I’m super happy to see this paper out in Language, Cognition and Neuroscience!
“Predictive sentence comprehension during story-listening in autism spectrum disorder” [link]
This is the third paper out of a collaboration with Renee Lajiness-O’Neill, Susan Bowyer and Ioulia Kovelman (see also here and here) and also benefits from the expertise of John Hale. Short story: school-aged children listen to our Alice in Wonderland stimulus [link] during MEG scanning and we quantify predictive processing using Surprisal. Both groups show relatively early sensitivity to surprisal over temporal channels, with no detectable differences between groups. Check out the Abstract below, or just read the paper!
Individuals 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.