Quantifying Narrative Ability in Autism Spectrum Disorder: A Computational Linguistic Analysis of Narrative Coherence

Autism is a neurodevelopmental disorder characterized by serious difficulties with the social use of language, along with impaired social functioning and ritualistic/repetitive behaviors (American Psychiatric Association in Diagnostic and statistical manual of mental disorders: DSM-5, 5th edn. American Psychiatric Association, Arlington, 2013). While substantial heterogeneity exists in symptom expression, impairments in language discourse skills, including narrative (or storytelling), are universally observed in autism (Tager-Flusberg et al. in Handbook on autism and pervasive developmental disorders, 3rd edn. Wiley, New York, pp 335–364, 2005). This study applied a computational linguistic tool, Latent Semantic Analysis (LSA), to objectively characterize narrative performance in high-functioning individuals with autism and typically-developing controls, across two different narrative contexts that differ in the interpersonal and cognitive demands placed on the narrator. Results indicated that high-functioning individuals with autism produced narratives comparable in semantic content to those produced by controls when narrating from a picture book, but produced narratives diminished in semantic quality in a more demanding narrative recall task. This pattern is similar to that detected from analyses of hand-coded picture book narratives in prior research, and extends findings to an additional narrative context that proves particularly challenging for individuals with autism. Results are discussed in terms of the utility of LSA as a quantitative, objective, and efficient measure of narrative ability.