博碩士論文 etd-0723118-235831 詳細資訊


[回到前頁查詢結果 | 重新搜尋]

姓名 車建禹(Chien-Yu Che) 電子郵件信箱 E-mail 資料不公開
畢業系所 資訊管理學系研究所(Information Management)
畢業學位 碩士(Master) 畢業時期 106學年第2學期
論文名稱(中) 大腦決策機制之社會網絡分析應用
論文名稱(英) Applying Social Network Analysis to Brain Decision Mechanisms
檔案
  • etd-0723118-235831.pdf
  • 本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
    請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
    論文使用權限

    紙本論文:2 年後公開 (2020-08-24 公開)

    電子論文:使用者自訂權限:校內 2 年後、校外 2 年後公開

    論文語文/頁數 中文/79
    統計 本論文已被瀏覽 5397 次,被下載 0 次
    摘要(中) 決策行為與我們的生活息息相關,決策所涉及的範圍甚廣,不論是簡單的賭博,或是複雜的金融投資,都需要參與決策,在學術界中也是一項重要的研究領域。而學術上有許多運用社會網絡分析(Social Network Analysis)來研究大腦腦區彼此之間的關聯性,但是大部分研究的實驗設計並非涉及決策,且部分文獻僅探索大腦「結構性」的網絡,因而無法找出在特定的決策任務下「功能性」腦區之間的關聯程度。
    為此,本研究的目的在建構一個大腦網絡分析的系統,收集彙整現有的大腦決策相關文獻,其中包括獎賞、選擇、情緒、風險等七項的決策事件,並應用社會網絡分析方法來呈現在某一特定的決策事件下,腦區彼此之間的連結與強度,以及活化腦區的網絡中心性。本研究匯入了150篇已發表期刊中決策事件和活化腦區的關係,並加以編碼鍵入資料庫,在系統網站建立分析指標演算法,並使用UCINET工具驗證此系統的正確性,最後以情緒事件來評估其效度。
    摘要(英) Decision making is everywhere and it involves a broad range of tasks, from a simple gambling to a complex financial investment. Thus, decision making is very important in our life and also a key research area in academia. Since decisions are made by brains, decision making research has evolved from observing behavior to investigating the neural mechanisms involved in neural decision making. Many research findings regarding the functionality of various brain regions have been reported but conflicts also exist. We need to resolve the conflict through further analysis of prior research results.
    Social Network Analysis is a powerful data mining tool for finding the correlation among different players in a community. It has been applied to analyzing neural networks in the brain but not many in the decision making area. Hence, the purpose of this study is to construct a network analysis system that can be used to consolidate findings in existing neural decision making literature for a more comprehensive and valid interpretation of decision networks in the brain.
    A prototype system has been developed to apply the social network analysis method to present links, connection strengths and centrality of brain regions under a specific decision task, such as reward, emotion, and so on. To evaluate the designed system, over 150 published articles in neural decision making has been coded and the system was verified by comparing with UCINET for correctness. Emotion was selected as an event to assess its content validity. Both assessments show the value of this prototype system.
