Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/118784
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dc.contributor.advisor楊立行zh_TW
dc.contributor.author李孜希zh_TW
dc.contributor.authorLee, Tzu Hsien_US
dc.creator李孜希zh_TW
dc.creatorLee, Tzu Hsien_US
dc.date2018en_US
dc.date.accessioned2018-07-20T10:01:33Z-
dc.date.available2018-07-20T10:01:33Z-
dc.date.issued2018-07-20T10:01:33Z-
dc.identifierG1037520101en_US
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/118784-
dc.description碩士zh_TW
dc.description國立政治大學zh_TW
dc.description心理學系zh_TW
dc.description103752010zh_TW
dc.description.abstract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\n文獻探討 4\n預測、歸納與函式學習 4\n預測與函式學習 6\n實驗一 10\n實驗設計 11\n參與者與儀器 11\n實驗程序 12\n結果與討論 14\n學習表現 15\n學習速度 15\n小結 20\n實驗二 22\n實驗設計 25\n參與者與儀器 25\n結果與討論 25\n學習表現 26\n學習速度 28\n小結 31\n實驗三 35\n實驗設計 38\n參與者與儀器 38\n結果與討論 38\n學習表現 39\n學習速度 41\n奇偶性 45\n小結 48\n綜合討論 50\n預測是不是函式學習? 50\n預測作業的難易度 53\n預測模型的可能性 53\n測驗階段 54\n工作記憶廣度與動靜態呈現方式 55\n研究限制 57\n會閃躲的標靶 57\n缺乏空白實驗 57\n刺激間隔時間過短 57\n未來研究方向 58\n結論 60\n參考文獻 61zh_TW
dc.format.extent2324248 bytes-
dc.format.mimetypeapplication/pdf-
dc.source.urihttp://thesis.lib.nccu.edu.tw/record/#G1037520101en_US
dc.subject函式學習zh_TW
dc.subject預測zh_TW
dc.title預測的心理機制zh_TW
dc.titleThe psychological mechanism of forecastingen_US
dc.typethesisen_US
dc.relation.referenceAndreassen, P. B., & Kraus, S. J. (1990). Judgmental extrapolation and the salience of change. Journal of forecasting, 9(4), 347-372. doi:10.1002/for.3980090405\nBrehmer, B. (1974). Hypotheses about relations between scaled variables in the learning of probabilistic inference tasks. Organizational Behavior and Human Performance, 11(1), 1-27.\nBusemeyer, 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.\nBusemeyer, 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:10.1037/0278-7393.10.4.638\nCarroll, J. D. (1963). Functional learning: The learning of continuous functional mappings relating stimulus and response continua. Princeton, NJ.\nDeLosh, 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.\nGoodwin, 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.\nHarvey, N. (1988). Judgmental forecasting of univariate time series. Journal of Behavioral Decision Making, 1(2), 95-110. doi:10.1002/bdm.3960010204\nHogarth, R. M., & Makridakis, S. (1981). Forecasting and planning: An evaluation. Management Science, 27(2), 115-138.\nKalish, 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.111.4.1072\nKoh, 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:10.1037/0278-7393.17.5.811\nKusev, 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.\nLawrence, 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.2006.03.007\nLawrence, M., & Makridakis, S. (1989). Factors affecting judgmental forecasts and confidence intervals. Organizational Behavior and Human Decision Processes, 43(2), 172-187.\nPeirce, J. W. (2007). PsychoPy—psychophysics software in Python. Journal of neuroscience methods, 162(1), 8-13.\nRoll, R. (1984). Orange juice and weather. The American Economic Review, 74(5), 861-880.\nSniezek, J. A. (1986). The role of variable labels in cue probability learning tasks. Organizational Behavior and Human Decision Processes, 38(2), 141-161.\nStewart, 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.doi10.6814/THE.NCCU.PSY.006.2018.C01-
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