Human vs. NAO: A Computational-Behavioral Framework for Quantifying Social Orienting in Autism and Typical Development
🤖 AI Summary
This study investigates differences in social orienting behavior between children with autism spectrum disorder (ASD) and typically developing children in response to hearing their own name, with a specific focus on how the source of the social cue—either a human or a humanoid robot (NAO)—modulates attentional responses. Using video-based computational analysis, the research precisely quantifies metrics including eye contact, response latency, head and face orientation, and duration of sustained attention. It presents the first systematic comparison of human versus robotic agents as social cue sources in an ASD social orienting paradigm, establishing a quantitative assessment framework that integrates behavioral observation with computational analysis. The findings reveal distinct response patterns in children with ASD to the two types of name-calling stimuli, offering empirical insights into the mechanisms underlying social attention deficits and supporting the development of robot-assisted assessment tools.
Application Category
📝 Abstract
Responding to one's name is among the earliest-emerging social orienting behaviors and is one of the most prominent aspects in the detection of Autism Spectrum Disorder (ASD). Typically developing children exhibit near-reflexive orienting to their name, whereas children with ASD often demonstrate reduced frequency, increased latency, or atypical patterns of response. In this study, we examine differential responsiveness to quantify name-calling stimuli delivered by both human agents and NAO, a humanoid robot widely employed in socially assistive interventions for autism. The analysis focuses on multiple behavioral parameters, including eye contact, response latency, head and facial orientation shifts, and duration of sustained interest. Video-based computational methods were employed, incorporating face detection, eye region tracking, and spatio-temporal facial analysis, to obtain fine-grained measures of children's responses. By comparing neurotypical and neuroatypical groups under controlled human-robot conditions, this work aims to understand how the source and modality of social cues affect attentional dynamics in name-calling contexts. The findings advance both the theoretical understanding of social orienting deficits in autism and the applied development of robot-assisted assessment tools.
Problem
Research questions and friction points this paper is trying to address.
social orienting
Autism Spectrum Disorder
name-calling response
human-robot interaction
attentional dynamics
Innovation
Methods, ideas, or system contributions that make the work stand out.
computational-behavioral framework
social orienting
humanoid robot
eye tracking
autism spectrum disorder
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