Abstract
ABSTRACTIndividuals with Autism Spectrum Disorder (ASD) show a range of language production deficits, however, language comprehension in ASD remains under-studied in part because co-morbid social deficits affect behavioural compliance. This challenge can be overcome by engaging participants in a naturalistic task while passively collecting neural signals. To test predictive processing with naturalistic language, we collect MEG data while 16 8–12-year-old high-functioning participants with a clinical diagnosis of ASD and 16 age- and gender-matched typically developing peers listen to an audiobook story. The neuromagnetic signals are correlated with word-by-word states from a computational model that quantifies incremental sentence predictions in terms of surprisal. Consistent with prior eye-tracking work, our results are compatible with predictive parsing that is equivalent between high-functioning individuals with ASD and TD peers.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