    關鍵字(中)
  • 社會網絡分析
  • 認知神經科學
  • 決策神經科學
  • 大腦決策
  • 大腦網絡
  • 關鍵字(英)
  • Brain decision making
  • decision neuroscience
  • Social network analysis
  • Brain network
  • Cognitive neuroscience
  • 論文目次 論文審定書 i
    誌謝 ii
    中文摘要 iii
    英文摘要 iv
    目錄 v
    圖次 vii
    表次 viii
    第一章 緒論 1
    第一節 研究背景 1
    第二節 研究動機 2
    第三節 研究目的與問題 3
    第四節 研究流程與論文結構 4
    第二章 文獻探討 5
    第一節 決策神經科學的研究問題 5
    一、腦部功能概要 5
    二、決策神經科學的研究 5
    第二節 情緒決策 8
    一、和情緒反應相關的腦區 8
    二、情緒決策之彙總分析 9
    第三節 社會網絡分析 11
    一、社會網絡的源起、定義與發展 11
    二、關係資料型態 13
    三、社會網絡分析的衡量指標 14
    四、加權後的中心性指標 20
    第三章 研究方法 25
    第一節 設計科學研究法 25
    第二節 系統雛型之架構 26
    第四章 系統分析設計與建置 27
    第一節 系統需求分析 27
    一、系統功能分析 27
    二、系統開發環境分析 28
    第二節 系統功能與介面設計 29
    一、社會網絡分析方法 29
    二、網站介面之功能 32
    第五章 系統驗證 37
    第一節 以UCINET分析中心性 37
    第二節 情緒事件之分析 41
    一、大腦第二層級之分析 41
    二、大腦第三層級之分析 42
    三、跨層級分析 44
    第六章 結論與建議 45
    第一節 研究結論 45
    第二節 研究貢獻 46
    第三節 研究限制與未來展望 47
    參考文獻 48
    附錄一 GID編碼 55
    附錄二 編碼後的資料 57
    參考文獻 中文文獻:
    陳韋亭(民103),大腦決策機制之資料探勘研究,中山大學資訊管理學系學位碩士論文。
    英文文獻:
    Barnes, J. A. (1954). Class and committees in a Norwegian island parish. Human relations, 7(1), 39-58.
    Barrat, A., Barthélemy, M., & Vespignani, A. (2004). Weighted evolving networks: coupling topology and weight dynamics. Physical review letters, 92(22), 228701.
    Barrat, A., Barthelemy, M., & Vespignani, A. (2007). The Architecture of Complex Weighted Networks: Measurements and Models. In Large Scale Structure And Dynamics Of Complex Networks: From Information Technology to Finance and Natural Science (pp. 67-92).
    Bavelas, A. (1948). A mathematical model for group structures. Applied anthropology, 7(3), 16-30.
    Beauchamp, M. A. (1965). An improved index of centrality. Behavioral science, 10(2), 161-163.
    Bechara, A., Tranel, D., Damasio, H., & Damasio, A. R. (1996),“Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex,” Cerebral cortex, 6(2), 215-225.
    Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994),“Insensitivity to future consequences following damage to human prefrontal cortex,” Cognition, 50(1), 7-15.
    Biggs, N., Lloyd, E. K., & Wilson, R. J. (1986). Graph Theory, 1736-1936. Oxford University Press.
    Bonacich, P. (1991). Simultaneous group and individual centralities. Social networks, 13(2), 155-168.
    Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social networks, 19(3), 243-269.
    Borgatti, S. (1998). What is social network analysis. In 1998.1998 Social Networks Conference in Barcelona (pp. 5-21).
    Borgatti, S. P., Jones, C., & Everett, M. G. (1998). Network measures of social capital. Connections, 21(2), 27-36.
    Borgatti, S. P. (2005). Centrality and network flow. Social networks, 27(1), 55-71.
    Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of mathematical sociology, 25(2), 163-177.
    Brandes, U. (2008). On variants of shortest-path betweenness centrality and their generic computation. Social Networks, 30(2), 136-145.
    Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008),“The brain's default network, ” Annals of the New York Academy of Sciences, 1124(1), 1-38.
    Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186.
    Burt, R. S. (1992). Structural Holes: The Social Structure of Competition.
    Camara, E., Rodriguez-Fornells, A., Ye, Z., & Münte, T. F. (2009),“ Reward networks in the brain as captured by connectivity measures, ” Frontiers in Neuroscience, 3(3), 350.
    Camille, N., Coricelli, G., Sallet, J., Pradat-Diehl, P., Duhamel, J. R., & Sirigu, A. (2004),“The involvement of the orbitofrontal cortex in the experience of regret,” Science, 304(5674), 1167-1170.
