Difference in Language Profiles of Children With Autism Spectrum Disorder and Down Syndrome Is Not Driven by Non‐Verbal Cognition
Notice bibliographique
Résumé
BACKGROUND: Autism Spectrum Disorder (ASD) and Down syndrome (DS) are among the most common types of neurodevelopmental conditions that have co-occurring language impairments. Usually, non-verbal IQ has been reported as one of the main predictors of language functioning in children with these conditions. Although language abilities of children with ASD and DS have been described in the previous studies, there is still a lack of direct comparisons of language profiles in the non-verbal IQ-matched groups of children with these disorders, and, therefore, it is largely unexplored whether language difficulties in these populations are of similar or different origins. AIMS: The study provided a direct comparison of language profiles in non-verbal IQ-matched children with ASD and DS at different linguistic levels (phonology, vocabulary and morphosyntax) in both production and comprehension and explored the influence of different psycholinguistic variables on accuracy. Also, the study assessed whether non-language factors (non-verbal IQ and age) influence language skills in both groups of children. METHODS AND PROCEDURES: In total, 60 children participated in the study: 20 children with ASD, 20 children with DS and 20 typically developing controls (7-11 years old; all groups were age-matched). The language testing included seven tests from the Russian Child Language Assessment Battery, assessing expressive and receptive language skills at phonological, lexical and morphosyntactic levels. OUTCOMES AND RESULTS: Overall, we revealed both similarities and differences in language profiles between children with ASD and DS. At the group performance level, children with ASD and DS were comparable in vocabulary and syntax but differed in phonological processing, on which children with ASD had higher accuracy. Some psycholinguistic variables that influenced accuracy in language test performance were present uniquely in the ASD group: for example, autistic children struggled more with verbs than nouns in naming or comprehended sentences with canonical SVO word order more accurately than sentences with noncanonical OVS word order. In comparison to children with DS, in the ASD group, non-verbal IQ was related to language skills in three out of seven tests, with evidence of a positive association between them. CONCLUSIONS AND IMPLICATIONS: This study provided new insights on the differences in language profiles of non-verbal IQ-matched children with ASD and DS and identified specific impairments related to linguistic levels and structural language characteristics in each group. These findings contributed to speech and language therapy strategies, as they highlighted specific 'linguistic deficits' that should be targeted during intervention and therapy. WHAT THIS PAPER ADDS: What is already known on this subject Language profiles of children with Autism Spectrum Disorder (ASD) and Down syndrome (DS) have been described in previous studies on different languages. Usually, non-verbal IQ has been reported as one of the main predictors of language functioning in these groups of individuals with neurodevelopmental disorders. However, there is a lack of direct comparisons of language profiles at different linguistic levels in these groups, matched by non-verbal IQ and using standardized language assessment tools to understand whether the nature of language impairments is common or different in ASD and DS regardless of non-verbal cognition. What this study adds to the existing knowledge This study provided a direct comparison of language profiles at different linguistic levels in children with ASD and DS matched by non-verbal IQ. This identified similarities and differences in language functioning at different linguistic levels in children with ASD and DS as well as revealed non-language factors that were associated with language abilities. What are the potential or actual clinical implications of this work? The study showed the differences in language profiles of children with ASD and DS regardless of non-verbal IQ and identified specific impairments related to linguistic levels and structural language characteristics. This knowledge contributes to speech and language therapy strategies, as it elucidates specific 'linguistic deficits' that should be targeted during intervention and therapy.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».