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題名 以部分XOR作業探討類別學習中XOR策略生成的原因
How the XOR categorization strategy is generated when learning a partial XOR category structure
作者 張育瑋
Chang, Yu-Wei
貢獻者 楊立行
張育瑋
Chang, Yu-Wei
關鍵詞 XOR策略
知識分化
分類學習
工作記憶
日期 2018
上傳時間 19-Jul-2018 17:27:34 (UTC+8)
摘要 Conaway與Kurtz（2015）提出部分XOR作業（partial-XOR task）的行為證據，顯示在沒有學習的情況下，仍有一部分參與者會捨棄接近性而使用XOR策略進行分類。這樣的結果並沒辦法用參考點模型解釋，但能被發散自編碼模型（divergent autoencoder model，簡稱DIVA；Kurtz, 2007）所解釋。然而，他們的研究並沒有說明，為什麼以及在何者情境下，人們會自主性地生成XOR策略。為此，本研究提出兩個假設，分別是對立捷思（contrast heuristic）與知識分化（knowledge partitioning）作為說明。實驗一先重製（replicate）了Conaway與Kurtz的結果。實驗二藉由破壞原先類別結構的對稱性，以期減少自主性XOR策略之生成，然而這個假設並沒有得到支持，顯示對立捷思不是人們自主性使用XOR策略的原因。實驗三則操弄刺激向度在不同類別內的相關程度，使得一個類別內兩刺激向度有高相關；但另一個類別內則相關為零。若如DIVA所示，自主性XOR策略的生成與類別內刺激向度之間的相關有關，我們應預期實驗三中觀察到的自主性XOR策略生成的比例下降。若如知識分化所示，人們只是將部分XOR的結構切分成不同區域，再以不同規則進行分類，則XOR策略應仍會出現，不受刺激向度之間的相關程度影響，結果發現不但沒有下降還反而增加，支持知識分化的說法。同時，這兩個實驗也都發現XOR策略的生成與工作記憶廣度無關，進一步突顯知識分化與XOR策略之間的關聯性。由於採用知識分化策略必須要能夠分別注意不同的刺激向度，實驗四以心理不可分割的刺激向度進行實驗，果然沒有發現任何自主性XOR策略的生成。綜合四個實驗，本研究結論，使用部分XOR類別結構所誘發的自主性XOR分類策略其實是由於實驗參與者使用了知識分化的緣故。
參考文獻 俞信安（民96）。分類研究中的自發性知識分化現象（未出版之碩士論文）。國立中正大學，嘉義市。
Abdi, H., Valentin, D., & Edelman, B. (1999). Neural Networks (Quantitative Applications in the Social Sciences).
Aha, D. W., & Goldstone, R. L. (1992). Concept learning and flexible weighting. Paper presented at the Proceedings of the fourteenth annual conference of the Cognitive Science Society.
Ashby, F. G., Alfonso-Reese, L. A., & Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological review, 105(3), 442.
Ashby, F. G., & Gott, R. E. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14(1), 33.
Ashby, F. G., & Maddox, W. T. (1990). Integrating information from separable psychological dimensions. Journal of Experimental Psychology: Human Perception and Performance, 16(3), 598.
Ashby, F. G., & Maddox, W. T. (2005). Human category learning. Annu. Rev. Psychol., 56, 149-178.
Ashby, F. G., Paul, E. J., & Maddox, W. T. (2011). 4 COVIS. Formal approaches in categorization, 65.
Ashby, F. G., & Townsend, J. T. (1986). Varieties of perceptual independence. Psychological review, 93(2), 154.
Brainard, D. H., & Vision, S. (1997). The psychophysics toolbox. Spatial vision, 10, 433-436.
Conaway, N., & Kurtz, K. J. (2015). A Dissociation between Categorization and Similarity to Exemplars. Paper presented at the CogSci.
Conaway, N., & Kurtz, K. J. (2017). Similar to the category, but not the exemplars: A study of generalization. Psychonomic Bulletin & Review, 24(4), 1312-1323.
Craig, S., & Lewandowsky, S. (2012). Whichever way you choose to categorize, working memory helps you learn. Quarterly Journal of Experimental Psychology, 65(3), 439-464.
DeCaro, M. S., Carlson, K. D., Thomas, R. D., & Beilock, S. L. (2009). When and how less is more: Reply to Tharp and Pickering. Cognition, 111(3), 415-421.
DeCaro, M. S., Thomas, R. D., & Beilock, S. L. (2008). Individual differences in category learning: Sometimes less working memory capacity is better than more. Cognition, 107(1), 284-294.
