Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impaired social communication and the presence of restricted, repetitive patterns of behaviors and interests. Prior research suggests that restricted patterns of behavior in ASD may be cross-domain phenomena that are evident in a variety of modalities. Computational studies of language in ASD provide support for the existence of an underlying dimension of restriction that emerges during a conversation. Similar evidence exists for restricted patterns of facial movement. Using tools from computational linguistics, computer vision, and information theory, this study tests whether cognitive-motor restriction can be detected across multiple behavioral domains in adults with ASD during a naturalistic conversation. Our methods identify restricted behavioral patterns, as measured by entropy in word use and mouth movement. Results suggest that adults with ASD produce significantly less diverse mouth movements and words than neurotypical adults, with an increased reliance on repeated patterns in both domains. The diversity values of the two domains are not significantly correlated, suggesting that they provide complementary information.
Highlights
Autism spectrum disorder (ASD) is a behaviorally-defined neurodevelopmental condition that affects approximately 1.5% of children in the U.S (Christensen et al, 2016)
Conversational length did not differ for ASD and typically developing (TD) participants, which was expected given the controlled 3-minute task design
Participant groups did not differ on speech rate, but the ASD group had a significantly larger sum of Confederate-to-Participant (C2P) pauses than the TD group
Summary
Autism spectrum disorder (ASD) is a behaviorally-defined neurodevelopmental condition that affects approximately 1.5% of children in the U.S (Christensen et al, 2016). Verbal abilities in ASD range from minimally verbal (a few words or sounds) to above average (Pickles et al, 2014) This heterogeneity makes it harder to diagnose ASD reliably, and expert clinicians may disagree about whether or not an individual meets criteria (Regier et al, 2013). Ecologically valid stimuli have been shown to be superior for capturing ASD-related differences in behavior (Chevallier et al, 2015), most traditional ASD assessments continue to be conducted in highly controlled contexts. Taken together, these challenges highlight the need for a precision medicine approach to ASD (Beversdorf, 2016) that includes quantified and precise behavioral assessments in naturalistic settings
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