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

cs.HCMar 24, 2026

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

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

Organizations: 1,2,4Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, U.P, India. · Department of Humanities and Social Sciences, Indian Institute of Technology Kanpur, Kanpur, 208016, U.P, India.

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.

Figures & tables

Parameter	Value
Subjects (Total)	75 children (45 TD, 30 ASD)
Manual data used	63 children (39 TD, 24 ASD)
Videos Used	116 (31*3 TD + 23 ASD)
Duration per Session	5-7 minutes per child
Name-Calling Instances	15 (3 per stimulus, randomized order)
Stimuli Types	5 (SM, SW, NM, NW, NR)

Table 1: Data Specifications and Recording Details

Stimulus	Mean EOP (%)	Duration (s)	Latency (s)
SM	68.42 ± 9.14	5.21 ± 1.37	1.74 ± 0.56
SW	64.87 ± 10.02	5.36 ± 1.28	1.69 ± 0.49
NM	52.73 ± 7.86	6.02 ± 1.43	1.51 ± 0.47
NW	48.34 ± 8.15	6.19 ± 1.55	1.47 ± 0.44
NR	44.65 ± 7.11	6.81 ± 1.68	1.42 ± 0.39

Table 2: Descriptive statistics (Mean ± SD) of behavioral parameters across five stimuli for the typical group.

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.

Group	Stimulus	$\mathbf{\mu}$	$\mathbf{\sigma}$	Full (%)	Partial (%)	None (%)
Typical	SM	1.59	0.66	68.38	22.22	9.40
Typical	SW	1.50	0.82	70.94	8.55	20.51
Typical	NM	1.41	0.77	58.12	24.79	17.09
Typical	NW	1.25	0.77	44.44	35.90	19.66
Typical	NR	1.28	0.82	51.28	25.64	23.08

Table 3: Descriptive statistics of mean response (

\mu

), standard deviation (

\sigma

), and percentage distribution of response types across stimuli for typical children.

Group	Stimulus	$\mathbf{\mu}$	$\mathbf{\sigma}$	Full (%)	Partial (%)	None (%)
Autistic	SM	0.78	0.88	29.17	19.44	51.39
Autistic	SW	0.96	0.85	33.33	29.17	37.50
Autistic	NM	1.56	0.67	65.28	25.00	9.72
Autistic	NW	1.56	0.73	69.44	16.67	13.89
Autistic	NR	1.74	0.56	79.17	15.28	5.56

Table 4: Descriptive statistics of mean response (

\mu

), standard deviation (

\sigma

), and percentage distribution of response types across stimuli for autistic children.

Figure 4: Ridgeline density distributions showing within-group differences across stimuli.

Panel of Figure 5 — 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.

Panel of Figure 6 — Figure 6: Turn-wise response maps across the three name-calling trials. (a) Typical group responses. (b) ASD group responses.

Figure 7: Cronbach’s $\alpha$ values across stimulus conditions for the typical and autistic groups.

Dimension	Dominant in TD	Dominant in ASD	Interpretation
Response magnitude	Human voices (SM, SW)	Robot voices (NM, NW, NR)	Modality-specific sensitivity
Reliability ( $\alpha$ )	High for human	High for robot	Temporal consistency preference
Turn slope ( $S$ )	Negative	Flat or positive	Habituation vs sustained engagement
Variance	Low for human	Low for robot	Selective stability
Signal discrimination ( $d^{\prime}$ )	Low	High for robot stimuli	Diagnostic separability

Table 5: Summary of group-dominant metrics across analytical dimensions.

Stimulus	Metric	$U$	$p$ -value	Cohen’s $d$
NM	Mean EOP	1210.5	$<1\times 10^{-9}$	$-1.08$
NW	Mean EOP	1211.0	$<1\times 10^{-9}$	$-1.11$
NR	Mean EOP	795.0	$<1\times 10^{-14}$	$-1.55$
NM, NW, NR	Latency, Duration	ns	$>0.16$	–

Table 6: Between-group Mann–Whitney

U

analysis across stimuli.

Panel of Figure 8 — Figure 8: (a) Latency and (b) Mean EOP distribution curves showing deviation from normality, justifying rank-based analyses.

