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1. Comprehensive Social Trait Judgments From Faces in Autism Spectrum Disorder

   https://doi.org/10.1177/09567976231192236  

   Cao, Runnan; Zhang, Na; Yu, Hongbo; Webster, Paula J.; Paul, Lynn K.; Li, Xin; Lin, Chujun; Wang, Shuo (October 2023, Psychological Science)

   Processing social information from faces is difficult for individuals with autism spectrum disorder (ASD). However, it remains unclear whether individuals with ASD make high-level social trait judgments from faces in the same way as neurotypical individuals. Here, we comprehensively addressed this question using naturalistic face images and representatively sampled traits. Despite similar underlying dimensional structures across traits, online adult participants with self-reported ASD showed different judgments and reduced specificity within each trait compared with neurotypical individuals. Deep neural networks revealed that these group differences were driven by specific types of faces and differential utilization of features within a face. Our results were replicated in well-characterized in-lab participants and partially generalized to more controlled face images (a preregistered study). By investigating social trait judgments in a broader population, including individuals with neurodevelopmental variations, we found important theoretical implications for the fundamental dimensions, variations, and potential behavioral consequences of social cognition. 

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2. Parallel Social Information Processing Circuits Are Differentially Impacted in Autism

   https://doi.org/10.1016/j.neuron.2020.10.002  

   Lewis, Eastman M.; Stein-O’Brien, Genevieve L.; Patino, Alejandra V.; Nardou, Romain; Grossman, Cooper D.; Brown, Matthew; Bangamwabo, Bidii; Ndiaye, Ndeye; Giovinazzo, Daniel; Dardani, Ian; et al (November 2020, Neuron)

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3. Distinct neurocognitive bases for social trait judgments of faces in autism spectrum disorder

   https://doi.org/10.1038/s41398-022-01870-9  

   Yu, Hongbo; Cao, Runnan; Lin, Chujun; Wang, Shuo (March 2022, Translational Psychiatry)

   Abstract Autism spectrum disorder (ASD) is characterized by difficulties in social processes, interactions, and communication. Yet, the neurocognitive bases underlying these difficulties are unclear. Here, we triangulated the ‘trans-diagnostic’ approach to personality, social trait judgments of faces, and neurophysiology to investigate (1) the relative position of autistic traits in a comprehensive social-affective personality space, and (2) the distinct associations between the social-affective personality dimensions and social trait judgment from faces in individuals with ASD and neurotypical individuals. We collected personality and facial judgment data from a large sample of online participants (N = 89 self-identified ASD;N = 307 neurotypical controls). Factor analysis with 33 subscales of 10 social-affective personality questionnaires identified a 4-dimensional personality space. This analysis revealed that ASD and control participants did not differ significantly along the personality dimensions of empathy and prosociality, antisociality, or social agreeableness. However, the ASD participants exhibited a weaker association between prosocial personality dimensions and judgments of facial trustworthiness and warmth than the control participants. Neurophysiological data also indicated that ASD participants had a weaker association with neuronal representations for trustworthiness and warmth from faces. These results suggest that the atypical association between social-affective personality and social trait judgment from faces may contribute to the social and affective difficulties associated with ASD. 

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4. Measuring Social Modulation of Gaze in Autism Spectrum Condition With Virtual Reality Interviews

   https://doi.org/10.1109/TNSRE.2022.3198933  

   Artiran, Saygin; Ravisankar, Raghav; Luo, Sarah; Chukoskie, Leanne; Cosman, Pamela (January 2022, IEEE Transactions on Neural Systems and Rehabilitation Engineering)
5. Promoting Social Engagement With a Multi-Role Dancing Robot for In-Home Autism Care

   https://doi.org/10.3389/frobt.2022.880691  

   Javed, Hifza; Park, Chung Hyuk (June 2022, Frontiers in Robotics and AI)

   This work describes the design of real-time dance-based interaction with a humanoid robot, where the robot seeks to promote physical activity in children by taking on multiple roles as a dance partner. It acts as a leader by initiating dances but can also act as a follower by mimicking a child’s dance movements. Dances in the leader role are produced by a sequence-to-sequence (S2S) Long Short-Term Memory (LSTM) network trained on children’s music videos taken from YouTube. On the other hand, a music orchestration platform is implemented to generate background music in the follower mode as the robot mimics the child’s poses. In doing so, we also incorporated the largely unexplored paradigm of learning-by-teaching by including multiple robot roles that allow the child to both learn from and teach to the robot. Our work is among the first to implement a largely autonomous, real-time full-body dance interaction with a bipedal humanoid robot that also explores the impact of the robot roles on child engagement. Importantly, we also incorporated in our design formal constructs taken from autism therapy, such as the least-to-most prompting hierarchy, reinforcements for positive behaviors, and a time delay to make behavioral observations. We implemented a multimodal child engagement model that encompasses both affective engagement (displayed through eye gaze focus and facial expressions) as well as task engagement (determined by the level of physical activity) to determine child engagement states. We then conducted a virtual exploratory user study to evaluate the impact of mixed robot roles on user engagement and found no statistically significant difference in the children’s engagement in single-role and multiple-role interactions. While the children were observed to respond positively to both robot behaviors, they preferred the music-driven leader role over the movement-driven follower role, a result that can partly be attributed to the virtual nature of the study. Our findings support the utility of such a platform in practicing physical activity but indicate that further research is necessary to fully explore the impact of each robot role. 

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