A Study of the Mental Lexical Structures of Late Chinese-English Bilinguals Based on Complex Network Analysis

doi:10.12002/j.bisu.504

Abstract

Abstract:

This study employed semantic fluency task and complex network analysis to investigate the lexical structures of late Chinese-English bilinguals. By obtaining network visualization and network parameters, including average nodes, average degree, average clustering coefficient, average shortest-path length, small world index, and modularity index, this study characterized the lexical structure from the perspectives of structural features and structural diff erence. First, it was found that the bilinguals’ lexicons were small-world and modular in nature. Second, compared with the L2 lexical network, the L1 counterpart was better organized, which may be attributed to the influence of L2 proficiency and the age of L2 acquisition. Third, the results concerning how words within the network are connected supported the hypothesis of the spreading activation model. Based on the study’s fi ndings, the strategies recommended for teaching English vocabulary in China include emphasizing alignment with learners’ developing mental lexicon and using methods that directly connect L2 vocabulary with images or real objects to enhance dynamic L2 vocabulary growth.

Keywords: complex network analysis; semantic fl uency task; mental lexicon; late Chinese-English bilinguals; bilingual lexicon

摘要：

本研究结合语义流畅任务和复杂网络分析，探索晚期汉英双语者的双语词库结构，通过构建词汇关联网络、计算网络参数（网络节点数、边数、平均度、平均聚类系数、平均最短路径、小世界属性参数和模块化参数）和生成网络可视化图，考察词库结构的特征与差异。结果发现：①双语词库具有小世界属性和模块化属性；②L1词汇网络比L2词汇网络结构更为优化，这与被试的L2使用频次和习得年龄相关；③词汇网络节点的组织方式在一定程度上支持激活扩散模型。本文据此为中国英语词汇教学提出相关建议：顺应学习者心理词库结构的发展趋势，采用L2词汇与图片或实物直接相连的教学方法，以促进L2词库结构的动态发展。

关键词: 复杂网络分析, 语义流畅任务, 心理词库, 晚期汉英双语者, 双语词库

CLC Number:

H04/H3

Chen Yao, Zhou Rong. A Study of the Mental Lexical Structures of Late Chinese-English Bilinguals Based on Complex Network Analysis[J]. Journal of Beijing International Studies University, 2024, 46(1): 142-160.

陈瑶, 周榕. 基于复杂网络分析的晚期汉英双语者心理词库结构探究[J]. 北京第二外国语学院学报, 2024, 46(1): 142-160.

Figures/Tables 6

References 44

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网络类型	N	E	K	CC	CCran	ASPL	ASPLran	S	Q
英语 - 动物	26	293	22.538	0.917	0.867	1.098	1.361	1.310	0.696
英语 - 蔬果	26	323	24.846	0.994	0.956	1.006	1.334	1.380	0.690
汉语 - 动物	45	947	42.098	0.963	0.935	1.043	1.364	1.345	0.701
汉语 - 蔬果	42	836	39.810	0.974	0.948	1.029	1.358	1.356	0.713

网络类型	N	E	K	CC	CCran	ASPL	ASPLran	S	Q
英语 - 动物	26	293	22.538	0.917	0.867	1.098	1.361	1.310	0.696
英语 - 蔬果	26	323	24.846	0.994	0.956	1.006	1.334	1.380	0.690
汉语 - 动物	45	947	42.098	0.963	0.935	1.043	1.364	1.345	0.701
汉语 - 蔬果	42	836	39.810	0.974	0.948	1.029	1.358	1.356	0.713

网络类型	平均聚类系数	平均最短路径	模块化参数	平均度
英语 - 动物	0.497	1.500	0.096	12.538
英语 - 蔬果	0.497	1.502	0.096	11.923
汉语 - 动物	0.499	1.499	0.078	23.111
汉语 - 蔬果	0.500	1.499	0.079	21.476

网络类型	平均聚类系数	平均最短路径	模块化参数	平均度
英语 - 动物	0.497	1.500	0.096	12.538
英语 - 蔬果	0.497	1.502	0.096	11.923
汉语 - 动物	0.499	1.499	0.078	23.111
汉语 - 蔬果	0.500	1.499	0.079	21.476