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Bridging Interpersonal and Ecological Dynamics of Cognition through a Systems Framework of Bilingualism

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Abstract

Human cognition occurs within social contexts, and nowhere is this more evident than language behavior. Regularly using multiple languages is a globally ubiquitous, individual experience that is shaped by social environmental forces, ranging from interpersonal interactions to ambient language exposure. Here, we develop a Systems Framework of Bilingualism, where embedded layers of individual, interpersonal, and ecological sociolinguistic factors jointly predict people’s language behavior. Of note, we quantify interpersonal and ecological language dynamics through the novel applications of language-tagged social network analysis and geospatial demographic analysis among 106 English-French bilingual adults in Montréal, Canada. Consistent with a Systems view, we found that people’s individual language behavior, on a global level (i.e., overall language use), was jointly predicted by the language characteristics of their interpersonal social networks and the ambient linguistic patterns of their residential neighborhood environments, whereas more granular aspects of language behavior (i.e., word-level proficiency) was mainly driven by local, interpersonal social networks. Together, this work offers a novel theoretical framework, bolstered by innovative analytic techniques to quantify complex social information and empower more holistic assessments of multifaceted human behaviors and cognition, like language.

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... Additionally, legacy data from the LHQ and other questionnaires could be combined across labs to provide preliminary answers to some of the systems framework's queries (e.g., how robust are language measures across the globe and age ranges, etc.; Li, Zhang, Yu & Zhao, 2020). Well-formed social network questionnaires probing multiple levels of the systems framework also offer a solution: from the individual level, the interpersonal level, and possibly parts of the ecological level; and data from these levels can be joined together as a multilevel/hierarchical dataset (e.g., Collective, 2016;Tiv et al., 2021; see Fig 1D). ...
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