心流體驗

出自維基百科,自由嘅百科全書
(由心流跳轉過嚟)
跳去導覽 跳去搵嘢
一個人聚精會神打緊隻籃球遊戲;佢似乎處於相當程度嘅心流狀態。

心流體驗粵拼sam1 lau4 tai2 jim6英文flow experience),簡稱心流psychological flow / flow),係一種俾人譽為「最佳體驗」(optimal experience)嘅心理狀態。經歷心流嘅人通常係做緊某樣要求技術嘅活動,而佢做做吓一路做一路進入一個高度專注嘅狀態,感覺好享受嗰樣活動,一路直至活動結束為止。呢種高度專注同愉快嘅狀態就係所謂嘅心流[1]

心流可以由好多種唔同嘅活動引起:一樣引起心流嘅活動通係為做嗰個人提供一定程度嘅挑戰,唔係太易又唔係太難(即係所謂嘅適度挑戰,optimal challenge);而心理實驗等嘅研究表明,心流可以由好多唔同嘅活動引起,包括運動[2]畫畫[3]、捉[4]以至打機(睇埋動態難度調控方面嘅研究)[5]等嘅活動都可以令到做嗰個人進入心流-只要嗰樣活動能夠為做嗰個人提供適度挑戰就得。

心流吸引咗多個領域嘅研究者關注:一方面,心理學神經科學等嘅領域有興趣想知一個人處於心流狀態嗰陣個裏面發生緊乜嘢事[6];另一方面,心流往往能夠令一個人覺得好享受引起心流嗰樣活動,以及有動機進一步提升自己喺嗰樣活動上嘅技術[7],所以對心流嘅研究亦都有助於思考點樣幫人學習同享受一樣活動-而呢啲課題對於教育學管理學遊戲設計等嘅領域嚟講都相當有用[8][9]

基礎概念[編輯]

特徵[編輯]

一個細路女好專心噉做緊嘢。
睇埋:意識注意力同埋時間知覺

定義上,心流體驗有以下嘅特徵[1][10]

起因[編輯]

睇埋:有意義嘅玩同埋有益難度

一件引致心流嘅工作通常有以下嘅特徵(可以睇埋有意義嘅玩):

  1. 有明確嘅目的,令做嗰個人清楚知道自己要做乜;
  2. 會對做嗰個人俾清晰嘅回輸(feedback),令做嗰個人清楚知道自己嘅進度;
  3. 適度挑戰(optimal challenge),即係話件工作唔係太易又唔係太難,令到做嗰個人有一定嘅機會會成功,但又有一定嘅機會會失敗[14]

順帶一提,心流之所以會叫心「流」係有原因嘅:喺 1975 年心流研究啱啱開始嗰陣,研究呢個現象嘅心理學家發現佢哋有好幾個受試者將佢哋感受到嘅獨特體驗描述成「好似俾一股水流帶住佢哋前進噉」-於是班心理學家就決定將呢種現象改名做「心流」[15]

操作化[編輯]

睇埋:動態難度調控

實驗性嘅心流研究上,最常用嚟人工噉引起心流嘅方法係曉調控自身難度嘅電子遊戲:最能夠引起心流嘅係適度挑戰;想像一個研究者想做實驗研究心流,佢可以寫個簡單嘅電子遊戲程式,令隻遊戲曉按玩家嘅能力調整自己嘅難度,即係個遊戲程式喺玩家表現差(難度高得滯)嗰陣會降低難度,喺玩家表現好(難度低得滯)嗰陣會提升難度(動態難度調控),從而做到為玩家提供適度挑戰嘅效果,然後研究者就可以靠呢個遊戲程式引起心流,跟住就研究心流呢種心理狀態-例:個研究者搵班受試者返實驗室,要佢哋一路玩一隻曉調整自己難度嘅遊戲,一路用腦電圖(EEG)等嘅方法量度受試者嘅活動,靠噉研究心流狀態同啲乜嘢腦活動有統計相關[16]

難度[編輯]

喺基本嘅動作遊戲當中,難度呢家嘢可以用以下呢啲參數嚟操作化[17]

  • 玩家角色嘅火力(數值愈低隻遊戲就愈難);
  • 威力提升出現嘅頻率(數值愈低隻遊戲就愈難);
  • 關卡嘅特性,例如(喺射擊遊戲入面)戰場上有幾多掩護物可以俾玩家匿埋喺後面[18]
  • NPC 嘅特性:
    • 敵人嘅速度(數值愈高隻遊戲就愈難);
    • 敵人嘅血量(數值愈高隻遊戲就愈難);
    • 敵人出現嘅頻率(數值愈高隻遊戲就愈難);
    • 敵人嘅火力(數值愈高隻遊戲就愈難);
    • 人工智能嘅水平[19]
  • 喺一隻涉及玩家避開敵人嘅遊戲(例如食鬼)當中,難度嘅適當度()可以用以下呢個指標量度[20]
    當中 係指玩家每一次玩,要行咗幾多步啲敵人先成功殺死佢, 係玩咗 局之後嘅 平均值, 係喺嗰 局當中 嘅最大值,而 係一個參數。難度愈適當, 數值愈高。

