Re-Evaluating the Role of Statistical Learning: Domain-General Predictors of Language in Typical and Atypical Development.

The ability to detect statistical regularities in the input is widely assumed to support language acquisition, but its unique contribution to individual differences in language outcomes remains uncertain, especially in neurodevelopmental conditions. This study tested whether statistical learning predicts expressive vocabulary and grammatical skills in children with typical development and in groups with developmental language disorder, autism spectrum disorder or attention deficit hyperactivity disorder. A total of 102 Hungarian-speaking children aged 8 to 15 years completed tasks assessing statistical learning, auditory and visual perceptual speed, short-term and working memory, sentence repetition, and expressive vocabulary. Statistical learning showed modest associations with language measures, but the effects of perceptual speed and working memory were stronger. Verbal working memory consistently emerged as the strongest predictor of vocabulary and sentence repetition across all groups. At the same time, the relatively low reliability of the statistical learning measures may have attenuated the observed associations, potentially leading to an underestimation of the strength of their relationship with language. The pattern of associations did not differ systematically between diagnostic groups, consistent with the idea that variation in language outcomes reflects general cognitive differences that cut across traditional diagnostic categories. This finding is consistent with dimensional accounts of neurodevelopment, which propose that differences between conditions arise through quantitative rather than qualitative variation in core cognitive processes. However, the various dimensions were hard to disentangle at this sample size. By demonstrating this pattern for language abilities, the present study extends the dimensional perspective to the domain of language development and highlights the importance of accounting for shared cognitive mechanisms when studying typical and atypical language variation.