Individual differences in representational similarity of first and second languages in the bilingual brain

Abstract

Current theories of bilingualism disagree on the extent to which separate brain regions are used to maintain or process one’s first and second language. The present study took a novel multivariate approach to address this question. We examined whether bilinguals maintain distinct neural representations of two languages; specifically, we tested whether brain areas that are involved in processing word meaning in either language are reliably representing each language differently, and whether language representation is influenced by individual differences in proficiency level and age of acquisition of L2. Thirty-one English-Mandarin bilingual adults performed a picture-word matching task in both languages. We then used representational similarity analysis to examine which brain regions reliably showed different patterns of activity for each language. As a group, there were no regions that reliably represented languages distinctly. However, both proficiency and age of acquisition predicted dissimilarity between language representations in several brain areas within the language network as well as several regions of the ventral visual pathway, demonstrating that top-down language knowledge and individual language experience shapes concept representation in the processing stream. The results support the model of an integrated language system in bilinguals, along with a novel description of how representations for each language change with proficiency level and L2 age of acquisition.

Enhancing the network specific individual characteristics in rs‐fMRI functional connectivity by dictionary learning

“…The MTL‐sCASO method defines a term to encompass the effect of the trade‐off between the similar network topology generally shared among individuals and the inter‐individual variability in estimating the brain network during rest. On the other hand, there are studies that have focused on a specific attribute such as, attention (Rosenberg et al, 2016 ), gender (Smith et al, 2014 ), procrastination (Wu et al, 2016 ), age (Geerligs et al, 2015 ), along with intelligence (Levakov et al, 2021 ) finding individual differences in bilingual individuals (Nichols et al, 2021 ), and predicting individual differences in propensity to trust (Lu et al, 2019 ).…”

“…Several studies have attempted to extract individual differences at the FC level. Researchers have used similarity measures such as Pearson correlation between FCs computed by two or more scans of the same subject (Finn et al, 2015 , 2017 ; Kraus et al, 2021 ) others compute them with algorithms that either exploit the sparse nature of the FC (Wang et al, 2021 ) or use analysis techniques like representational similarity analysis (Nichols et al, 2021 ). There are other studies that use machine learning‐based methods like Amico and Goñi ( 2018 ) that demonstrate how principal component analysis (PCA) can enhance the individual differences and Qin et al ( 2019 ) showed how low‐rank learning algorithms like Robust PCA (RPCA) could also improve these differences.…”