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Computational Approaches to Understanding Interaction and Development

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Title: Computational Approaches to Understanding Interaction and Development
Language: English
Authors: Messinger, D. S., Perry, L. K., Mitsven, S. G., Tao, Y., Moffitt, J., Fasano, R. M., Custode, S. A., Jerry, C. M.
Source: Grantee Submission. 2022.
Peer Reviewed: Y
Page Count: 64
Publication Date: 2022
Sponsoring Agency: National Institute on Deafness and Other Communication Disorders (NIDCD) (DHHS/NIH)
Institute of Education Sciences (ED)
National Science Foundation (NSF)
Contract Number: R01DC018542
R324A180203
IBSS1620294
Document Type: Reports - Research
Descriptors: Artificial Intelligence, Computation, Measurement Techniques, Automation, Assistive Technology, Audio Equipment, Preschool Children, Autism Spectrum Disorders, Developmental Disabilities, Peer Relationship, Child Language, Child Behavior, Predictor Variables, Social Behavior, Diagnostic Tests, Observation
Assessment and Survey Identifiers: Autism Diagnostic Observation Schedule
DOI: 10.1016/bs.acdb.2021.12.002
Abstract: Audio-visual recording and location tracking produce enormous quantities of digital data with which researchers can document children's everyday interactions in naturalistic settings and assessment contexts. Machine learning and other computational approaches can produce replicable, automated measurements of these big behavioral data. The economies of scale afforded by repeated automated measurements offer a potent approach to investigating linkages between real-time behavior and developmental change. In our work, automated measurement of audio from child-worn recorders--which quantify the frequency of child and adult speech and index its phonemic complexity--are paired with ultrawide radio tracking of children's location and interpersonal orientation. Applications of objective measurement indicate the influence of adult behavior in both expert ratings of attachment behavior and ratings of autism severity, suggesting the role of dyadic factors in these "child" assessments. In the preschool classroom, location/orientation measures provide data-driven measures of children's social contact, fertile ground for vocal interactions. Both the velocity of children's movement toward one another and their social contact with one another evidence homophily: children with autism spectrum disorder, other developmental disabilities, and typically developing children were more likely to interact with children in the same group even in inclusive preschool classrooms designed to promote interchange between all children. In the vocal domain, the frequency of peer speech and the phonemic complexity of teacher speech predict the frequency and phonemic complexity of children's own speech over multiple timescales. Moreover, children's own speech predicts their assessed language abilities across disability groups, suggesting how everyday interactions facilitate development. [This paper was published in "Advances in Child Development and Behavior" v62 2022.]
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2023
Accession Number: ED625515
Database: ERIC
FullText Text:
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  Data: <searchLink fieldCode="SO" term="%22Grantee+Submission%22"><i>Grantee Submission</i></searchLink>. 2022.
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  Data: Audio-visual recording and location tracking produce enormous quantities of digital data with which researchers can document children's everyday interactions in naturalistic settings and assessment contexts. Machine learning and other computational approaches can produce replicable, automated measurements of these big behavioral data. The economies of scale afforded by repeated automated measurements offer a potent approach to investigating linkages between real-time behavior and developmental change. In our work, automated measurement of audio from child-worn recorders--which quantify the frequency of child and adult speech and index its phonemic complexity--are paired with ultrawide radio tracking of children's location and interpersonal orientation. Applications of objective measurement indicate the influence of adult behavior in both expert ratings of attachment behavior and ratings of autism severity, suggesting the role of dyadic factors in these "child" assessments. In the preschool classroom, location/orientation measures provide data-driven measures of children's social contact, fertile ground for vocal interactions. Both the velocity of children's movement toward one another and their social contact with one another evidence homophily: children with autism spectrum disorder, other developmental disabilities, and typically developing children were more likely to interact with children in the same group even in inclusive preschool classrooms designed to promote interchange between all children. In the vocal domain, the frequency of peer speech and the phonemic complexity of teacher speech predict the frequency and phonemic complexity of children's own speech over multiple timescales. Moreover, children's own speech predicts their assessed language abilities across disability groups, suggesting how everyday interactions facilitate development. [This paper was published in "Advances in Child Development and Behavior" v62 2022.]
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