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运用网络科学描绘蒙特利尔双语者在跨语言及交际语境下的谈话内容。

Using Network Science to Map What Montréal Bilinguals Talk about Across Languages and Communicative Contexts.

作者信息

Tiv Mehrgol, Gullifer Jason, Feng Ruo, Titone Debra

机构信息

Department of Psychology, McGill University.

出版信息

J Neurolinguistics. 2020 Nov;56. doi: 10.1016/j.jneuroling.2020.100913. Epub 2020 Jul 29.

Abstract

Recent work within the language sciences, particularly bilingualism, has sought new methods to evaluate and characterize how people differentially use language across different communicative contexts. These differences have thus far been linked to changes in cognitive control strategy, reading behavior, and brain organization. Here, we approach this issue using a novel application of Network Science to map the conversational topics that Montréal bilinguals discuss across communicative contexts (e.g., work, home, family, school, social), in their dominant vs. non-dominant language. Our results demonstrate that all communicative contexts display a unique pattern in which conversational topics are discussed, but only a few communicative contexts (work and social) display a unique pattern of how many languages are used to discuss particular topics. We also demonstrate that the dominant language has greater network size, strength, and density than the non-dominant language, suggesting that more topics are used in a wider variety of contexts in this language. Lastly, using community detection to thematically group the topics in each language, we find evidence of greater specificity in the non-dominant language than the dominant language. We contend that Network Science is a valuable tool for representing complex information, such as individual differences in bilingual language use, in a rich and granular manner, that may be used to better understand brain and behavior.

摘要

语言科学领域,尤其是双语研究领域的近期工作,一直在探寻新方法,以评估和描述人们在不同交际语境中如何差异化地使用语言。迄今为止,这些差异已与认知控制策略、阅读行为和大脑组织的变化联系起来。在此,我们运用网络科学的一种新颖应用来处理这个问题,以绘制蒙特利尔双语者在不同交际语境(如工作、家庭、学校、社交)中,使用其优势语言和非优势语言讨论的对话主题。我们的结果表明,所有交际语境都呈现出一种独特的对话主题讨论模式,但只有少数交际语境(工作和社交)呈现出使用多少种语言来讨论特定主题的独特模式。我们还表明,优势语言比非优势语言具有更大的网络规模、强度和密度,这表明该语言在更广泛的语境中使用了更多主题。最后,通过使用社区检测方法按主题对每种语言中的主题进行分组,我们发现非优势语言比优势语言具有更高的特异性。我们认为,网络科学是一种有价值的工具,能够以丰富且细致的方式呈现复杂信息,比如双语语言使用中的个体差异,这可能有助于更好地理解大脑与行为。

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