自我和谐人格水平的语言结构信号预报  

Signal Prediction of the Structure of Language to SCC Personality Level

作　　者：徐芃[1] 熊健[2] 

机构地区：[1]广州大学心理咨询中心,广东广州510006 [2]广州大学经济与统计学院,广东广州510006

出　　处：《数理统计与管理》2014年第6期1030-1037,共8页Journal of Applied Statistics and Management

基　　金：教育部人文社会科学研究规划基金项目(11YJAZH106)

摘　　要：认知心理学认为语言表征是心理表征的符号现实,自我和谐是心理学人格理论中最重要的概念之一,语言表征也呈现多样化。通过相关分析法和顺序后退法在高维特征空间中进行主效应信号筛选,降低人工神经网络的输入维数。针对外显语言表征投射内隐心理状态的非线性特征,运用BP网络的高度非线性映射能力,对自我和谐人格水平进行分类诊断。结果表明:自我和谐人格水平的语言表征主效应信号诊断准确率达到95.8%。It is considered that linguistic representation is a kind of sign mapping psychological representation in cognition psychology research area. Self consistency and congruence （SCC） is one of most important concepts of psychology personality theory. And natural language people use is diversification also. Some important essential linguistic signals were chosen in the high dimension feature space by related analytic method and sequential backward selection （SBS） way. In view of nonlinear characteristic of linguistic signals mapping intrinsic mental and altitude mapping ability of BP neural network, the differential diagnosis to SCC was realized. This research demonstrated that the rate of accuracy of diagnosis of primary linguistic signals of SCC achieved 95.8%.

关 键 词：自我和谐 语言表征 BP网络 人格水平 

分 类 号：O212[理学—概率论与数理统计]