The Role of Posterior Parietal Cortex in Problem Representation

被引:0
|
作者
Xiang, Jie [1 ,2 ]
Qin, Yulin [1 ,3 ]
Chen, Junjie [2 ]
Zhou, Haiyan [1 ]
Li, Kuncheng [4 ]
Zhong, Ning [1 ,5 ]
机构
[1] Beijing Univ Technol, Int WIC Inst, Beijing, Peoples R China
[2] Taiyuan Univ Technol, Coll Comp & Software, Taiyuan, Peoples R China
[3] Carnegie Mellon Univ, Dept Psychol, Pittsburgh, PA USA
[4] Xuanwu Hosp Capital Univ Med Sci, Dept Radiol, Beijing, Peoples R China
[5] Maebashi Inst Technol, Dept Life Sci & Informat, Maebashi, Gunma, Japan
来源
BRAIN INFORMATICS, BI 2010 | 2010年 / 6334卷
关键词
TOWER-OF-LONDON; PERFORMANCE; FMRI; SCHIZOPHRENIA; ACTIVATION; PET;
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
Problem representation is one of the key factors in problem solving. According to previous studies, PPC (Posterior Parietal Cortex) is critical for problem representation. Whether does problem expression form affect problem representation? What are the cognitive role of PPC in representation? In order to answer these questions, a fMRI experiment was performed in this study to examine the role of PPC in problem solving. It was a 2 x 2 designed experiment with two 2-level factors: task complexity (one-step and two-steps) and expression form (digits and symbols). In a digital task, 4 digits are provided in the initial grids, while 4 symbols of poker are provided in a symbolic task. In a task of one-step, participants only need one time rules retrieving to get the target answer, while in a task of two-steps, participants need two times rules retrieving to get the answer of target after getting the answer of a bridging location. The results of fMRI show that PPC activated significantly. The further analysis shows that there is a positive correlation between the activation intensity of PPC and task complexity, but the correlation between the activation intensity of PPC and task expression is not significant. According these results, we infer that PPC plays an important role in problem representation, maybe this representation is a high level abstraction.
引用
收藏
页码:417 / 426
页数:10
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