    Cole, M. W., & Schneider, W. (2007),“The cognitive control network: integrated cortical regions with dissociable functions,” Neuroimage, 37(1), 343-360.
    Cohen, M. X., Heller, A. S., & Ranganath, C. (2005). Functional connectivity with anterior cingulate and orbitofrontal cortices during decision-making. Cognitive Brain Research, 23(1), 61-70.
    Damasio, A. R. (1996). The somatic marker hypothesis and the possible functions of the prefrontal cortex. Phil. Trans. R. Soc. Lond. B, 351(1346), 1413-1420.
    Davidson, R. J., & Irwin, W. (1999). The functional neuroanatomy of emotion and affective style. Trends in cognitive sciences, 3(1), 11-21.
    Davidson, R. J. (2000). Affective style, psychopathology, and resilience: brain mechanisms and plasticity. American Psychologist, 55(11), 1196.
    Delgado, M. R., Nystrom, L. E., Fissell, C., Noll, D. C., & Fiez, J. A. (2000),“Tracking the hemodynamic responses to reward and punishment in the striatum,” Journal of neurophysiology, 84(6), 3072-3077.
    Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische mathematik, 1(1), 269-271.
    Dimoka, A. (2010),“What does the brain tell us about trust and distrust? Evidence from a functional neuroimaging study,” MIS Quarterly, 34(2), 373-396.
    De Quervain, D. J. F., Fischbacher, U., Treyer, V., Schellhammer, M., Schnyder, U., Buck, A., & Fehr, E. (2004),“The neural basis of altruistic punishment,” Science,305(5688),1254-1258.
    Eguiluz, V. M., Chialvo, D. R., Cecchi, G. A., Baliki, M., & Apkarian, A. V. (2005). Scale-free brain functional networks. Physical review letters, 94(1), 018102.
    Ernst, M., Nelson, E. E., McClure, E. B., Monk, C. S., Munson, S., Eshel, N.,& Pine, D. S. (2004),“Choice selection and reward anticipation: an fMRI study,” Neuropsychologia, 42(12), 1585-1597.
    Everton, S. F. (2012). Disrupting dark networks (Vol. 34). Cambridge University Press.
    Faust, K. (1997). Centrality in affiliation networks. Social networks, 19(2), 157-191.
    Ferreira-Santos, F. (2012). Complex Network Analysis of Brain Connectivity: An Introduction. University of Porto.
    Flament, C. (1963). Applications of graph theory to group structure. Englewood Cliffs, NJ, Prentice-Hall.
    Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 35-41.
    Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social networks, 1(3), 215-239.
    Freeman, L. (2004). The development of social network analysis. A Study in the Sociology of Science, 1.
    Freeman, L. C., Borgatti, S. P., & White, D. R. (1991). Centrality in valued graphs: A measure of betweenness based on network flow.
    Garton, L., Haythornthwaite, C., & Wellman, B. (1997). Studying online social networks. Journal of Computer‐Mediated Communication, 3(1), 0-0.
    Goodrich, M. T., & Tamassia, R. (2002). Algorithm design. Wiely India.
    Granovetter, M. S. (1977). The strength of weak ties. In Social networks (pp. 347-367).
    Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences, 100(1), 253-258.
    Hastie, R., & Dawes, R.M. (2001). Rational choice in an uncertain world. Thousand Oaks, CA: Sage Publications.
    He, Y., Chen, Z. J., & Evans, A. C. (2007). Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cerebral cortex, 17(10), 2407-2419.
    Hsu, M., Bhatt, M., Adolphs, R., Tranel, D., & Camerer, C. F. (2005),“Neural systems responding to degrees of uncertainty in human decision-making,” Science, 310(5754), 1680-1683.
    Iturria-Medina, Y., Sotero, R. C., Canales-Rodríguez, E. J., Alemán-Gómez, Y., & Melie-García, L. (2008). Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory. Neuroimage, 40(3), 1064-1076.
    Kaiser, M. (2011). A tutorial in connectome analysis: topological and spatial features of brain networks. Neuroimage, 57(3), 892-907.
    Knutson, B., Adams, C. M., Fong, G. W., & Hommer, D. (2001),“Anticipation of increasing monetary reward selectively recruits nucleus accumbens,” J Neurosci, 21(16), RC159.
    Krain, A. L., Wilson, A. M., Arbuckle, R., Castellanos, F. X., & Milham, M. P. (2006),“Distinct neural mechanisms of risk and ambiguity: a meta-analysis of decision-making,” Neuroimage, 32(1), 477-484.
    Kuhnen, C. M., & Knutson, B. (2005),“The neural basis of financial risk taking,” Neuron, 47(5), 763-770.
    Leavitt, H. J. (1951). Some effects of certain communication patterns on group performance. The Journal of Abnormal and Social Psychology, 46(1), 38.
    LeDoux, J. (1996). The Emotional Brain: The Mysterious Underpinnings of Emotional Life, ed.
    LeDoux, J. E. (2000). Emotion circuits in the brain. Annual review of neuroscience, 23(1), 155-184.
    Lewis, M., Haviland-Jones, J. M., & Barrett, L. F. (Eds.). (2010). Handbook of emotions. Guilford Press.
    MacLean, P. D. (1952). Some psychiatric implications of physiological studies on frontotemporal portion of limbic system (visceral brain). Clinical Neurophysiology, 4(4), 407-418.
    Maddock, R. J. (1999). The retrosplenial cortex and emotion: new insights from functional neuroimaging of the human brain. Trends in neurosciences, 22(7), 310-316.
    Marin, A., & Wellman, B. (2011). Social network analysis: An introduction. The SAGE handbook of social network analysis, 11.
    McClure, S. M., Li, J., Tomlin, D., Cypert, K. S., Montague, L. M., & Montague, P. R. (2004a),“Neural correlates of behavioral preference for culturally familiar drinks,” Neuron, 44(2), 379-387.
    McClure, S. M., York, M. K., & Montague, P. R. (2004b),“The neural substrates of reward processing in humans: the modern role of FMRI,” The Neuroscientist, 10(3), 260-268.
    Mitchell, J. C. (Ed.). (1969). Social networks in urban situations: analyses of personal relationships in Central African towns. Manchester University Press.
    Newman, M. E. (2001). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical review E, 64(1), 016132.
    Newman, M. E. (2001). The structure of scientific collaboration networks. Proceedings of the national academy of sciences, 98(2), 404-409.
    Newman, M. E. (2004). Analysis of weighted networks. Physical review E, 70(5), 056131.
    Ochsner, K. N., & Gross, J. J. (2005). The cognitive control of emotion. Trends in cognitive sciences, 9(5), 242-249.
    Ochsner, K. N., Silvers, J. A., & Buhle, J. T. (2012). Functional imaging studies of emotion regulation: a synthetic review and evolving model of the cognitive control of emotion. Annals of the New York Academy of Sciences, 1251(1), E1-E24.
    O'Doherty, J. P., Deichmann, R., Critchley, H. D., & Dolan, R. J. (2002),“Neural responses during anticipation of a primary taste reward,” Neuron, 33(5), 815-826.
    Opsahl, T., Colizza, V., Panzarasa, P., & Ramasco, J. J. (2008). Prominence and control: the weighted rich-club effect. Physical review letters, 101(16), 168702.
    Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social networks, 32(3), 245-251.
    Papez, J. W. (1937). A proposed mechanism of emotion. Archives of Neurology & Psychiatry, 38(4), 725-743.
    Park, C. H., Kim, S. Y., Kim, Y. H., & Kim, K. (2008). Comparison of the small-world topology between anatomical and functional connectivity in the human brain. Physica A: statistical mechanics and its applications, 387(23), 5958-5962.
    Paulus, M. P., & Frank, L. R. (2003),“Ventromedial prefrontal cortex activation is critical for preference judgments,” Neuroreport, 14(10), 1311-1315.
    Paulus, M. P., Rogalsky, C., Simmons, A., Feinstein, J. S., & Stein, M. B. (2003),“Increased activation in the right insula during risk-taking decision making is related to harm avoidance and neuroticism,” Neuroimage, 19(4), 1439-1448.
    Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of management information systems, 24(3), 45-77.
    Pessoa, L. (2008). On the relationship between emotion and cognition. Nature reviews neuroscience, 9(2), 148.
    Petrides, M. (2005). Lateral prefrontal cortex: architectonic and functional organization. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 360(1456), 781-795.
    Phan, K. L., Wager, T., Taylor, S. F., & Liberzon, I. (2002). Functional neuroanatomy of emotion: a meta-analysis of emotion activation studies in PET and fMRI. Neuroimage, 16(2), 331-348.
    Ravasz, E., & Barabási, A. L. (2003). Hierarchical organization in complex networks. Physical Review E, 67(2), 026112.
    Rilling, J. K., Gutman, D. A., Zeh, T. R., Pagnoni, G., Berns, G. S., & Kilts, C. D. (2002),“A neural basis for social cooperation,” Neuron, 35(2), 395-405.
    Rogers, R. D., Owen, A. M., Middleton, H. C., Williams, E. J., Pickard, J. D., Sahakian, B. J., & Robbins, T. W. (1999),“Choosing between small, likely rewards and large, unlikely rewards activates inferior and orbital prefrontal cortex,” The Journal of Neuroscience, 19(20), 9029-9038.
    Rolls, E. T. (2000). On the brain and emotion. Behavioral and brain sciences, 23(2), 219-228.
    Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059-1069.
    Sanfey, A. G., Loewenstein, G., McClure, S. M., & Cohen, J. D. (2006),“Neuroeconomics: cross-currents in research on decision-making,”Trends in cognitive sciences, 10(3), 108-116.
    Simmel, G. (1950). The sociology of georg simmel (Vol. 92892). Simon and Schuster.
    Sporns, O., Chialvo, D. R., Kaiser, M., & Hilgetag, C. C. (2004). Organization, development and function of complex brain networks. Trends in cognitive sciences, 8(9), 418-425.
    Sporns, O. (2010). Networks of the brain: quantitative analysis and modeling. Analysis and Function of Large-Scale Brain Networks, 7.
    Travers, J., & Milgram, S. (1967). The small world problem. Phychology Today, 1(1), 61-67.
    Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge university press.
    Wig, G. S., Schlaggar, B. L., & Petersen, S. E. (2011). Concepts and principles in the analysis of brain networks. Annals of the New York Academy of Sciences, 1224(1), 126-146.
    Yang, S., & Knoke, D. (2001). Optimal connections: strength and distance in valued graphs. Social networks, 23(4), 285-295.
    Yu, S., Huang, D., Singer, W., & Nikolić, D. (2008). A small world of neuronal synchrony. Cerebral cortex, 18(12), 2891-2901.
    參考書籍:
    榮泰生(民 102)。UCINET在社會網絡分析(SNA)之應用。臺北市:五南。
    陳世榮(譯)(民 102)。社會網絡分析方法:UCINET的應用(原作者:Robert A. Hanneman, Mark Riddle)。高雄市:巨流。(原著出版年: 2005)
    參考網站:
    http://neurosynth.org/
      http://www.analytictech.com/networks/whatis.htm
    口試委員
  • 黃三益 - 召集委員
  • 陳灯能 - 委員
  • 梁定澎 - 指導教授
  • 口試日期 2018-07-24 繳交日期 2018-08-24

    [回到前頁查詢結果 | 重新搜尋]


    如有任何問題請與論文審查小組聯繫