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

Vartika Narayani Srinet, Anirudha Bhattacharjee, Braj Bhushan, Bishakh Bhattacharya

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.

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

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.
Paper Structure (41 sections, 11 equations, 9 figures, 11 tables, 1 algorithm)

This paper contains 41 sections, 11 equations, 9 figures, 11 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Experimental Participants and Data Acquisition
  5. Participants
  6. Experimental Design
  7. Data Collection and Recording Procedure
  8. Manual Behavioral Coding and Analysis
  9. Behavioral Response Coding
  10. Descriptive and Reliability Analyses
  11. Non-Parametric Inference and Effect Size
  12. Temporal Trends and Sensitivity Indices
  13. Video-Based Feature Extraction Framework
  14. Conceptual Overview
  15. Frame Decoding and Alignment
  16. ...and 26 more sections

Figures (9)

  • Figure 1: The Experimental setup
  • Figure 2: Distribution of Mean Eye-Openness Percentage (EOP) across five stimuli for typical children. Human stimuli (SM, SW) show higher EOP, while robotic stimuli (NM, NW, NR) display reduced but sustained engagement.
  • Figure 3: Group-level behavioral response patterns across all stimuli.
  • Figure 4: Ridgeline density distributions showing within-group differences across stimuli.
  • Figure 5: Three-dimensional response surfaces illustrating mean response patterns across turns and stimulus conditions. (a) Typical group responses show stronger engagement with socially familiar cues. (b) ASD group responses demonstrate greater stability and higher engagement for robotic modalities across repeated turns.
  • ...and 4 more figures