... 等等。

生理相關[編輯]

睇埋:心理壓力

目前嘅研究指,心流狀態係一個有少少心理壓力嘅狀態:例如有研究發現,一個人喺進入心流狀態嗰時心率變化度(heart-rate variability,HRV)會下降(HRV 下降一般表示個人感受到壓力)[4]

被指同心流有關嘅腦區:

  • 中側前額皮層(medial prefrontal cortex;人喺諗同自己有關嘅資訊嗰陣會用嘅腦區)被指喺心流嗰陣會活動下降[21]

應用[編輯]

玩家玩嗰陣經歷嘅心流可以用嚟評價一隻電子遊戲,一隻電子遊戲可以想像成一件件連串嘅有挑戰性嘅事件[22]

註釋[編輯]

  1. 呢點同認為「愈係專注就愈需要花費精神先做得到」嘅廿世紀注意力理論唔相容。

睇埋[編輯]

文獻[編輯]

  • Abuhamdeh, S. (2020). Investigating the "flow" experience: Key conceptual and operational issues. Frontiers in Psychology.
  • Cheron, G. (2016). How to measure the psychological "flow"? A neuroscience perspective. Frontiers in psychology, 7, 1823.
  • Csikszentmihalyi, M. (2000). Beyond boredom and anxiety. Jossey-Bass.
  • Csíkszentmihályi, Mihály (1996), Creativity: Flow and the Psychology of Discovery and Invention, New York: Harper Perennial, ISBN 978-0-06-092820-9
  • Csíkszentmihályi, Mihály (1996), Finding Flow: The Psychology of Engagement With Everyday Life, Basic Books, ISBN 978-0-465-02411-7 (a popular exposition emphasizing technique).
  • Csíkszentmihályi, Mihály (2003), Good Business: Leadership, Flow, and the Making of Meaning, New York: Penguin Books, ISBN 978-0-14-200409-8.
  • Dietrich, A., & Stoll, O. (2010). Effortless attention, hypofrontality, and perfectionism (PDF). Effortless attention: A new perspective in the cognitive science of attention and action, 159-178.
  • Ellis, G. D., Freeman, P. A., Jamal, T., & Jiang, J. (2019). A theory of structured experience (PDF). Annals of Leisure Research, 22(1), 97-118.
  • Ewing, K. C., Fairclough, S. H., & Gilleade, K. (2016). Evaluation of an adaptive game that uses EEG measures validated during the design process as inputs to a biocybernetic loop. Frontiers in human neuroscience, 10, 223.
  • Ghani, J. A., & Deshpande, S. P. (1994). Task characteristics and the experience of optimal flow in human-computer interaction (PDF). The Journal of psychology, 128(4), 381-391.
  • Jackson, Susan A. & Csíkszentmihályi, Mihály (1999), Flow in Sports: The Keys to Optimal Experiences and Performances, Champaign, Illinois: Human Kinetics Publishers, ISBN 978-0-88011-876-7
  • Mainemelis, Charalampos (2001), "When the Muse Takes It All: A Model for the Experience of Timelessness in Organizations", The Academy of Management Review, 26 (4): 548–565, doi:10.2307/3560241, JSTOR 3560241
  • Martins, J., Costa, C., Oliveira, T., Gonçalves, R., & Branco, F. (2019). How smartphone advertising influences consumers' purchase intention (PDF). Journal of Business Research, 94, 378-387.
  • Nuyens, F. M., Kuss, D. J., Lopez-Fernandez, O., & Griffiths, M. D. (2019). The potential interaction between time perception and gaming: A narrative review (PDF). International Journal of Mental Health and Addiction, 1-21.
  • Pearce, J. (2005, October). Engaging the learner: how can the flow experience support e-learning? (PDF). In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 2288-2295). Association for the Advancement of Computing in Education (AACE).
  • Tse, D. C., Nakamura, J., & Csikszentmihalyi, M. (2020). Beyond challenge-seeking and skill-building: Toward the lifespan developmental perspective on flow theory (PDF). The Journal of Positive Psychology, 15(2), 171-182.
  • Weber, R., & Fisher, J. T. (2020). Flow. The International Encyclopedia of Media Psychology, 1-5.

