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Related Concept Videos

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

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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.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
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Language Development01:22

Language Development

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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.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
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Related Experiment Video

Updated: Nov 4, 2025

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism
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Evaluating atypical language in autism using automated language measures.

Alexandra C Salem1, Heather MacFarlane2, Joel R Adams3

  • 1Department of Psychiatry, Oregon Health and Science University, Portland, 97239, USA. salem@ohsu.edu.

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|May 27, 2021
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Summary

Automated Language Measures (ALMs) from speech transcripts offer a cost-effective way to identify autism spectrum disorder (ASD) language differences. These novel measures show promise for improving diagnostic accuracy and outcome assessment in ASD.

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Area of Science:

  • Linguistics
  • Developmental Psychology
  • Clinical Psychology

Background:

  • Measuring language atypicalities in Autism Spectrum Disorder (ASD) is challenging and expensive.
  • There is a need for more efficient and valid language outcome measures for ASD.
  • Existing methods for assessing language in ASD are often time-consuming and resource-intensive.

Purpose of the Study:

  • To develop and validate Automated Language Measures (ALMs) using language transcripts.
  • To assess the effectiveness of ALMs in differentiating between individuals with ASD and neurotypical controls.
  • To determine if ALMs can serve as reliable outcome measures for language in ASD.

Main Methods:

  • Collected language transcripts from 169 participants (ages 7-17) including those with ASD, typically developing (TD), and ADHD.
  • Generated seven ALMs from transcripts: mean length of utterance in morphemes, number of different word roots (NDWR), um proportion, content maze proportion, unintelligible proportion, c-units per minute, and repetition proportion.
  • Utilized nonparametric ANOVAs and logistic regression analyses to compare groups and predict ASD status.

Main Results:

  • Significant group differences were found for most ALMs, with the ASD group generally scoring lower than TD and ADHD groups (except for repetition proportion).
  • The TD and ADHD groups did not significantly differ from each other on the ALMs.
  • Four ALMs accurately predicted ASD status (67.9-75.5%), with combined ALMs achieving 82.4% accuracy.

Conclusions:

  • Automated Language Measures (ALMs) derived from speech transcripts are a valid and promising approach for assessing language in ASD.
  • ALMs can differentiate individuals with ASD from comparison groups and predict ASD status with notable accuracy.
  • These novel ALMs offer a potential solution for more efficient and cost-effective language outcome measurement in ASD research and clinical practice.