Contributions of common genetic variants to specific languages and to when a language is learned

Wong, Patrick C. M.; Kang, Xin; So, Hon‐Cheong; Choy, Kwong Wai

doi:10.1038/s41598-021-04163-1

Cited by 8 publications

(6 citation statements)

References 88 publications

(75 reference statements)

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“…33 SES did not predict language development in our earlier study of infants and toddlers. 17 It also did not predict first language (L1) (Chinese) development in studies of kindergarteners, 34 school-age children, 35 and university students 36 in Hong Kong. Because SES is widely reported to be a predictor of (first) language development in studies outside of Hong Kong (see Dailey et al 37 for a review), we used SES as a predictor variable in the model with the largest number of participants as well.…”

Section: Predictor Variablesmentioning

confidence: 88%

Infant Neural Speech Encoding Forecasts Language Difficulties in Preschool Years

Wong,

Pan,

Lai

et al. 2024

Preprint

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Importance: Developmental language problems must be detected early to capitalize on the high effectiveness of early intervention. Because developmental language problems are not formally diagnosed until preschool years, a method must be developed and validated to forecast language problems from as early as infancy. Objective: To develop and validate predictive models of language problems with neural data collected from as early as infancy and language problems detected up to 36 months of age.Design, Setting, & Participants: Electroencephalography (EEG) neural encoding of speech, gestational age, birth weight, birth sex, and language outcome data were collected from 439 healthy children from a community sample. Ages and Stages Questionnaire data were also collected from a subset of children. EEG was collected between 3 weeks to 24 months of age and language outcome up to 36 months of age.Exposure: All children underwent EEG testing in which they listened to three speech stimuli (two native and one non-native) and early-latency and long-latency responses were collected. No intervention was involved in the study.Main Outcome Measures: The Chinese Communicative Development Inventory – Cantonese version (CCDI-C) and the language subscale of the Bayley Scales of Infant and Toddler Development, third edition (Bayley-III) were collected between 7 to 36 months of age. Results: Random Forest was used as machine learning algorithm to classify children into binary groups based on a percentile-rank cut-off on the outcome measures (e.g., below or above the 16th percentile). Different predictive models were developed and compared, including those with and without EEG and clinical measures. Models with non-neural measures (e.g., gestational age and birth weight) predicted language outcome above chance level. Models with EEG measures alone outperformed any models without neural features, with sensitivity and Area Under Curve (AUC) scoring well above 90% for the best models. When validated with unseen data, both sensitivity and AUC remained at 90%.Conclusion & Relevance: EEG neural encoding of speech can be a new procedure for screening language developmental problems as early as infancy. Families with children who are screened positive may consider early intervention in the form of parent coaching to promote better language development.

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Section: Predictor Variablesmentioning

confidence: 88%

Infant Neural Speech Encoding Forecasts Language Difficulties in Preschool Years

Wong,

Pan,

Lai

et al. 2024

Preprint

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“…For any measurable trait, physical or psychological, there is enormous variability across individuals, reflecting an interaction between the environment and the genetic and epigenetic makeup of each individual. This interaction is particularly pronounced for language: while humans are genetically endowed with the ability the acquire language, the specific language(s) learned by a person is determined by their environment ( Wong et al, 2022 ). Individual differences are pervasive across the language system and are related to environmental variables (such as the quantity and quality of the language that an individual is exposed to) and to individual differences in other cognitive functions, such as working memory, executive function, and statistical learning, reviewed in ( Kidd et al, 2018 ; Shinn-Cunningham, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

Multivariate fMRI responses in superior temporal cortex predict visual contributions to, and individual differences in, the intelligibility of noisy speech

et al. 2023

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“…We encourage researchers to use this database for further analysis and publication. Relevant data have been used in previous publications 36 , 37 .…”

Section: Usage Notesmentioning

confidence: 99%

A large-scale repository of spoken narratives in French, German and Spanish from Cantonese-speaking learners

et al. 2023

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Interdisciplinary research on foreign language learning has important implications for learning and education. In this paper, we present the Repository of Third Language (L3) Spoken Narratives from Modern Language Learners in Hong Kong (L3HK Repository). This database contains 906 audio recordings and annotated transcripts of spoken narratives in French, German, and Spanish that were elicited from Cantonese-speaking (L1) young adults using a wordless picture book, “Frog, Where Are You?”. All participants spoke English as the second language (L2) and learned the target language as a third language (L3). We collected their demographic information, answers to a motivation questionnaire, parental socioeconomic status, and music background. Furthermore, for a subset of participants, we collected their L1 and L2 proficiency scores and additional experimental data on working memory and music perception. This database is valuable for examining cross-sectional changes in foreign language learning. The extensive data on phenotypes provide opportunities to explore learner-internal and learner-external factors in foreign language learning outcomes. These data may also be helpful for those who work on speech recognition.

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Contributions of common genetic variants to specific languages and to when a language is learned

Cited by 8 publications

References 88 publications

Infant Neural Speech Encoding Forecasts Language Difficulties in Preschool Years

Infant Neural Speech Encoding Forecasts Language Difficulties in Preschool Years

Multivariate fMRI responses in superior temporal cortex predict visual contributions to, and individual differences in, the intelligibility of noisy speech

A large-scale repository of spoken narratives in French, German and Spanish from Cantonese-speaking learners

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