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Autism Spectrum Disorder

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Related Experiment Video

Updated: Dec 21, 2025

Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers

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Published on: March 14, 2018

Predicting Expressive Language From Early Vocalizations in Young Children With Autism Spectrum Disorder: Which Vocal

Jena McDaniel¹, Paul Yoder², Annette Estes³

¹Life Span Institute, University of Kansas, Lawrence.

Journal of Speech, Language, and Hearing Research : JSLHR

|May 14, 2020

Summary

Human-coded vocal quality, not quantity, offers valuable insights for predicting expressive language in children with autism spectrum disorder (ASD). Investing in quality coding enhances predictive accuracy for early language development.

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Area of Science:

Developmental Psychology
Speech-Language Pathology
Autism Spectrum Disorder Research

Background:

Early language development is crucial for children with autism spectrum disorder (ASD).
Predicting expressive language skills in young children with ASD is essential for timely intervention.
Current methods for assessing vocal development vary in cost and complexity.

Purpose of the Study:

To evaluate the incremental validity of vocal development variables for predicting later expressive language in children with ASD.
To compare the predictive value of vocal quality versus vocal quantity.
To assess the added value of human-coded variables over automated analyses.

Main Methods:

Eighty-seven children with ASD (13-30 months) participated.
Vocalization quantity was measured via human coding and automated analysis.
Vocalization quality was assessed using four human-coded variables and one automated variable.
Expressive language was measured at baseline and 12 months.

Main Results:

Human-coded vocal quality variables demonstrated incremental validity in predicting later expressive language skills, even when controlling for vocalization quantity.
Human-coded vocal variables predicted expressive language skills independently of automated vocal variables.
Quality of vocalizations proved more predictive than quantity.

Conclusions:

Resources should be allocated to human coding of vocalization quality for improved prediction of expressive language in young children with ASD.
Despite higher costs, human-coded vocal quality provides significant added value.
This approach can inform early intervention strategies for children with ASD.