Group	Metric	$H$	$p$ -value	Key Contrast
Autistic	Duration	19.56	$5.7\times 10^{-5}$	NR $>$ NW
Autistic	Mean EOP	8.74	0.0126	NR $>$ NW
Typical	Mean EOP	6.80	0.0334	NM $>$ NR
Both	Latency	–	$>0.40$	not significant

Table 7: Within-group Kruskal–Wallis and post-hoc comparisons.

Figure 9: Raincloud plots showing Duration distributions across stimuli for autistic and typical participants. Longer durations under NR are evident for autistic children.

Pair	Group	$\rho$	$p$ -value
Latency–Duration	Autistic	$-0.47$	$4.8\times 10^{-12}$
Latency–Duration	Typical	$-0.45$	$3.8\times 10^{-15}$
Duration–EOP	Autistic	$0.28$	$5.5\times 10^{-5}$
Duration–EOP	Typical	$0.35$	$1.9\times 10^{-9}$
Latency–EOP	Both	ns	$>0.6$

Table 8: Spearman correlations among behavioral metrics.

Variable	Autistic (range)	Typical (range)
Duration	0.726–0.825	0.668–0.780
Mean EOP	0.669–0.836	0.395–0.717
Latency	0.224–0.806	0.146–0.361

Table 9: Reliability coefficients (Cronbach’s

\alpha

) for behavioral metrics across groups.

Group	Variable	Stimulus NM	Stimulus NW	Stimulus NR	Interpretation
Autistic	Duration	$-0.14$	$-0.40$	$+0.02$	Duration slightly decreases for NM/NW but stabilizes under NR.
Autistic	Latency	$-0.01$	$+0.01$	$-0.01$	Essentially flat; almost no learning trend.
Autistic	Mean EOP	$-0.07$	$-0.90$	$+0.56$	Engagement declines slightly under NM,NW, but increases under NR.
Typical	Duration	$-0.94$	$-1.15$	$-0.25$	Strong habituation; Drop with turn
Typical	Latency	$+0.01$	$+0.10$	$+0.01$	Slight positive slopes; marginally faster response initiation.
Typical	Mean EOP	$-3.13$	$-7.17$	$+2.63$	Clear divergence: engagement decays under NM/NW, rises under NR.

Table 10: Temporal slopes (

\beta_{1}

) across stimuli and groups with interpretation.

Analytical Level	Model/Test Applied	Key Outcome	Supporting Output
Between-group	Mann–Whitney $U$	Autistic $>$ Typical for Mean EOP across all stimuli	Table 6 , Fig. 8
Within-group	Kruskal–Wallis + Holm MWU	NR $>$ NW for Autistic, NM $>$ NR for Typical	Table 7 , Fig. 9
Correlation	Spearman $\rho$	Negative (Latency–Duration), Positive (Duration–EOP)	Table 8
Reliability	Cronbach $\alpha$	High for Duration; moderate for EOP; variable for Latency	Table 9
Temporal trends	Linear slope $\beta_{1}$	Sustained engagement under NR; habituation under NM/NW	Table 10

Table 11: Overview of analytical models and corresponding findings.

Autism Spectrum Disorder

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Analysis of Motor Signatures of Social Adaptation in Autism for Efficient Human-Centric Systems

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Autism Spectrum Disorder

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Dance imitation integrates motor planning, sensorimotor integration, and social cognition, offering a sensitive framework to characterize motor behavior in autism. In this work, we explore a computational analysis framework to identify potential biomarkers that allow the design and development of improved medical and human-machine systems. We analyzed 3D motion capture data from autistic and neurotypical adults performing dance imitation under solo and socially-framed duo conditions. Methodologically, using Dynamic Time Warping, we quantified movement consistency and propose the Social Context Sensitivity Index (SCSI) to measure modulation of variability by social framing. These features were then used on a classifier to discriminate subjects into autistic or neurotypical groups. Results show that neurotypical adults exhibited increased movement variability in socially-framed imitation, especially in upper and lower limbs, whereas autistic adults maintained consistent movement across contexts. Classification achieved 79.2% balanced accuracy in distinguishing groups. These findings suggest that social context sensitivity in motor imitation constitutes a robust biomarker of autism-related motor behavior, highlighting the importance of social modulation in motor assessments and informing the development of inclusive human-centric technologies.

Abstract

Autism Spectrum Disorder

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