 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.subject | fMRI | en |
| dc.subject | older adults | en |
| dc.subject | cognitive training | en |
| dc.subject | LEGO robot programming | en |
| dc.subject | active inference | en |
| dc.title | 以樂高程式設計介入高齡者認知健康 | zh_TW |
| dc.title | A Lego Robot Programming Intervention for Enhancing Older Adults Cognitive Health | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 毛慧芬;余家斌;吳建德 | zh_TW |
| dc.contributor.oralexamcommittee | Hui-fen Mao;Chia-Pin Yu;Chien-Te Wu | en |
| 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.keyword | older adults,cognitive training,LEGO robot programming,active inference,fMRI, | en |
| dc.relation.page | 86 | - |
| dc.identifier.doi | 10.6342/NTU202301153 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-06-27 | - |
| dc.contributor.author-college | 醫學院 | - |
| dc.contributor.author-dept | 腦與心智科學研究所 | - |
| 顯示於系所單位: | 腦與心智科學研究所 | |
文件中的檔案:
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-111-2.pdf 未授權公開取用 | 3.46 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。
