English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 109952/140903 (78%)
Visitors : 46041292      Online Users : 1060
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
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/120931


    Title: Demonstration of Cognitive Modeling in Categorization: Fitting two neural network models to the data from Yang and Lewandowsky (2003) study.
    認知模擬在類別學習上的應用：以Yang與Lewandowsky (2003)之研究為例
    Authors: Yang, Lee-Xieng
    楊立行
    Contributors: 心腦學中心
    Keywords: cognitive modeling;categorization;neural netw
    認知模擬;類別學習;類神經網路
    Date: 2007-09
    Issue Date: 2018-11-20 17:12:25 (UTC+8)
    Abstract: In investigating human mental processes and mental representations, a cognitive model represents a theoretical view, provides explanations to the observed phenomena and makes predictions about an unknown future. When evaluating how well a theory can account for the phenomenon of interest, modeling is a powerful research tool. However, local (Taiwanese) psychology students have limited exposure to what cognitive modeling is, how to do implement cognitive models, and why cognitive modelling is important. This is partly due to a lack of university courses that teach cognitive modelling and partly due to the demands that modelling places on one`s skills. The purpose of this article is to provide a conceptual guideline of how to do modeling, by fitting two neural network models-ALCOVE and ATRIUM to the data from the study of Yang and Lewandowsky (2003), which tested the theoretical concept of knowledge partitioning in categorization. The modeling results show that ATRIUM outperforms ALCOVE in accounting for the knowledge partitioning results. Some relevant theoretical-level discussions, such as the heterogeneity of categorization, are also included.
    為了探索人類心理歷程與心智表徵，各種不同的認知模型不斷地被研究者提出。這些認知模型代表著不同的理論觀點，它們不僅可以對現象提出解釋，還可以對未知進行預測。當我們想要檢視一個理論模型對於現象的可以達到多好的解釋力，以實徵資料進行電腦模擬就成為了一項強而有力的研究工具。然而本地（台灣）的心理學背景的學生往往缺少學習這項工具的管道，而不清楚什麼是電腦模擬、不知道如何進行，不了解它的重要性何在。這部分可能源自於心理系所鮮少開設相關的課程，也或者是它需要較高的程式設計能力。因此，本文目的在於提供進行認知模擬的概念性引導方針：文中將首先介紹兩個在類別學習領域上相當知名的類神經網路模型ALCOVE與ATRIUM，並以Yang與Lewandosky(2003)知識分化的研究為例進行電腦模擬，透過此二模型與實徵資料的分別比對結果，進一步對此二模型背後所支持的理論進行比較分析。模擬結果顯示，ATRIUM對於在類別學習上的知識分化現象的解釋力明顯高於ALCOVE。此外，一些相關的理論層次的議題，如分類表徵的異質性等，也因為獲得模擬的結果而能夠被更深入的討論。
    Relation: Chinese Journal of Psychology, Vol.49, pp.285-300
    中華心理學刊
    Data Type: article
    DOI 連結: http://dx.doi.org/10.6129/CJP.2007.4903.04
    DOI: 10.6129/CJP.2007.4903.04
    Appears in Collections:[心智‧大腦與學習研究中心 ] 會議論文

    Files in This Item:

    File Description SizeFormat
    285-300.pdf1522KbAdobe PDF2290View/Open


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


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback