An Application of Network Science to Phonological Sequence Learning in Children With Developmental Language Disorder Purpose Network science has been a valuable tool in language research for investigating relationships between complex linguistic elements but has not yet been applied to sound sequencing in production. In the present work, we used standard error-based accuracy and articulatory kinematic approaches as well as novel measures from network science ... Research Article
Research Article  |   September 19, 2018
An Application of Network Science to Phonological Sequence Learning in Children With Developmental Language Disorder
 
Author Affiliations & Notes
  • Sara Benham
    School of Behavioral and Brain Sciences, University of Texas at Dallas
  • Lisa Goffman
    School of Behavioral and Brain Sciences, University of Texas at Dallas
  • Richard Schweickert
    Department of Psychological Sciences, Purdue University, West Lafayette, IN
  • Disclosure: The authors have declared that no competing interests existed at the time of publication.
    Disclosure: The authors have declared that no competing interests existed at the time of publication. ×
  • Correspondence to Sara Benham: sara.benham@utdallas.edu
  • Editor-in-Chief: Sean Redmond
    Editor-in-Chief: Sean Redmond×
  • Editor: Lizbeth Finestack
    Editor: Lizbeth Finestack×
Article Information
Development / Language Disorders / Attention, Memory & Executive Functions / Speech, Voice & Prosody / Language / Research Articles
Research Article   |   September 19, 2018
An Application of Network Science to Phonological Sequence Learning in Children With Developmental Language Disorder
Journal of Speech, Language, and Hearing Research, September 2018, Vol. 61, 2275-2291. doi:10.1044/2018_JSLHR-L-18-0036
History: Received January 29, 2018 , Revised March 28, 2018 , Accepted May 6, 2018
 
Journal of Speech, Language, and Hearing Research, September 2018, Vol. 61, 2275-2291. doi:10.1044/2018_JSLHR-L-18-0036
History: Received January 29, 2018; Revised March 28, 2018; Accepted May 6, 2018

Purpose Network science has been a valuable tool in language research for investigating relationships between complex linguistic elements but has not yet been applied to sound sequencing in production. In the present work, we used standard error-based accuracy and articulatory kinematic approaches as well as novel measures from network science to evaluate variability and sequencing errors in speech production in children with developmental language disorder (DLD; aka specific language impairment).

Method Twelve preschoolers with DLD and 12 age-matched controls participated in a 3-day novel word learning study. Transcription and articulatory movement data were collected to measure accuracy and variability of productions, and networks of speech productions were generated to analyze syllable co-occurrence patterns.

Results Results indicated that children with DLD were less accurate than children with typical language at the segmental level. Crucially, these findings did not align with performance at the articulatory level, where there were no differences in movement variability between children with DLD and those with typical language. Network analyses revealed characteristics that were not captured by standard measures of phonetic accuracy, including a larger inventory of syllable forms, more connections between the forms, and less consistent production patterns.

Conclusions Network science provides significant insights into phonological learning trajectories in children with DLD and their typically developing peers. Importantly, errors in word production by children with DLD do not surface as a result of weakness in articulatory control. Instead, results suggest that speech errors in DLD may relate to deficits in sound sequencing.

Acknowledgments
This research was supported by the National Institute on Deafness and Other Communication Disorders Grant R01 DC04826 awarded to Lisa Goffman. We would like to express our gratitude to the study participants and their families as well as to Janna Berlin, Barb Brown, Allison Gladfelter, Meredith Saletta, Amanda Steeb, and Janet Vuolo for their assistance with data collection and processing. We would also like to thank LouAnn Gerken and Larry Leonard for their valuable insights and contributions to this project.
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