Network Analysis of Autistic Language Learners Along the Small World Spectrum / Eileen HAEBIG in Autism Research, 18-8 (August 2025)  

Public ISBD [article]  inAutism Research > 18-8 (August 2025) . - p.1580-1594 Titre : Network Analysis of Autistic Language Learners Along the Small World Spectrum Type de document : texte imprimé Auteurs : Eileen HAEBIG, Auteur ; Stanley WEST, Auteur ; Eva JIMENEZ, Auteur ; Thomas T. HILLS, Auteur ; Christopher R. COX, Auteur Article en page(s) : p.1580-1594 Langues : Anglais (eng) Mots-clés : autism  children  semantic network modeling  semantic structure  word learning Index. décimale : PER Périodiques Résumé : ABSTRACT Recent network analyses of vocabulary growth revealed important relationships between the structure of the semantic environment and early vocabulary acquisition in non-autistic children. However, autistic children may be less likely to encode associated features of novel objects, suggesting divergent processes for acquiring semantic information about words. We examined the expressive vocabularies of 815 non-autistic and 163 autistic children (words produced: MAutistic 183.06, MNon-autistic 182.91). We estimated their trajectories of semantic development using network analyses. Network structure was based on child-oriented word associations. We analyzed networks according to indegree, average shortest path length, clustering coefficient, and small-world propensity (features holistically contributing to ?small-world? network structure). Analyses revealed that autistic and non-autistic children are sensitive to the structure of their semantic environment. However, group differences were observed, with an early peak in the autistic group's clustering coefficient (how closely connected groups of words are), followed by a sharp decline. Moreover, across each network metric, we found that autistic children had reduced small-world structure relative to non-autistic toddlers. Thus, group differences indicate that, although autistic children are learning from their semantic environment, they may be processing their semantic environment differently, the language input to which they are exposed differs relative to non-autistic children, or a combination of the two. En ligne : https://doi.org/10.1002/aur.70065 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=566 [article] Network Analysis of Autistic Language Learners Along the Small World Spectrum [texte imprimé] / Eileen HAEBIG, Auteur ; Stanley WEST, Auteur ; Eva JIMENEZ, Auteur ; Thomas T. HILLS, Auteur ; Christopher R. COX, Auteur . - p.1580-1594. Langues : Anglais (eng) in Autism Research > 18-8 (August 2025) . - p.1580-1594 Mots-clés : autism  children  semantic network modeling  semantic structure  word learning Index. décimale : PER Périodiques Résumé : ABSTRACT Recent network analyses of vocabulary growth revealed important relationships between the structure of the semantic environment and early vocabulary acquisition in non-autistic children. However, autistic children may be less likely to encode associated features of novel objects, suggesting divergent processes for acquiring semantic information about words. We examined the expressive vocabularies of 815 non-autistic and 163 autistic children (words produced: MAutistic 183.06, MNon-autistic 182.91). We estimated their trajectories of semantic development using network analyses. Network structure was based on child-oriented word associations. We analyzed networks according to indegree, average shortest path length, clustering coefficient, and small-world propensity (features holistically contributing to ?small-world? network structure). Analyses revealed that autistic and non-autistic children are sensitive to the structure of their semantic environment. However, group differences were observed, with an early peak in the autistic group's clustering coefficient (how closely connected groups of words are), followed by a sharp decline. Moreover, across each network metric, we found that autistic children had reduced small-world structure relative to non-autistic toddlers. Thus, group differences indicate that, although autistic children are learning from their semantic environment, they may be processing their semantic environment differently, the language input to which they are exposed differs relative to non-autistic children, or a combination of the two. En ligne : https://doi.org/10.1002/aur.70065 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=566

Vocabulary development in autistic children: a network growth analysis / Eileen HAEBIG in Journal of Child Psychology and Psychiatry, 67-6 (June 2026)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 67-6 (June 2026) . - p.844-858 Titre : Vocabulary development in autistic children: a network growth analysis Type de document : texte imprimé Auteurs : Eileen HAEBIG, Auteur ; Stanley WEST, Auteur ; Christopher R. COX, Auteur Article en page(s) : p.844-858 Langues : Anglais (eng) Mots-clés : Autism  word learning  semantic network modeling Index. décimale : PER Périodiques Résumé : Background Autistic children are typically late to develop their expressive vocabulary, but little is known about their early word learning process. This study compared three network growth models on their ability to account for the trajectories of expressive vocabulary acquisition in autistic and non-autistic children. Methods We studied expressive vocabularies using item-level data from a child vocabulary checklist (n?=?721 records from young autistic children; n?=?2,166 records from non-autistic toddlers). We estimated vocabulary growth trajectories for autistic and non-autistic children and assessed the goodness of fit of three models of vocabulary growth, with varying sensitivity to the structure of the environment and the learner's existing vocabulary knowledge. To do so, we first computed word-level acquisition norms that indicate the vocabulary size at which individual words tend to be learned by each group. Then we evaluated how well network growth models, based on natural language co-occurrence structure and word associations, accounted for variance in the autistic and non-autistic acquisition norms. Results Our word-level vocabulary size of acquisition norms closely aligned with age of acquisition data, indicating their utility when age of acquisition norms cannot be derived for neurodivergent populations. Furthermore, we extended key observations and demonstrated that the growth models explained similar amounts of variance in each group. Both groups are biased to learn words that have many connections to words that have been previously learned; however, even after accounting for this learning influence, autistic and non-autistic vocabulary growth trajectories receive an added boost in learning when words are connected to many other words in the learning environment, indicating a similar learning profile. Conclusions Both groups preferentially acquire new words by leveraging the semantic structure in the learning environment, indicating an overlap in theoretical accounts of vocabulary growth. En ligne : https://doi.org/10.1111/jcpp.70076 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=587 [article] Vocabulary development in autistic children: a network growth analysis [texte imprimé] / Eileen HAEBIG, Auteur ; Stanley WEST, Auteur ; Christopher R. COX, Auteur . - p.844-858. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 67-6 (June 2026) . - p.844-858 Mots-clés : Autism  word learning  semantic network modeling Index. décimale : PER Périodiques Résumé : Background Autistic children are typically late to develop their expressive vocabulary, but little is known about their early word learning process. This study compared three network growth models on their ability to account for the trajectories of expressive vocabulary acquisition in autistic and non-autistic children. Methods We studied expressive vocabularies using item-level data from a child vocabulary checklist (n?=?721 records from young autistic children; n?=?2,166 records from non-autistic toddlers). We estimated vocabulary growth trajectories for autistic and non-autistic children and assessed the goodness of fit of three models of vocabulary growth, with varying sensitivity to the structure of the environment and the learner's existing vocabulary knowledge. To do so, we first computed word-level acquisition norms that indicate the vocabulary size at which individual words tend to be learned by each group. Then we evaluated how well network growth models, based on natural language co-occurrence structure and word associations, accounted for variance in the autistic and non-autistic acquisition norms. Results Our word-level vocabulary size of acquisition norms closely aligned with age of acquisition data, indicating their utility when age of acquisition norms cannot be derived for neurodivergent populations. Furthermore, we extended key observations and demonstrated that the growth models explained similar amounts of variance in each group. Both groups are biased to learn words that have many connections to words that have been previously learned; however, even after accounting for this learning influence, autistic and non-autistic vocabulary growth trajectories receive an added boost in learning when words are connected to many other words in the learning environment, indicating a similar learning profile. Conclusions Both groups preferentially acquire new words by leveraging the semantic structure in the learning environment, indicating an overlap in theoretical accounts of vocabulary growth. En ligne : https://doi.org/10.1111/jcpp.70076 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=587

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