Bilingual Experience and Executive Control over the Adult Lifespan: The Role of Biological Sex

Subramaniapillai, Sivaniya; Rajah, M. Natasha; Pasvanis, Stamatoula; Titone, Debra

doi:10.31219/osf.io/tsr36

Cited by 6 publications

(9 citation statements)

References 25 publications

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“…We extracted classic measures of L2 exposure, such as L2 AoA (based on the onset of learning) and global exposure to the L1, L2, and L3 (third language). Global L2 exposure is frequently used as a covariate in the MLL lab (e.g., Pivneva et al, 2014; Subramaniapillai et al, 2018), and these global exposure measures did not factor in to the computation of language diversity to allow for a comparison of the measures.…”

Section: Methodsmentioning

confidence: 99%

“…Bilingual and multilingual individuals vary widely in their exposure to, and socially distributed use of, language, particularly if they live in highly multilingual locations. Individual differences in static language experiences, such as age of language acquisition (AoA; e.g., Flege, Munro & MacKay, 1995; Gullifer, Chai, Whitford, Pivneva, Baum, Klein & Titone, 2018; Kousaie, Chai, Sander & Klein, 2017; Luk, De Sa & Bialystok, 2011; Piske, MacKay & Flege, 2001; Subramaniapillai, Rajah, Pasvanis & Titone, 2018; Titone, Libben, Mercier, Whitford & Pivneva, 2011), and current language experiences, such as amount of second language (L2) exposure (e.g., Gullifer et al, 2018; Hartanto & Yang, 2016; Hofweber, Marinis & Treffers-Daller, 2016; Jylkkä, Soveri, Wahlström, Lehtonen, Rodriguez-Fornells & Laine, 2017; Pivneva, Mercier & Titone, 2014; Prior & Gollan, 2011; Subramaniapillai et al, 2018; Titone, Gullifer, Subramaniapillai, Rajah & Baum, 2017), drive linguistic performance and executive control abilities. However, there is not yet consensus on the best practices for measuring current language experience (Baum & Titone, 2014; Gollan, Weissberger, Runnqvist, Montoya & Cera, 2012; Surrain & Luk, 2017; Takahesu Tabori, Mech & Atagi, 2018; Tomoschuk, Ferreira & Gollan, 2018) despite its theoretical importance (Abutalebi & Green, 2016; Green & Abutalebi, 2013), in large part because bilingualism and bilingual experience are not homogenous constructs.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing the social diversity of bilingualism using language entropy

2019

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Bilingual and multilingual individuals exhibit variation in everyday language experience. Studies on bilingualism account for individual differences with measures such as L2 age of acquisition, exposure, or language proficiency, but recent theoretical perspectives posit that the relative balance between the two or more languages throughout daily life (i.e., interactional context) is a crucial determinant for language representation, access, and control. We propose an innovative measure to characterize this construct by using entropy to estimate the social diversity of language use. Language entropy is computed from commonly-collected language history data and generalizes to multilingual communicative contexts. We show how language entropy relates to other indices of bilingual experience and that it predicts self-report L2 outcome measures over and above classic measures of language experience. Thus, we proffer language entropy as a means to characterize individual differences in bilingual (and multilingual) language experience related to the social diversity of language use.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterizing the social diversity of bilingualism using language entropy

2019

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“…To investigate whether effects of CMRs and APOE risk varied across different age groups, we categorised participants' ages (i.e., Age Group) using the following bins: 45-55 years, 56-65 years, and 66-82 years (Table 2). The bins were selected to take the full cohort age range into account, and to enable comparisons of effects in middle-age and different stages of older adulthood in line with previous studies examining participants throughout the adult lifespan [80,81,82]. Distributions for BMI, WHR, and BF% within each Age Group are shown in SI Figure 2.…”

Section: Categorising Groups Based On I) Age At Menopause and Ii) Chronological Agementioning

confidence: 99%

“…In this study, we examined the associations between WM BAG and key CMRs, including BMI, WHR, and BF% [77,78,79], and APOE4 status in males (N = 10,605) and females (N = 10,703). We further assessed whether these risk factors had salient effects in middle age (44-55yrs) and different stages of older adulthood (56-65yrs and 66-82yrs) [80,81,82].…”

Section: Introductionmentioning

confidence: 99%

Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

Subramaniapillai¹,

Suri²,

Barth³

et al. 2021

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Cardiometabolic risk factors (CMRs) are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer’s Disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ε4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied i) between males and females, ii) according to age at menopause in females, and iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males com- pared to females. Higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66-81yrs), where higher BF% was linked to lower BAG (younger brain age relative to chronological age). Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. APOE4 status was not significantly associated with BAG, and no significant interactions with CMRs were found. In conclusion, the findings point to sex- and age-specific associations between body fat composition and brain age. Incorporating sex as a factor of interest in studies addressing cardiometabolic risk may promote sex-specific precision medicine, consequently improving health care for both males and females.

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“…Selective survival may be occurring in this subset of women who engage in reserve factors that increase resistance in women more than men. Although our review did not find studies that considered sex differences in all of the reserve factors considered, studies do demonstrate the importance of education (Letteneur et al, 2000, Oliviera et al, 2016, Koran et al, 2017, bilingualism (Subramaniapillai et al, 2018), and physical activity (Baker et al, 2010;Barha et al, 2017;Colcombe and Kramer, 2003;Van Uffelen et al, 2008) to women's resistance to age-related cognitive decline.…”

Section: Discussionmentioning

confidence: 70%

Sex differences in cognitive reserve: implications for Alzheimer’s Disease in women

Subramaniapillai¹,

Almey²,

Mn³

et al. 2020

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Women represent ⅔ of the cases of Alzheimer’s disease (AD). Current research has focused on differential risks to explain higher rates of AD in women. However, factors that reduce risk for AD, like cognitive reserve, are less well explored. We asked: what is known about sex differences in how cognitive reserve mitigates risk for AD? To address this, we conducted a narrative review of the literature. Keywords were: “sex/gender differences”, “cognitive/brain reserve”, “Alzheimer’s Disease”, and the following cognitive reserve contributors: “education”, “IQ”, “occupation”, “cognitive stimulation”, “bilingualism”, “socioeconomic status”, “physical activity”, “social support”. Fifteen papers disaggregated their data by sex. Those papers observed sex differences in cognitive reserve contributors. There is also evidence that a subset of women may have greater resistance to AD, possibly due to greater cognitive reserve. We discuss how traditional cognitive reserve contributors are gendered and may not capture factors that support cognition in aging women.

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Bilingual Experience and Executive Control over the Adult Lifespan: The Role of Biological Sex

Cited by 6 publications

References 25 publications

Characterizing the social diversity of bilingualism using language entropy

Characterizing the social diversity of bilingualism using language entropy

Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

Sex differences in cognitive reserve: implications for Alzheimer’s Disease in women

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