Autism Spectrum Disorder (ASD) is a common and costly neurodevelopmental disorder with core deficits related to social communication. There is growing empirical support suggesting that early intervention can significantly improve specific early social communication skills (i.e., joint attention and social orienting) and that such improvements partially mediate improvements in other critical developmental areas, including broad social and language outcomes. Unfortunately, many families struggle greatly to access appropriate, effective early intervention services at young ages due to resource limitations, including: limited access to services, delays and waits related to identification/treatment, a limited number of expert providers, as well as barriers related to the cost of treatment. There is an urgent need for the development of new tools and paradigms that can help advance access to powerful interventions across resource-strained systems. One potential new paradigm involves attempting to harness the abilities of sophisticated technological platforms to address ASD concerns. The primary goal of the current research is to refine, augment, and test the ability of an innovative, intelligent technological architecture and system specifically designed to address early social communication vulnerabilities in children with ASD. We explicitly do not propose this technology as a stand-alone intervention nor as a replacement for existing necessary intervention and care at later ages. Rather, we propose it as a critical test of a new technological tool and paradigm for thinking about advanced early detection and action regarding early social communication concerns and ASD. We will test the ability of our innovative three- dimensional intelligent learning environment to detect and meaningfully respond to core social communication behaviors evidenced during caregiver interactions. Simply, the environment will be designed to help young children more often respond to their names, follow gaze and common gestures, and coordinate social attention in interactions with their caregivers. Future rigorous study and understanding of this system?s ability to bolster these early pivotal social communication skills could lay the groundwork for radically different future intervention approaches. In the current work, we will first refine and augment our existing intelligent technological architecture, currently capable of detecting and inferring social attention in real-time as well as providing prompts based on this detection, to create a robust and powerful blended technological and social learning platform. We will then evaluate the ability of this technology to operate fluidly and autonomously with a clinical sample. The expected benefit of this research could be tremendous. It would, for the first time, allow for the systematic evaluation of an innovative, technologically-mediated ASD intervention platform for very young children that could readily translate into meaningful real-world use at a critical point in development.

Public Health Relevance

As a record number of children are being diagnosed with Autism Spectrum Disorder (ASD), there is an urgent need to develop powerful interventions that can be delivered across resource strained environments. We propose a new technology-based paradigm that will help caregivers teach early social communication skills to young children with ASD. This intelligent environment will automatically detect children?s attention, instruct caregivers how to help bolster attention, and also autonomously activate aspects of the environment itself to ultimately help young children learn.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Exploratory/Developmental Grants (R21)
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Special Emphasis Panel (ZMH1)
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Gilotty, Lisa
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Vanderbilt University Medical Center
United States
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