AI Insight
This study compared 82 preschool children with autism and language impairment, Down syndrome, or fragile X syndrome using both standardized language tests and natural language samples during play. While standardized tests showed no differences between groups, natural language analysis revealed that children with fragile X syndrome performed better on word-based measures, and non-word vocalization patterns captured meaningful variation in communication abilities. Cluster analysis identified three distinct communication profiles: Higher Language, Lower Output, and High Output-Low Structure.
Why it matters
Natural language sampling provides clinically useful information beyond standardized tests, particularly for characterizing expressive communication in children with neurodevelopmental disorders. The non-word vocalization coding system may be especially valuable for assessing children with very limited verbal abilities, enabling more precise characterization of their communication skills and potential intervention targets.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Purpose: This study examined whether word-based and non-word natural language sampling (NLS) measures complement standardized language scores in characterizing expressive communication among preschool children with autism with language impairment (AUT-LI), Down syndrome (DS), and fragile X syndrome (FXS). Method: Participants included 82 preschool children with AUT-LI (n = 32), DS (n = 34), or FXS (n = 16). Expressive communication was characterized using the Preschool Language Scales and 10-minute natural language samples collected during a standardized play-based protocol. NLS measures included traditional word-based metrics, such as lexical diversity, intelligibility, and word approximations, as well as tiered non-word vocalization measures, including non-speech, single-phoneme, and multi-phoneme vocalizations. Analyses examined age associations, diagnostic group differences, between group differences in variability, and exploratory communication profiles derived using principal component analysis and partitioning around medoids clustering. Results: Diagnostic groups did not significantly differ on standardized expressive language scores. Children with FXS showed higher performance than the AUT-LI and DS groups on several word-based NLS measures, whereas non-word vocalization measures primarily revealed differences in within-group variability. Word-based NLS measures were consistently associated with standardized expressive language skills, while associations for non-word vocalizations varied by vocalizations type. Cluster-validity indices factored a two-cluster solution. A hypothesis-informed three cluster solution that included non-word vocalizations suggested Higher Language, Lower Output, and High Output-Low Structure profiles. Conclusions: Standardized and NLS measures provide complementary descriptions of expressive communication in children with neurodevelopmental disorders. Non-word vocalization coding may be especially useful for describing clinical meaningful variation among children with limited expressive language. Keywords: Natural language sampling; expressive language; neurodevelopmental disorders; autism spectrum disorder; Down syndrome; Fragile X syndrome