Computational approaches to understanding interaction and development

D. S. Messinger, L. K. Perry, S. G. Mitsven, Y. Tao, J. Moffitt, R. M. Fasano, S. A. Custode, C. M. Jerry

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationNew Methods and Approaches for Studying Child Development
EditorsRick O. Gilmore, Jeffrey J. Lockman
PublisherAcademic Press Inc.
Pages191-230
Number of pages40
ISBN (Print)9780323915878
DOIs
StatePublished - Jan 2022

Publication series

NameAdvances in Child Development and Behavior
Volume62
ISSN (Print)0065-2407

Keywords

  • Audio
  • Automated measurement
  • Deep learning
  • Development
  • Interaction
  • Language
  • Machine learning
  • Objective
  • Radio frequency identification
  • Social

ASJC Scopus subject areas

  • Pediatrics, Perinatology, and Child Health
  • Developmental and Educational Psychology
  • Behavioral Neuroscience

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