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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/131101


    Title: 新範例模型：相異性為基礎的分類
    New Exemplar Model: Categorization Based on Dissimilarity
    Authors: 姜柏安
    Chiang, Po-An
    Contributors: 楊立行
    姜柏安
    Chiang, Po-An
    Keywords: 類別學習
    範例理論
    相異性資訊
    Date: 2020
    Issue Date: 2020-08-03 17:56:20 (UTC+8)
    Abstract: 範例理論在分類領域一貫佔有主導地位，儘管如此，傳統範例模型仍因為未納入刺激間相異性資訊與必須使用每一個範例資訊而無法解釋部分分類行為表現。本研究以範例理論為基礎納入相異性與部分類別資訊分類的能力，提出增廣一般脈絡模型（augmented generalized context model, AGCM），並引用傳統範例模型如GCM與ALCOVE近年遇到的部分XOR問題挑戰為例，說明AGCM的優勢之處。Conaway與Kurtz（2016）發現在部分XOR問題（partial exclusive-or problem）中出現的外推分類現象，傳統範例模型無法提供解釋，但以類別的統計特性為表徵的發散自編碼模型（divergent autoencoder model, DIVA）可以說明此現象。研究一修改Conaway與Kurtz（2016）的實驗設計，於學習區塊間穿插對特定類別範例的重複呈現，結束後先進行再認作業，再進行分類的轉移階段。結果顯示強化類別內變異小的類別範例記憶有助於參與者習得接近性分類；而強化對類別內變異大的類別範例記憶有助於參與者習得外推分類，此結果為Conaway與Kurtz（2016）的發現提供了強而有力的範例表徵說明。研究二發現AGCM良好適配研究一所觀察到分類的平均趨勢與個別差異，估計參數的分配也符合單一類別資訊分類策略的預期。研究三檢驗AGCM是否對典型的類別問題有良好的解釋能力，結果發現AGCM在Nosofsky（1987）六個實驗結果的適配中，表現大多數都勝過GCM，確立AGCM作為範例模型的合理性。研究四試圖論證相異性於分類時的必要性。Stewart、Brown與Chater（2002）利用簡單知覺刺激結構發現類別對比效果，並以相異性說明類別對比效果的產生。研究四以AGCM進行電腦模擬，透過調整相異性加權參數得出與Stewart等人一致的類別對比效果。總結而言，本研究透過實驗證明範例理論不但可以解決部分XOR問題，且優於DIVA。補足過去傳統範例模型被攻擊之處，AGCM在少量增加模型複雜度的情況下能適恰解釋更多的分類表現。
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    Description: 碩士
    國立政治大學
    心理學系
    106752011
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106752011
    Data Type: thesis
    DOI: 10.6814/NCCU202001014
    Appears in Collections:[Department of Psychology] Theses

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