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https://ah.lib.nccu.edu.tw/handle/140.119/59704| 題名: | 類別學習的個別差異分析：以潛在剖面技術為例 Use of latent profile analysis to identify individual difference phenotypes in categorical learning |
作者: | 鍾德政 | 貢獻者: | 楊立行 鍾德政 |
關鍵詞: | 類別學習 個別差異 潛在剖面分析 |
日期: | 2010 | 上傳時間: | 3-Sep-2013 | 摘要: | 自1990年開始，有越來越多的研究者開始重視類別學習表現的個別差異，即不同的受試者為何會在相同的分類作業中使用不同的策略。在過去，我們透過知識分化的現象去瞭解人們個別差異的分別，個別差異的分析可以幫助我們瞭解人與人之間的變異性與獨特性，然而，隨著這方面的研究逐漸增多，個別差異因素對於心理歷程的重要性不斷的被放大，但在過去類別學習的研究中對於個別差異的分析仍然一直沒有太深入的探討，主要是因為個別差異的分析結果，充滿了太多的不確定性以及未知可能性。在上述的狀況下，使用過去研究常用的分類方法，如非階層群聚分析中的k-means群聚分析法，必須先設定要分類組別數情狀下，未知可能性的組別可能就會被忽略，甚至被硬性併到其他組別，不但導致整體資料的遺失，嚴重的情況下還會使研究的效果無法突出。\n為了避免過去分類時分析方法的缺點，所以本研究使用潛在剖面分析幫助分類學習中知識分化的分組，潛在剖面分析並不像K-Means聚類法與階層式聚類法一開始就要決定組別數目，而是取決於不同的組數數目時誰有最佳的適合度統計量。 相信使用潛在剖面分析可以避免知識分化中未知可能性組別的遺失，此點已在本研究的三個分析中可以得到證實。\n 本研究分別對於幾種不同類型的類別學習作業進行潛在剖面分析，透過研究中的四個實驗，以期開發出一套適合分析分類策略對於個別差異影響的方法。企圖證明在不同類型的實驗結構下，依然能使用潛在剖面分析來分類，且分類的結果可以找出適當的分組以及發現實驗未預期的組別。並結合結構方程混合模型，進一步去釐清在知識分化現象中，影響分類策略選擇的因子。 | 參考文獻: | 邱皓政。（民97年）。潛在類別模式：原理與技術。台北市：五南。\n俞信安。（民96年）。分類研究中的自發性知識分化現象。國立中正大學心理學研究所碩士論文，未出版，嘉義縣。\n楊立行。（民96年）。分類學習與工作記憶。未發表之國科會專題計畫。\n蔡涵如。（民97年）。工作記憶與類別學習中的知識分化現象。國立成功大學認知科學研究所碩士論文，未出版，台南市。\nAshby, F. G., & Maddox, W. T.(2005). Human category learning. 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Individual differences in working memory capacity and learning: Evidence from serial reaction time task. Memory & Cognition, 33, 213-220.\nYang, L.-X., & Lewandowsky, S. (2003). Context-gated knowledge partitioning in categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 663-679.\nYang, L.-X., & Lewandowsky, S. (2004). Knowledge partitioning in categorization: constraints on exemplar models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 1045-1064. | 描述: | 碩士 國立政治大學 心理學研究所 97752011 99 |
資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0097752011 | 資料類型: | thesis |
| Appears in Collections: | 學位論文 |
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