Donkin, C., Newell, B. R., Kalish, M., Dunn, J. C., & Nosofsky, R. M. (2015). Identifying strategy use in category learning tasks: A case for more diagnostic data and models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41(4), 933.
Edmunds, C., Milton, F., & Wills, A. J. (2015). Feedback can be superior to observational training for both rule-based and information-integration category structures. The Quarterly Journal of Experimental Psychology, 68(6), 1203-1222.
Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of experimental psychology: General, 127(2), 107.
Estes, W. K. (1994). Classification and cognition: Oxford University Press.
Filoteo, J. V., Lauritzen, S., & Maddox, W. T. (2010). Removing the frontal lobes: The effects of engaging executive functions on perceptual category learning. Psychological Science, 21(3), 415-423.
Fried, L. S., & Holyoak, K. J. (1984). Induction of category distributions: A framework for classification learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(2), 234.
Garner, W. R. (2014). The processing of information and structure: Psychology Press.
Garner, W. R., & Felfoldy, G. L. (1970). Integrality of stimulus dimensions in various types of information processing. Cognitive psychology, 1(3), 225-241.
Gluck, M. A., & Bower, G. H. (1988). From conditioning to category learning: An adaptive network model. Journal of experimental psychology: General, 117(3), 227.
Kalish, M. L., Lewandowsky, S., & Kruschke, J. K. (2004). Population of linear experts: knowledge partitioning and function learning. Psychological review, 111(4), 1072.
Kruschke, J. K. (1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological review, 99(1), 22.
Kruschke, J. K. (2011). Model of attentional learning. In E. M. Pothos & A. J. Wills (Eds.), Formal approaches in categorization
Cambridge University Press.
Kurtz, K. J. (2007). The divergent autoencoder (DIVA) model of category learning. Psychonomic Bulletin & Review, 14(4), 560-576.
Levering, K. R., & Kurtz, K. J. (2015). Observation versus classification in supervised category learning. Memory & Cognition, 43(2), 266-282.
Lewandowsky, S. (2011). Working memory capacity and categorization: individual differences and modeling. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37(3), 720.
Lewandowsky, S., Kalish, M., & Griffiths, T. L. (2000). Competing strategies in categorization: Expediency and resistance to knowledge restructuring. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(6), 1666.
Lewandowsky, S., Kalish, M., & Ngang, S. (2002). Simplified learning in complex situations: Knowledge partitioning in function learning. Journal of experimental psychology: General, 131(2), 163.
Lewandowsky, S., & Kirsner, K. (2000). Knowledge partitioning: Context-dependent use of expertise. Memory & Cognition, 28(2), 295-305.
Lewandowsky, S., Oberauer, K., Yang, L.-X., & Ecker, U. K. (2010). A working memory test battery for MATLAB. Behavior Research Methods, 42(2), 571-585.
Lewandowsky, S., Roberts, L., & Yang, L.-X. (2006). Knowledge partitioning in categorization: Boundary conditions. Memory & Cognition, 34(8), 1676-1688.
Lewandowsky, S., Yang, L.-X., Newell, B. R., & Kalish, M. L. (2012). Working memory does not dissociate between different perceptual categorization tasks. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(4), 881.
Maddox, W. T., & Ashby, F. G. (1993). Comparing decision bound and exemplar models of categorization. Perception & Psychophysics, 53(1), 49-70.
Maddox, W. T., & Ashby, F. G. (2004). Dissociating explicit and procedural-learning based systems of perceptual category learning. Behavioural Processes, 66(3), 309-332.
Maddox, W. T., & David, A. (2005). Delayed feedback disrupts the procedural-learning system but not the hypothesis-testing system in perceptual category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(1), 100.
Maddox, W. T., Love, B. C., Glass, B. D., & Filoteo, J. V. (2008). When more is less: Feedback effects in perceptual category learning. Cognition, 108(2), 578-589.
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描述 碩士
國立政治大學
心理學系
104752007
資料來源 http://thesis.lib.nccu.edu.tw/record/#G1047520072
資料類型 thesis
dc.contributor.advisor 楊立行zh_TW
dc.contributor.author (Authors) 張育瑋zh_TW
dc.contributor.author (Authors) Chang, Yu-Weien_US
dc.creator (作者) 張育瑋zh_TW
dc.creator (作者) Chang, Yu-Weien_US
dc.date (日期) 2018en_US
dc.date.accessioned 19-Jul-2018 17:27:34 (UTC+8)-
dc.date.available 19-Jul-2018 17:27:34 (UTC+8)-
dc.date.issued (上傳時間) 19-Jul-2018 17:27:34 (UTC+8)-
dc.identifier (Other Identifiers) G1047520072en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/118760-
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) 104752007zh_TW
dc.description.abstract (摘要) Conaway與Kurtz（2015）提出部分XOR作業（partial-XOR task）的行為證據，顯示在沒有學習的情況下，仍有一部分參與者會捨棄接近性而使用XOR策略進行分類。這樣的結果並沒辦法用參考點模型解釋，但能被發散自編碼模型（divergent autoencoder model，簡稱DIVA；Kurtz, 2007）所解釋。然而，他們的研究並沒有說明，為什麼以及在何者情境下，人們會自主性地生成XOR策略。為此，本研究提出兩個假設，分別是對立捷思（contrast heuristic）與知識分化（knowledge partitioning）作為說明。實驗一先重製（replicate）了Conaway與Kurtz的結果。實驗二藉由破壞原先類別結構的對稱性，以期減少自主性XOR策略之生成，然而這個假設並沒有得到支持，顯示對立捷思不是人們自主性使用XOR策略的原因。實驗三則操弄刺激向度在不同類別內的相關程度，使得一個類別內兩刺激向度有高相關；但另一個類別內則相關為零。若如DIVA所示，自主性XOR策略的生成與類別內刺激向度之間的相關有關，我們應預期實驗三中觀察到的自主性XOR策略生成的比例下降。若如知識分化所示，人們只是將部分XOR的結構切分成不同區域，再以不同規則進行分類，則XOR策略應仍會出現，不受刺激向度之間的相關程度影響，結果發現不但沒有下降還反而增加，支持知識分化的說法。同時，這兩個實驗也都發現XOR策略的生成與工作記憶廣度無關，進一步突顯知識分化與XOR策略之間的關聯性。由於採用知識分化策略必須要能夠分別注意不同的刺激向度，實驗四以心理不可分割的刺激向度進行實驗，果然沒有發現任何自主性XOR策略的生成。綜合四個實驗，本研究結論，使用部分XOR類別結構所誘發的自主性XOR分類策略其實是由於實驗參與者使用了知識分化的緣故。zh_TW
dc.description.tableofcontents 摘要 i
第一章 文獻探討 1
第一節 規則模型 1
第二節 原型理論 2
第三節 範例理論 2
第四節 混合模型 4
第五節 特徵統計模型 5
第六節 工作記憶和類別學習 9
第二章 實驗一 11
第一節 方法 11
第二節 結果 13
第三節 討論 16
第三章 實驗二 20
第一節 方法 21
第二節 結果 23
第三節 討論 31
第四章 實驗三 33
第一節 方法 34
第二節 結果 36
第三節 討論 47
第五章 實驗四 49
第一節 方法 50
第二節 結果 51
第三節 討論 54
第六章 綜合討論 55
第一節 研究主要發現 55
第二節 知識分化與DIVA觀點的比較 56
第三節 接近性策略與XOR策略的比較 57
第四節 XOR策略傾向指標 58
第五節 研究限制與未來方向 59
第七章 結論 62
參考文獻 63
附錄 68
第一節 工作記憶廣度的分測驗程序 68		zh_TW
dc.format.extent 2656973 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1047520072en_US
dc.subject (關鍵詞) XOR策略zh_TW
dc.subject (關鍵詞) 知識分化zh_TW
dc.subject (關鍵詞) 分類學習zh_TW
dc.subject (關鍵詞) 工作記憶zh_TW
dc.title (題名) 以部分XOR作業探討類別學習中XOR策略生成的原因zh_TW
dc.title (題名) How the XOR categorization strategy is generated when learning a partial XOR category structureen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 俞信安（民96）。分類研究中的自發性知識分化現象（未出版之碩士論文）。國立中正大學，嘉義市。
Abdi, H., Valentin, D., & Edelman, B. (1999). Neural Networks (Quantitative Applications in the Social Sciences).
Aha, D. W., & Goldstone, R. L. (1992). Concept learning and flexible weighting. Paper presented at the Proceedings of the fourteenth annual conference of the Cognitive Science Society.
Ashby, F. G., Alfonso-Reese, L. A., & Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological review, 105(3), 442.
Ashby, F. G., & Gott, R. E. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14(1), 33.
Ashby, F. G., & Maddox, W. T. (1990). Integrating information from separable psychological dimensions. Journal of Experimental Psychology: Human Perception and Performance, 16(3), 598.
Ashby, F. G., & Maddox, W. T. (2005). Human category learning. Annu. Rev. Psychol., 56, 149-178.
Ashby, F. G., Paul, E. J., & Maddox, W. T. (2011). 4 COVIS. Formal approaches in categorization, 65.
