English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88613/118155 (75%)
Visitors : 23479783      Online Users : 648
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 理學院 > 心理學系 > 學位論文 >  Item 140.119/54640
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/54640

    Title: 類別結構的亂度因素、刺激向度個數對分類學習行為的影響
    Categorical entropy, number of stimulus dimensions, and category learning
    Authors: 林家源
    Lin, Chia Yuan
    Contributors: 楊立行
    Yang, Lee Xieng
    Lin, Chia Yuan
    Keywords: 類別學習
    category learning
    materials dimensionality
    categorical entropy
    Date: 2011
    Issue Date: 2012-10-30 11:27:22 (UTC+8)
    Abstract: Sloutsky (2010; Kloos與Sloutsky, 2008) 操弄不同的類別結構亂度 (categorical entropy) 進行類別學習作業,藉此提出了雙系統理論,認為人們會啟動不同的系統,濃縮式系統 (compression-based system)或選擇式系統 (selection-based system),以適應不同的類別結構組成之刺激材料。本研究回顧了Sloutsky的研究證據與過去類別學習領域的相關文獻,認為此雙系統理論可能只適用在向度數目較多的情境之下,因此設計了三個實驗,使用和Kloos與Sloutsky (2008) 相同的實驗派典,欲說明刺激材料的向度個數確實會影響到人們的類別學習行為。實驗一發現,Sloutsky所預測的類別結構與學習方式之交互作用只出現在向度個數較多的情境,向度個數少時則無此交互作用。實驗二得到與實驗一相同的結果,並排除了刺激材料本身特性(幾何圖形或類自然類別材料)此一混淆變項。實驗三採用特別設計的依變項,直接觀察受試者採用相似性(similarity)或規則(rule)的方式進行分類判斷,集群分析的結果顯示在向度數目少的情境時,不管何種類別結構受試者均傾向使用以規則為基礎的選擇式系統學習。因此,綜合以上發現,本研究認為Sloutsky的雙系統理論必須考慮到向度數目此一變項,才能更廣泛的應用於各種類別學習情境之中。
    The goal of this research is to point out that the dimensions of experimental materials can influence human category learning, which is neglected by traditional models of category learning. Three experiments in this research examined the effect of stimuli complexity by following the paradigms of Kloos and Sloutsky (2008). In Experiment 1, the prediction of Sloutsky’s theory (2010) on the interaction effect between category structures and learning conditions succeeds only at high complexity of materials, but fails in the low complexity condition. Experiment 2 was conducted by the same experimental setting as Experiment 1, but the natural-like stimuli were replaced by well-defined artificial geometrics. The result of Experiment 2 is the same as Experiment 1, suggesting that the complexity of materials plays a critical role in category learning no matter what kind of stimuli are used. Experiment 3 found that various materials complexity had distinct effects on human category representations. Namely, when experimental stimuli are relatively complex, people would use the corresponding category learning system to represent stimuli to learn dense categories or sparse ones. In contrast, when the stimuli are relatively simple, participants would represent the stimuli in a rule-based manner both in dense and sparse category structures.
    Reference: Alfonso-Reese, L. A., Ashby, F. G., and Brainard, D. H. (2002). What makes a categorization task difficult? Perception & Psychophysics, 64, 570-583.
    Anderson, J. R., & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Erlbaum.
    Anderson, J. R., & Betz, J. (2001). A hybrid model of categorization. Psychonomic Bulletin & Review, 8, 629–647.
    Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105, 442-481.
    Ashby, F. G., & Gott, R. E. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, & Cognition, 14, 33-53.
    Ashby, F. G., & Maddox, W. T. (2005). Human category learning. Annual Review of Psychology, 56, 149-178.
    Ashby, F. G., Maddox, W. T., Bohil, C. J. (2002). Observational versus feedback training in rule-based and information-integration category learning. Memory & Cognition, 30, 666-677.
    Ashby, F. G., Queller, S., and Berretty, P. M. (1999). On the dominance of unidimensional rules in unsupervised categorization. Perception & Psychophysics, 61, 1178-1199.
    Bar-Gad, I., Morris, G., & Bergman, H. (2003). Information processing, dimensionality reduction and reinforcement learning in the basal ganglia. Progress in Neurobiology, 71, 439–473.
    Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10, 433-436.
    Cincotta, C. M., & Seger, C. A. (2007). Dissociation between striatal regions while learning to categorize via feedback and via observation. Journal of Cognitive Neuroscience, 19, 249-265.
    Cohen, J. D., Botvinick, M. M., & Carter, C. S. (2000). Anterior cingulate and prefrontal cortex: Who’s in control? Nature Neuroscience, 3, 421-423.
    Cohen, J. D., Perlstein, W. M., Braver, T. S., Nystrom, L. E., Noll, D., C., Jonides, J., & Smith, E., E. (1997). Temporal dynamics of brain activation during a working memory task. Nature, 386, 604-608.
