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Quantifying Sources of Variability in Infancy Research Using the Infant-Directed-Speech Preference
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Perceptual Connectivity Influences Toddlers’ Attention to Known Objects and Subsequent Label Processing
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In: Brain Sci (2021)
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Moving towards accurate and early prediction of language delay with network science and machine learning approaches
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In: Sci Rep (2021)
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Abstract:
Due to wide variability of typical language development, it has been historically difficult to distinguish typical and delayed trajectories of early language growth. Improving our understanding of factors that signal language disorder and delay has the potential to improve the lives of the millions with developmental language disorder (DLD). We develop predictive models of low language (LL) outcomes by analyzing parental report measures of early language skill using machine learning and network science approaches. We harmonized two longitudinal datasets including demographic and standardized measures of early language skills (the MacArthur-Bates Communicative Developmental Inventories; MBCDI) as well as a later measure of LL. MBCDI data was used to calculate several graph-theoretic measures of lexico-semantic structure in toddlers’ expressive vocabularies. We use machine-learning techniques to construct predictive models with these datasets to identify toddlers who will have later LL outcomes at preschool and school-age. This approach yielded robust and reliable predictions of later LL outcome with classification accuracies in single datasets exceeding 90%. Generalization performance between different datasets was modest due to differences in outcome ages and diagnostic measures. Grammatical and lexico-semantic measures ranked highly in predictive classification, highlighting promising avenues for early screening and delineating the roots of language disorders.
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8047042/ https://doi.org/10.1038/s41598-021-85982-0
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Lexical recognition in deaf children learning ASL: activation of semantic and phonological features of signs
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Quantifying Sources of Variability in Infancy Research Using the Infant-Directed-Speech Preference
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In: ISSN: 2515-2459 ; EISSN: 2515-2467 ; Advances in Methods and Practices in Psychological Science ; https://hal-univ-rennes1.archives-ouvertes.fr/hal-02509817 ; Advances in Methods and Practices in Psychological Science, [Thousand Oaks]: [SAGE Publications], 2020, 3 (1), pp.24-52. ⟨10.1177/2515245919900809⟩ (2020)
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Quantifying Sources of Variability in Infancy Research Using the Infant-Directed-Speech Preference
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In: ADVANCES IN METHODS AND PRACTICES IN PSYCHOLOGICAL SCIENCE, vol 3, iss 1 (2020)
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When slowing down processing helps learning: Lexico-semantic structure supports retention but interferes with disambiguation of novel object-label mappings
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In: Dev Sci (2020)
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Lexical Recognition in Deaf Children Learning American Sign Language: Activation of Semantic and Phonological Features of Signs
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In: Lang Learn (2020)
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Quantifying sources of variability in infancy research using the infant-directed-speech preference
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Dataset for Vocabulary size and structure affects real-time lexico-semantic recognition in 18-month-olds ...
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Developmental Timescale of Rapid Adaptation to Conflicting Cues in Real-Time Sentence Processing
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Modeling Early Lexico-Semantic Network Development: Perceptual Features Matter Most
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Vocabulary size and structure affects real-time lexical recognition in 18-month-olds
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Vocabulary size and Native Speaker self-identification influence flexibility in linguistic prediction among adult bilinguals
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Prediction in a visual language: real-time sentence processing in American Sign Language across development
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Prediction in a visual language: real-time sentence processing in American Sign Language across development ...
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Prediction in a visual language: real-time sentence processing in American Sign Language across development ...
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Prediction in a visual language: real-time sentence processing in American Sign Language across development
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Lexical leverage: category knowledge boosts real-time novel word recognition in 2-year-olds.
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In: Developmental science, vol 19, iss 6 (2016)
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