[編輯]

  1. 1.0 1.1 Csikszentmihalyi, M., & Csikszentmihalyi, I. S. (Eds.). (1992). Optimal experience: Psychological studies of flow in consciousness. Cambridge university press.
  2. Jackson, S. A. (1996). Toward a conceptual understanding of the flow experience in elite athletes. Research quarterly for exercise and sport, 67(1), 76-90.
  3. Keller, J., & Blomann, F. (2008). Locus of control and the flow experience: An experimental analysis. European Journal of Personality: Published for the European Association of Personality Psychology, 22(7), 589-607.
  4. 4.0 4.1 Tozman, T., Zhang, Y. Y., & Vollmeyer, R. (2017). Inverted U-shaped function between flow and cortisol release during chess play. Journal of Happiness Studies, 18(1), 247-268.
  5. Klasen, M., Weber, R., Kircher, T. T., Mathiak, K. A., & Mathiak, K. (2012). Neural contributions to flow experience during video game playing. Social cognitive and affective neuroscience, 7(4), 485-495.
  6. 6.0 6.1 6.2 Dietrich, A., & Stoll, O. (2010). Effortless attention, hypofrontality, and perfectionism. Effortless attention: A new perspective in the cognitive science of attention and action, 159-178.
  7. 7.0 7.1 Tse, D. C., Nakamura, J., & Csikszentmihalyi, M. (2020). Beyond challenge-seeking and skill-building: Toward the lifespan developmental perspective on flow theory (PDF). The Journal of Positive Psychology, 15(2), 171-182.
  8. Erhel, S., & Jamet, E. (2019). Improving instructions in educational computer games: Exploring the relations between goal specificity, flow experience and learning outcomes. Computers in Human Behavior, 91, 106-114.
  9. Kiili, K. (2005). On educational game design: Building blocks of flow experience. Tampere University of Technology.
  10. Nakamura, J.; Csikszentmihályi, M. (20 December 2001). "Flow Theory and Research". In C. R. Snyder Erik Wright, and Shane J. Lopez (ed.). Handbook of Positive Psychology. Oxford University Press. pp. 195–206.
  11. Kahneman, D. (1973). Attention and effort. Englewood Cliffs, NJ: Prentice-Hall.
  12. Nuyens, F. M., Kuss, D. J., Lopez-Fernandez, O., & Griffiths, M. D. (2019). The potential interaction between time perception and gaming: A narrative review (PDF). International Journal of Mental Health and Addiction, 1-21.
  13. Custodero, L. A. (2002). Seeking challenge, finding skill: Flow experience and music education. Arts education policy review, 103(3), 3-9.
  14. Fong, C. J., Zaleski, D. J., & Leach, J. K. (2015). The challenge-skill balance and antecedents of flow: A meta-analytic investigation (PDF). The Journal of Positive Psychology, 10(5), 425-446.
  15. Csikszentmihalyi, M. (2000). Beyond boredom and anxiety. Jossey-Bass. p. 10.
  16. Tozman, T., Magdas, E. S., MacDougall, H. G., & Vollmeyer, R. (2015). Understanding the psychophysiology of flow: A driving simulator experiment to investigate the relationship between flow and heart rate variability. Computers in Human Behavior, 52, 408-418.
  17. Hunicke, R. (2005, June). The case for dynamic difficulty adjustment in games (PDF). In Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology (pp. 429-433).
  18. Fraser, J., Katchabaw, M., & Mercer, R. E. (2014). A methodological approach to identifying and quantifying video game difficulty factors (PDF). Entertainment Computing, 5(4), 441-449.
  19. Spronck, P., Sprinkhuizen-Kuyper, I., & Postma, E. (2004, November). Difficulty scaling of game AI 互聯網檔案館歸檔,歸檔日期2019年8月19號,.. In Proceedings of the 5th International Conference on Intelligent Games and Simulation (GAME-on 2004) (pp. 33-37).
  20. Yannakakis, G. N., & Hallam, J. (2007). Towards optimizing entertainment in computer games. Applied Artificial Intelligence, 21(10), 933-971.
  21. Ulrich, M., Niemann, J., Boland, M., Kammer, T., Niemann, F., & Grön, G. (2018). The neural correlates of flow experience explored with transcranial direct current stimulation. Experimental brain research, 236(12), 3223-3237.
  22. Sweetser, P., & Johnson, D. (2019, December). GameFlow and Player Experience Measures: An Initial Comparison of Conceptual Constructs. In Proceedings of the 31st Australian Conference on Human-Computer-Interaction (pp. 317-321).
  23. Cruz, C. A., & Uresti, J. A. R. (2017). Player-centered game AI from a flow perspective: Towards a better understanding of past trends and future directions. Entertainment Computing, 20, 11-24. p .6-7.

[編輯]