    Colreavy, E. & Lewandowsky, S. (2008). Strategy development and learning differences in supervised and unsupervised categorization. Memory & Cognition, 36, 762-755.
    D'Esposito, M., Postle, B. R., Ballard, D., & Lease, J. (1999). Maintenance versus manipulation of information held in working memory: An event related fMRI study. Brain and Cognition, 41, 66-86.
    Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107-140.
    Homa, D., & Chambliss, D. (1975). The relative contributions of common and distinctive information on the abstraction from ill-defined categories. Journal of Experimental Psychology: Human Learning and Memory, 1, 351-359.
    Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22-44.
    Kruschke, J. K. (1993). Human category learning: Implications for backpropagation Models. Connection Science, 5, 3-36.
    Kloos H., & Sloutsky, V. M. (2008). What’s behind different kinds of kinds: Effects of statistical density on learning and representation of categories. Journal of Experimental Psychology: General, 137, 52-72.
    Livingstion, K. R., Andrews, J. K., & Harnad, S. (1998). Categorical perception effects induced by category learning. Journal of Experimental Psychology: Learning, Memory and Cognition, 24, 732-753.
    Love, B. C. (2002). Comparing supervised and unsupervised category learning. Psychonomic Bulletin & Review, 9, 829-835.
    Love, B. C. (2003). The multifaceted nature of unsupervised category learning. Psychonomic Bulletin & Review, 10, 190-197.
    Maddox, W. T. & Ashby, F. G. (1993). Comparing decision bound and exemplar models of categorization. Perception & Psychophysics, 53, 49-70.
    Maddox, W. T., & Ashby, F. G. (1998). Selective attention and the formation of linear decision boundaries: Comment on McKinley and Nosofsky (1996). Journal of Experimental Psychology: Human Perception & Performance, 24, 302-322.
    Markman, A.B., & Makin, V.S. (1998). Referential communication and category acquisition. Journal of Experimental Psychology: General, 127, 331-354.
    Markman, A. B., & Ross, B., H. (2003). Category use and category learning. Psychological Bulletin, 129, 592-613.
    Martin, R. C., & Caramazza, A. (1980). Classification in well-defined and ill-defined categories evidence for common processing strategies. Journal of Experimental Psychology: General, 109, 320-353.
    McCloskey, M. E. & Glucksberg, S. (1978). Natural categories: Well defined or fuzzy sets? Memory & Cognition, 6, 462-472.
    McKinley, S. C., & Nosofsky, R. M. (1995). Investigations of exemplar and decision bound models in large, ill-defined category structures. Journal of Experimental Psychology: Human Perception & Performance, 21, 128-148.
    McKinley, S. C., & Nosofsky, R. M. (1996). Selective attention and the formation of linear decision boundaries. Journal of Experimental Psychology: Human Perception & Performance, 22, 294-317.
    Medin, D. L., & Edelson, S. M. (1988). Problem structure and the use of base-rate information from experience. Journal of Experimental Psychology: General, 117, 68-85.
    Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207-238.
    Medin, D. L., & Smith, E. E. (1981). Strategies and classification learning. Journal of Experimental Psychology: Human Learning and Memory, 7, 241-253.
    Minda, J. P,. & Smith, J. D. (2001). Prototypes in category learning: The effects of category size category structure, and stimulus complexity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 775-799
    Nomura, E. M., Maddox, W. T., Filoteo, J. V., Ing, A. D., Gitelman, D. R., Parrish, T. B., … Reber, P. J. (2007). Neural correlates of rule-based and information-integration visual category learning. Cerebral Cortex January, 17, 37-43.
    Nomura, E. M., Reber, P. J. (2008). A review of medial temporal lobe and caudate contributions to visual category learning. Neuroscience and Biobehavioral Reviews, 32, 279-291.
    Nosofsky, R. M. (1984). Choice, similarity, and the context theory of classification. Journalof Experimental Psychology: Learning, Memory, & Cognition, 10, 104-114.
    Nosofsky, R. M. (1986). Attention, similarity, and the identification categorization relationship. Journal of Experimental Psychology: General, 115, 39-57.
