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Expressive vocabulary has been characterized as a strength in the autistic population, with most studies focusing on the cues that autistic children use to learn new words. There is little knowledge about the organization of lexical networks in autism. This study aimed to characterize the lexical retrieval errors in a cohort of 60 Greek-speaking autistic preschoolers by combining manual coding with distributional semantics. Children completed a standardized picture-naming task yielding 745 errors classified into four error types: semantic, visual, phonological, and irrelevant. To quantify each error’s proximity to its intended target, we applied a pre-trained word embeddings model to derive continuous semantic proximity scores. More than half of the children exhibited expressive vocabulary abilities which were well below their chronological age level, while the opposite pattern was observed for one third of the children. Within this sample, semantic errors were predominant, and word embedding-derived distances corroborated the manual taxonomy: semantic substitutions displayed the highest proximity to target concepts, followed in descending order by phonological, visual, and irrelevant errors. Semantic errors showed no association with vocabulary delay, whereas irrelevant and visual errors increased with vocabulary delay, suggesting reduced semantic constraints in children whose vocabulary age equivalents were lower than their chronological ages. These findings underscore that naming errors in autistic children are not random but organized along graded semantic relationships, paving the way for computational modeling of lexical networks in this population. Integrating distributional-semantic modeling with traditional error analysis yields novel quantitative metrics for distinguishing between lexical error types, and paves the way for investigations into the development of lexical networks in autism.
Ανδρέου et al. (Fri,) studied this question.