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顶叶皮层与不稳定和稳定学习中的信息粒度

Parietal cortex and information granularity in labile and stable learning.

作者信息

Wang Xiuzhen, Zhong Ning, Lu Shengfu, Liu Chunnian, Gu Weiquan

机构信息

The International WIC Institute bCollege of Computer Science, Beijing University of Technology, Beijing, China.

出版信息

Neuroreport. 2010 Jan 27;21(2):123-6. doi: 10.1097/WNR.0b013e328334f1ae.

Abstract

We investigated the effects of rule learning based on information granularity. Using two homogeneous Boolean arithmetic tasks, we examined parietal cortex activity during the calculation of labile and stabilized learning. The results revealed stability-related behavioral advantages in a comparison of granularity-based effects with labile learning of Boolean problems. The functional MRI results revealed that different regions within the parietal cortex exhibited increased activity while solving Boolean problems in both the conditions. The calculation of labile rule learning based on low-granularity Boolean rules was significantly correlated with activation in bilateral parietal cortex, whereas stable rule learning based on high-granularity Boolean rules was correlated with activation in the left parietal cortex.

摘要

我们研究了基于信息粒度的规则学习的效果。通过两项同质的布尔算术任务,我们考察了在不稳定学习和稳定学习计算过程中顶叶皮层的活动。结果显示,在基于粒度的效果与布尔问题的不稳定学习的比较中,存在与稳定性相关的行为优势。功能磁共振成像结果显示,在两种情况下解决布尔问题时,顶叶皮层内的不同区域均表现出活动增加。基于低粒度布尔规则的不稳定规则学习计算与双侧顶叶皮层的激活显著相关,而基于高粒度布尔规则的稳定规则学习与左侧顶叶皮层的激活相关。

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