    Nosofsky, R. M. (1987). Attention and learning processes in the identification and categorization of integral stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 87-108.
    Nosofsky R. M., Clark, S. E., and Shin H. J. (1989). Rules and exemplars in categorization, identification, and recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 282-304.
    Nosofsky, R., M., Gluck, M., A., Palmeri, T., J., McKinley, S., C., & Glauthier, P. (1994). Comparing models of rule-based classification learning: A replication and extension of Shepard, Hovland, and Jenkins (1961). Memory & Cognition, 22, 352-369.
    Nosofsky, R. M., & Palmeri, T. J. (1998). A rule-plus-exception model for classifying objects in continuous-dimension spaces. Psychonomic Bulletin & Review, 5, 345-369.
    Nosofsky, R. M., Stanton, R. D., & Zaki, S. R. (2005). Procedural interference in perceptual classification: Implicit learning or cognitive complexity? Memory & Cognition, 33, 1256-1271.
    Pelli, D. G. (1997). The video toolbox software for visual psychophysics: Transforming numbers into movies. Spatial Vision, 10, 437-442.
    Pfefferbaum, A., Mathalon, D. H., Sullivan, E. V., Rawles, J. M., Zipursky, R. B., Lim, K. O. (1994). A quantitative magnetic resonance imaging study of changes in brain morphology from infancy to late adulthood. Archives of Neurology, 51, 874-887.
    Rosch, E. H. (1973). Natural categories. Cognitive Psychology, 4, 328-350.
    Rouder, J. N., & Ratcliff, R. (2004). Comparing categorization models. Journal of Experimental Psychology: General, 133, 63-82.
    Rouder J. N., & Ratcliff R. (2006). Comparing exemplar- and rule-based theories of categorization. Current Directions in Psychological Science, 15, 9-13.
    Seger, C. A. (2008). How do the basal ganglia contribute to categorization? Their roles in generalization, response selection, and learning via feedback. Neuroscience and Biobehavioral Reviews, 32, 265–278.
    Seger, C. A., & Cincotta, C. M. (2002). Striatal activation in concept learning. Cognitive, Affective, & Behavioral Neuroscience, 2, 149–161.
    Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379-423, 623-656.
    Shepard R. N., Hovland, C. I., and Jenkins, H. M. (1961). Learning and memorization of classfications. Psychological Monographs: General and Applied, 75, 517.
    Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119, 3-22.
    Sloutsky, V. M. (2003). The role of similarity in the development of categorization. Trends in Cognitive Sciences, 7, 246-251.
    Sloutsky, V. M. (2010). From perceptual categories to concepts: What develops? Cognitive Science, 34, 1244-1286.
    Smith, E. E., & Medin, D. L. (1981). Categories and concepts. Cambridge, MA: Harvard University Press.
    Smith, J. D., Murray, M. J., & Minda, J. P. (1997). Straight talk about linear separability. Journal of Experimental Psychology: Learning, Memory & Cognition, 23, 659-680.
    Sowell, E. R., Thompson, P. M., Holmes, C. J., Batth, R., Jernigan, T. L., & Toga, A. W. (1999). Localizing age-related changes in brain structure between childhood and adolescence using statistical parametric mapping. NeuroImage, 9, 587-597.
    Spellman, B. A. (1993). Implicit learning of base rates. Psycoloquy, 4(61). Retrieved June 9, 2012, from the World Wide Web: http://www.cogsci.ecs.soton.ac.uk/cgi/psyc/newpsy?4.61
    Stanton, R. D., & Nosofsky, R. M. (2007). Feedback interference and dissociations of classification: Evidence against the multiple-learning-systems hypothesis. Memory & Cognition, 35, 1747-1758.
    Verguts, T., Ameel, E., Storms, G. (2004). Measures of similarity in models of categorization. Memory & Cognition, 32, 379-389.
    Wickens, T. D. (1989). Multiway contingency tables analysis for the social sciences. Hillsdale, NJ: Lawrence Erlbaum.
    Zeithamova, D. & Maddox, W. T, (2006). Dual-task interference in perceptual category learning. Memory & Cognition, 34, 387-398.
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0098752001
    Data Type: thesis
    Appears in Collections:[心理學系] 學位論文

    Files in This Item:

    File SizeFormat
    200101.pdf1188KbAdobe PDF522View/Open

    All items in 政大典藏 are protected by copyright, with all rights reserved.

    社群 sharing

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback