TY - JOUR

T1 - Caregiver linguistic alignment to autistic and typically developing children

T2 - A natural language processing approach illuminates the interactive components of language development

AU - Fusaroli, Riccardo

AU - Weed, Ethan

AU - Rocca, Roberta

AU - Fein, Deborah

AU - Naigles, Letitia

PY - 2023/7

Y1 - 2023/7

N2 - BACKGROUND: Language development is a highly interactive activity. However, most research on linguistic environment has focused on the quantity and complexity of linguistic input to children, with current models showing that complexity facilitates language in both typically developing (TD) and autistic children.AIMS: After reviewing existing work on caregiver engagement of children's utterances, we aim to operationalize such engagement with automated measures of linguistic alignment, thereby providing scalable tools to assess caregivers' active reuse of their children's language. By assessing the presence of alignment, its sensitivity to the child's individual differences and how well it predicts language development beyond current models across the two groups, we showcase the usefulness of the approach and provide initial empirical foundations for further conceptual and empirical investigations.METHODS: We measure lexical, syntactic and semantic types of caregiver alignment in a longitudinal corpus involving 32 adult-autistic child and 35 adult-TD child dyads, with children between 2 and 5 years of age. We assess the extent to which caregivers repeat their children's words, syntax, and semantics, and whether these repetitions predict language development beyond more standard predictors.RESULTS: Caregivers tend to re-use their child's language in a way that is related to the child's individual, primarily linguistic, differences. Caregivers' alignment provides unique information improving our ability to predict future language development in both typical and autistic children.CONCLUSIONS: We provide evidence that language development also relies on interactive conversational processes, previously understudied. We share carefully detailed methods, and open-source scripts so as to systematically extend our approach to new contexts and languages.

AB - BACKGROUND: Language development is a highly interactive activity. However, most research on linguistic environment has focused on the quantity and complexity of linguistic input to children, with current models showing that complexity facilitates language in both typically developing (TD) and autistic children.AIMS: After reviewing existing work on caregiver engagement of children's utterances, we aim to operationalize such engagement with automated measures of linguistic alignment, thereby providing scalable tools to assess caregivers' active reuse of their children's language. By assessing the presence of alignment, its sensitivity to the child's individual differences and how well it predicts language development beyond current models across the two groups, we showcase the usefulness of the approach and provide initial empirical foundations for further conceptual and empirical investigations.METHODS: We measure lexical, syntactic and semantic types of caregiver alignment in a longitudinal corpus involving 32 adult-autistic child and 35 adult-TD child dyads, with children between 2 and 5 years of age. We assess the extent to which caregivers repeat their children's words, syntax, and semantics, and whether these repetitions predict language development beyond more standard predictors.RESULTS: Caregivers tend to re-use their child's language in a way that is related to the child's individual, primarily linguistic, differences. Caregivers' alignment provides unique information improving our ability to predict future language development in both typical and autistic children.CONCLUSIONS: We provide evidence that language development also relies on interactive conversational processes, previously understudied. We share carefully detailed methods, and open-source scripts so as to systematically extend our approach to new contexts and languages.

U2 - 10.1016/j.cognition.2023.105422

DO - 10.1016/j.cognition.2023.105422

M3 - Journal article

C2 - 36871399

VL - 236

JO - Cognition

JF - Cognition

SN - 0010-0277

M1 - 105422

ER -