Human vs. NAO: A Computational-Behavioral Framework for Quantifying Social Orienting in Autism and Typical Development

TL;DR

This study develops a computational framework to quantify social orienting behaviors in children with autism versus typical development, comparing responses to human and robot stimuli using video analysis.

Contribution

It introduces a novel, fine-grained, video-based method for assessing social responses to human and robot cues in children with autism and typical development.

Findings

Children with autism show different response patterns to human and robot stimuli.

The framework accurately measures eye contact, response latency, and facial orientation.

Results inform robot-assisted assessment and understanding of social deficits in autism.

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.