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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
Contributors: 心腦學中心
Keywords: cognitive modeling;categorization;neural netw
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.
Relation: Chinese Journal of Psychology, Vol.49, pp.285-300
Data Type: article
DOI 連結:
Appears in Collections:[心智‧大腦與學習研究中心 ] 會議論文

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