Creative Problem Solving: A CLARION theory

被引:0
|
作者
Helie, Sebastien [1 ]
Sun, Ron [2 ]
机构
[1] Univ Calif Santa Barbara, Dept Psychol, Santa Barbara, CA 93106 USA
[2] Rensselaer Polytech Inst, Dept Cognit Sci, Troy, NY 12180 USA
来源
2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 | 2010年
关键词
IMPLICIT; KNOWLEDGE; EXPLICIT; INSIGHT; MEMORY;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Psychological theories of problem solving have largely focused on explicit processes that gradually bring the solver closer to the solution step-by-step in a mostly explicit and deliberative way. This approach to problem solving is typically inefficient or ineffective when the problem is too complex, ill-understood, or ambiguous. In such a case, a 'creative' approach to problem solving might be more appropriate. We propose a computational psychological model implementing the Explicit-Implicit Interaction theory of creative problem solving (i.e., the CLARION theory of creative problem solving) that centers on the interaction of implicit and explicit processing. The model based on the CLARION theory has been used to simulate a variety of empirical psychological data sets.
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页数:7
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