To What Extent Can Statistical Learning Explain Language Profiles in Autism? Methodological and Theoretical Challenges.

Background and aims: Language development in autism varies widely, from fluently verbal to minimally verbal individuals, with socio-communicative difficulties often cited as key explanatory factors. Statistical learning (SL)-the ability to detect regularities in language-has also emerged as a potential contributor to language acquisition in autism. However, SL research in autism has predominantly focused on verbally fluent individuals, leaving non- and minimally verbal populations underexplored. This study aimed to examine the predictive roles of joint attention and statistical learning, specifically nonadjacent dependency learning, on expressive vocabulary and morphosyntactic outcomes in autistic children.

Methods: Participants included 40 autistic children aged 5-8 years with diverse linguistic profiles, ranging from verbally fluent to minimally verbal, and 40 non-autistic children. Joint attention was assessed during a semi-structured play protocol, which also provided naturalistic language samples for analysis. Measures of expressive vocabulary and morphosyntax were derived from the number of different words and verb flexions produced, respectively. Sensitivity to nonadjacent dependencies was evaluated through an artificial language learning task.

Results: Neither joint attention nor sensitivity to nonadjacent dependencies predicted expressive vocabulary or morphosyntactic skills in autistic children. Response to joint attention scores were significantly lower in autistic children than in non-autistic children but higher than in previous research. This may be due to the less structured and, therefore, more ecologically valid context in which joint attention was assessed (free play), in conjunction with age and maturation factors. Regarding the SL task, both autistic and non-autistic children demonstrated sensitivity to nonadjacent dependencies. Most interestingly perhaps, only 15 autistic children completed the SL task, with non-verbal cognitive abilities significantly predicting task completion.

Conclusions and implications: This study highlights the complexity of investigating the role of statistical learning in language development in autism. It underscores the limitations of behavioral SL paradigms for minimally verbal children. Future research should prioritize developing more ecologically valid and accessible paradigms to accurately assess statistical learning in minimally verbal children, thereby clarifying the role SL may play in language acquisition in autism.