TY - JOUR T1 - Predictive processing during a naturalistic statistical learning task in ASD JF - eneuro JO - eNeuro DO - 10.1523/ENEURO.0069-19.2020 SP - ENEURO.0069-19.2020 AU - Neelima Wagley AU - Renee Lajiness-O’Neill AU - Jessica S. F. Hay AU - Susan M. Bowyer AU - Margaret Ugolini AU - Ioulia Kovelman AU - Jonathan R. Brennan Y1 - 2020/11/16 UR - http://www.eneuro.org/content/early/2020/11/16/ENEURO.0069-19.2020.abstract N2 - Children’s sensitivity to regularities within the linguistic stream, such as the likelihood that syllables co-occur, is foundational to speech segmentation and language acquisition. Yet, little is known about the neurocognitive mechanisms underlying speech segmentation in typical development and in neurodevelopmental disorders that impact language acquisition such as Autism Spectrum Disorder (ASD). Here, we investigate the neural signals of statistical learning in 15 human participants (children ages 8-12) with a clinical diagnosis of ASD and 14 age- and gender-matched typically developing peers. We tracked the evoked neural responses to syllable sequences in a naturalistic statistical learning corpus using magnetoencephalography (MEG) in the left primary auditory cortex, posterior superior temporal gyrus, and inferior frontal gyrus, across three repetitions of the passage. In typically developing children, we observed a neural index of learning in all three regions of interest, measured by the change in evoked response amplitude as a function of syllable surprisal across passage repetitions. As surprisal increased, the amplitude of the neural response increased; this sensitivity emerged after repeated exposure to the corpus. Children with ASD did not show this pattern of learning in all three regions. We discuss two possible hypotheses related to children’s sensitivity to bottom-up sensory deficits and difficulty with top-down incremental processing.Significance Statement Language acquisition involves segmenting the continuous speech stream into sounds, syllables, and words. Learning these units relies on both the properties of the input, as well as emerging high-order cognitive mechanisms that guide learning from the top-down. We examined the neurobiology underlying the integration of top-down and bottom-up information in statistical speech segmentation in children with and without ASD. We offer evidence of neural and behavioral effects of syllable-to-syllable processing in speech segmentation that differ in typically developing children from children with a clinical diagnosis of ASD. Our findings inform developmental and cognitive theories of language acquisition by examining the computational nature of speech segmentation across different populations of learners. ER -