Ashby, F. G., & Townsend, J. T. (1986). Varieties of perceptual independence. Psychological review, 93(2), 154.
Brainard, D. H., & Vision, S. (1997). The psychophysics toolbox. Spatial vision, 10, 433-436.
Conaway, N., & Kurtz, K. J. (2015). A Dissociation between Categorization and Similarity to Exemplars. Paper presented at the CogSci.
Conaway, N., & Kurtz, K. J. (2017). Similar to the category, but not the exemplars: A study of generalization. Psychonomic Bulletin & Review, 24(4), 1312-1323.
Craig, S., & Lewandowsky, S. (2012). Whichever way you choose to categorize, working memory helps you learn. Quarterly Journal of Experimental Psychology, 65(3), 439-464.
DeCaro, M. S., Carlson, K. D., Thomas, R. D., & Beilock, S. L. (2009). When and how less is more: Reply to Tharp and Pickering. Cognition, 111(3), 415-421.
DeCaro, M. S., Thomas, R. D., & Beilock, S. L. (2008). Individual differences in category learning: Sometimes less working memory capacity is better than more. Cognition, 107(1), 284-294.
Donkin, C., Newell, B. R., Kalish, M., Dunn, J. C., & Nosofsky, R. M. (2015). Identifying strategy use in category learning tasks: A case for more diagnostic data and models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41(4), 933.
Edmunds, C., Milton, F., & Wills, A. J. (2015). Feedback can be superior to observational training for both rule-based and information-integration category structures. The Quarterly Journal of Experimental Psychology, 68(6), 1203-1222.
Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of experimental psychology: General, 127(2), 107.
Estes, W. K. (1994). Classification and cognition: Oxford University Press.
Filoteo, J. V., Lauritzen, S., & Maddox, W. T. (2010). Removing the frontal lobes: The effects of engaging executive functions on perceptual category learning. Psychological Science, 21(3), 415-423.
Fried, L. S., & Holyoak, K. J. (1984). Induction of category distributions: A framework for classification learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(2), 234.
Garner, W. R. (2014). The processing of information and structure: Psychology Press.
Garner, W. R., & Felfoldy, G. L. (1970). Integrality of stimulus dimensions in various types of information processing. Cognitive psychology, 1(3), 225-241.
Gluck, M. A., & Bower, G. H. (1988). From conditioning to category learning: An adaptive network model. Journal of experimental psychology: General, 117(3), 227.
Kalish, M. L., Lewandowsky, S., & Kruschke, J. K. (2004). Population of linear experts: knowledge partitioning and function learning. Psychological review, 111(4), 1072.
Kruschke, J. K. (1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological review, 99(1), 22.
Kruschke, J. K. (2011). Model of attentional learning. In E. M. Pothos & A. J. Wills (Eds.), Formal approaches in categorization
Cambridge University Press.
Kurtz, K. J. (2007). The divergent autoencoder (DIVA) model of category learning. Psychonomic Bulletin & Review, 14(4), 560-576.
Levering, K. R., & Kurtz, K. J. (2015). Observation versus classification in supervised category learning. Memory & Cognition, 43(2), 266-282.
Lewandowsky, S. (2011). Working memory capacity and categorization: individual differences and modeling. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37(3), 720.
Lewandowsky, S., Kalish, M., & Griffiths, T. L. (2000). Competing strategies in categorization: Expediency and resistance to knowledge restructuring. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(6), 1666.
Lewandowsky, S., Kalish, M., & Ngang, S. (2002). Simplified learning in complex situations: Knowledge partitioning in function learning. Journal of experimental psychology: General, 131(2), 163.
Lewandowsky, S., & Kirsner, K. (2000). Knowledge partitioning: Context-dependent use of expertise. Memory & Cognition, 28(2), 295-305.
Lewandowsky, S., Oberauer, K., Yang, L.-X., & Ecker, U. K. (2010). A working memory test battery for MATLAB. Behavior Research Methods, 42(2), 571-585.
Lewandowsky, S., Roberts, L., & Yang, L.-X. (2006). Knowledge partitioning in categorization: Boundary conditions. Memory & Cognition, 34(8), 1676-1688.
Lewandowsky, S., Yang, L.-X., Newell, B. R., & Kalish, M. L. (2012). Working memory does not dissociate between different perceptual categorization tasks. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(4), 881.
Maddox, W. T., & Ashby, F. G. (1993). Comparing decision bound and exemplar models of categorization. Perception & Psychophysics, 53(1), 49-70.
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dc.identifier.doi (DOI) 10.6814/THE.NCCU.PSY.007.2018.C01-