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題名 預測的心理機制
The psychological mechanism of forecasting作者 李孜希
Lee, Tzu Hsi貢獻者 楊立行
李孜希
Lee, Tzu Hsi關鍵詞 函式學習
預測日期 2018 上傳時間 20-Jul-2018 18:01:33 (UTC+8) 摘要 先前與預測（Forecasting）相關的研究雖多，卻多在企管、金融或經濟等領域，且對其心理機制無所著墨。在此研究中，我們提出了三種在預測中可能的心理表徵形式，分別是：以時間為獨變項的方程式、只參考變項前一刻數值的遞迴方程式，以及將所有曾出現過的變項數值作為獨變項的自迴歸方程式，試圖探討何者適合作為預測模型的心理表徵。本研究共招募了268位政大學生作為實驗參與者，透過三個在電腦上施測的行為實驗，我們逐一檢驗了這三種表徵的可能性，最終提出，預測函式的心理表徵應為遞迴方程式。在實驗一中，我們探討了預測作業的難易度與預測函式結構複雜度的關係，檢視其關聯性是否與函式學習作業中發現的現象一致，並指出預測作業的難易度可能與函式中所使用的參數個數無關，排除了以時間為獨變項的方程式作為預測心理表徵唯一的可能性。接下來，我們於實驗二探究了人們是否敏感於變項前後嘗試次之間的關聯性並藉此進行預測，發現一旦前後刺激之間的關聯性被破壞，人們在預測作業中的表現便大幅受到影響，表示前後嘗試次之間的關聯性是預測作業中的重要因素。最後，透過實驗三的設計，我們比較了兩個設計情境之間的差異，顯示人們在進行預測時主要是參考前一個刺激的數值，這讓我們得以確定預測的心理表徵形式為遞迴方程式。在重新檢視了預測和函式學習的異同之後，我們認為預測是函式學習的特例，是過去函式學習中未曾探究過的函式類型。 參考文獻 Andreassen, P. B., & Kraus, S. J. (1990). Judgmental extrapolation and the salience of change. Journal of forecasting, 9(4), 347-372. doi:10.1002/for.3980090405Brehmer, B. (1974). Hypotheses about relations between scaled variables in the learning of probabilistic inference tasks. Organizational Behavior and Human Performance, 11(1), 1-27.Busemeyer, J. R., Byun, E., Delosh, E. L., & McDaniel, M. A. (1997). Learning functional relations based on experience with input-output pairs by humans and artificial neural networks. In K. Lamberts & D. Shanks (Eds.), Knowledge concepts and categories (pp. 405-437). Cambridge, MA: MIT Press.Busemeyer, J. R., Dewey, G. I., & Medin, D. L. (1984). Evaluation of exemplar-based generalization and the abstraction of categorical information. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(4), 638-648. doi:10.1037/0278-7393.10.4.638Carroll, J. D. (1963). Functional learning: The learning of continuous functional mappings relating stimulus and response continua. Princeton, NJ.DeLosh, E. L., Busemeyer, J. R., & McDaniel, M. A. (1997). Extrapolation: the sine qua non for abstraction in function learning. J Exp Psychol Learn Mem Cogn, 23(4), 968-986.Goodwin, P., & Wright, G. (1993). Improving judgmental time series forecasting: A review of the guidance provided by research. International Journal of Forecasting, 9(2), 147-161.Harvey, N. (1988). Judgmental forecasting of univariate time series. Journal of Behavioral Decision Making, 1(2), 95-110. doi:10.1002/bdm.3960010204Hogarth, R. M., & Makridakis, S. (1981). Forecasting and planning: An evaluation. Management Science, 27(2), 115-138.Kalish, M. L., Lewandowsky, S., & Kruschke, J. K. (2004). Population of linear experts: knowledge partitioning and function learning. Psychological review, 111(4), 1072-1099. doi:10.1037/0033-295X.111.4.1072Koh, K., & Meyer, D. E. (1991). Function learning: Induction of continuous stimulus-response relations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17(5), 811-836. doi:10.1037/0278-7393.17.5.811Kusev, P., van Schaik, P., Tsaneva‐Atanasova, K., Juliusson, A., & Chater, N. (2018). Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions. Cognitive science, 42(1), 77-102.Lawrence, M., Goodwin, P., O`Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22(3), 493-518. doi:10.1016/j.ijforecast.2006.03.007Lawrence, M., & Makridakis, S. (1989). Factors affecting judgmental forecasts and confidence intervals. Organizational Behavior and Human Decision Processes, 43(2), 172-187.Peirce, J. W. (2007). PsychoPy—psychophysics software in Python. Journal of neuroscience methods, 162(1), 8-13.Roll, R. (1984). Orange juice and weather. The American Economic Review, 74(5), 861-880.Sniezek, J. A. (1986). The role of variable labels in cue probability learning tasks. Organizational Behavior and Human Decision Processes, 38(2), 141-161.Stewart, T. R., & Lusk, C. M. (1994). Seven components of judgmental forecasting skill: Implications for research and the improvement of forecasts. Journal of forecasting, 13(7), 579-599. 描述 碩士
國立政治大學
心理學系
103752010資料來源 http://thesis.lib.nccu.edu.tw/record/#G1037520101 資料類型 thesis dc.contributor.advisor 楊立行 zh_TW dc.contributor.author (Authors) 李孜希 zh_TW dc.contributor.author (Authors) Lee, Tzu Hsi en_US dc.creator (作者) 李孜希 zh_TW dc.creator (作者) Lee, Tzu Hsi en_US dc.date (日期) 2018 en_US dc.date.accessioned 20-Jul-2018 18:01:33 (UTC+8) - dc.date.available 20-Jul-2018 18:01:33 (UTC+8) - dc.date.issued (上傳時間) 20-Jul-2018 18:01:33 (UTC+8) - dc.identifier (Other Identifiers) G1037520101 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/118784 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 心理學系 zh_TW dc.description (描述) 103752010 zh_TW dc.description.abstract (摘要) 先前與預測（Forecasting）相關的研究雖多，卻多在企管、金融或經濟等領域，且對其心理機制無所著墨。在此研究中，我們提出了三種在預測中可能的心理表徵形式，分別是：以時間為獨變項的方程式、只參考變項前一刻數值的遞迴方程式，以及將所有曾出現過的變項數值作為獨變項的自迴歸方程式，試圖探討何者適合作為預測模型的心理表徵。本研究共招募了268位政大學生作為實驗參與者，透過三個在電腦上施測的行為實驗，我們逐一檢驗了這三種表徵的可能性，最終提出，預測函式的心理表徵應為遞迴方程式。在實驗一中，我們探討了預測作業的難易度與預測函式結構複雜度的關係，檢視其關聯性是否與函式學習作業中發現的現象一致，並指出預測作業的難易度可能與函式中所使用的參數個數無關，排除了以時間為獨變項的方程式作為預測心理表徵唯一的可能性。接下來，我們於實驗二探究了人們是否敏感於變項前後嘗試次之間的關聯性並藉此進行預測，發現一旦前後刺激之間的關聯性被破壞，人們在預測作業中的表現便大幅受到影響，表示前後嘗試次之間的關聯性是預測作業中的重要因素。最後，透過實驗三的設計，我們比較了兩個設計情境之間的差異，顯示人們在進行預測時主要是參考前一個刺激的數值，這讓我們得以確定預測的心理表徵形式為遞迴方程式。在重新檢視了預測和函式學習的異同之後，我們認為預測是函式學習的特例，是過去函式學習中未曾探究過的函式類型。 zh_TW dc.description.tableofcontents 研究動機與目的 1文獻探討 4預測、歸納與函式學習 4預測與函式學習 6實驗一 10實驗設計 11參與者與儀器 11實驗程序 12結果與討論 14學習表現 15學習速度 15小結 20實驗二 22實驗設計 25參與者與儀器 25結果與討論 25學習表現 26學習速度 28小結 31實驗三 35實驗設計 38參與者與儀器 38結果與討論 38學習表現 39學習速度 41奇偶性 45小結 48綜合討論 50預測是不是函式學習？ 50預測作業的難易度 53預測模型的可能性 53測驗階段 54工作記憶廣度與動靜態呈現方式 55研究限制 57會閃躲的標靶 57缺乏空白實驗 57刺激間隔時間過短 57未來研究方向 58結論 60參考文獻 61 zh_TW dc.format.extent 2324248 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1037520101 en_US dc.subject (關鍵詞) 函式學習 zh_TW dc.subject (關鍵詞) 預測 zh_TW dc.title (題名) 預測的心理機制 zh_TW dc.title (題名) The psychological mechanism of forecasting en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Andreassen, P. B., & Kraus, S. J. (1990). Judgmental extrapolation and the salience of change. Journal of forecasting, 9(4), 347-372. doi:10.1002/for.3980090405Brehmer, B. (1974). Hypotheses about relations between scaled variables in the learning of probabilistic inference tasks. Organizational Behavior and Human Performance, 11(1), 1-27.Busemeyer, J. R., Byun, E., Delosh, E. L., & McDaniel, M. A. (1997). Learning functional relations based on experience with input-output pairs by humans and artificial neural networks. In K. Lamberts & D. Shanks (Eds.), Knowledge concepts and categories (pp. 405-437). Cambridge, MA: MIT Press.Busemeyer, J. R., Dewey, G. I., & Medin, D. L. (1984). Evaluation of exemplar-based generalization and the abstraction of categorical information. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(4), 638-648. doi:10.1037/0278-7393.10.4.638Carroll, J. D. (1963). Functional learning: The learning of continuous functional mappings relating stimulus and response continua. Princeton, NJ.DeLosh, E. L., Busemeyer, J. R., & McDaniel, M. A. (1997). Extrapolation: the sine qua non for abstraction in function learning. J Exp Psychol Learn Mem Cogn, 23(4), 968-986.Goodwin, P., & Wright, G. (1993). Improving judgmental time series forecasting: A review of the guidance provided by research. International Journal of Forecasting, 9(2), 147-161.Harvey, N. (1988). Judgmental forecasting of univariate time series. Journal of Behavioral Decision Making, 1(2), 95-110. doi:10.1002/bdm.3960010204Hogarth, R. M., & Makridakis, S. (1981). Forecasting and planning: An evaluation. Management Science, 27(2), 115-138.Kalish, M. L., Lewandowsky, S., & Kruschke, J. K. (2004). Population of linear experts: knowledge partitioning and function learning. Psychological review, 111(4), 1072-1099. doi:10.1037/0033-295X.111.4.1072Koh, K., & Meyer, D. E. (1991). Function learning: Induction of continuous stimulus-response relations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17(5), 811-836. doi:10.1037/0278-7393.17.5.811Kusev, P., van Schaik, P., Tsaneva‐Atanasova, K., Juliusson, A., & Chater, N. (2018). Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions. Cognitive science, 42(1), 77-102.Lawrence, M., Goodwin, P., O`Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22(3), 493-518. doi:10.1016/j.ijforecast.2006.03.007Lawrence, M., & Makridakis, S. (1989). Factors affecting judgmental forecasts and confidence intervals. Organizational Behavior and Human Decision Processes, 43(2), 172-187.Peirce, J. W. (2007). PsychoPy—psychophysics software in Python. Journal of neuroscience methods, 162(1), 8-13.Roll, R. (1984). Orange juice and weather. The American Economic Review, 74(5), 861-880.Sniezek, J. A. (1986). The role of variable labels in cue probability learning tasks. Organizational Behavior and Human Decision Processes, 38(2), 141-161.Stewart, T. R., & Lusk, C. M. (1994). Seven components of judgmental forecasting skill: Implications for research and the improvement of forecasts. Journal of forecasting, 13(7), 579-599. zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.PSY.006.2018.C01 -
