Emotional Regulation of Adults with high-functioning ASD Using Pupillometry from Real and Artificial Stimuli

Abstract

Social interaction deficits are a core characteristic of Autism Spectrum Disorder (ASD) and are often linked to atypical attentional processing of socially relevant cues (Baron-Cohen, 1995). Understanding these mechanisms is essential for developing sensor-based learning analytics in serious games. Eye movement analysis provides a non-invasive and cost-effective approach to derive digital biomarkers for ASD (Frazier et al., 2018). In this study, we examined pupillometry features at rest and during emotion processing, building on earlier findings that pupil dynamics reflect autonomic and cognitive function in ASD (Anderson et al., 2013; Pszeida et al., 2025). We specifically tested whether video-based virtual stimuli elicit pupillometric responses comparable to real human faces.Participants included N = 20 neurodiverse (ND) adults with ASD (M = 26.20, SD = 4.78; 50% female) and N = 20 neurotypical (NT) controls (M = 23.71, SD = 2.93; 33% female). All participants completed a standardized psychological test battery and the digital Emotion Evaluation and Regulation Test (EERT), which presented video stimuli of six emotions (joy, sadness, fear, disgust, anger, neutral). Stimuli were drawn from (i) the validated FACES database (Ebner et al., 2010) and (ii) artificially generated virtual character faces (Poglitsch et al., 2024). ASD classification was supported by the RAADS-R screening tool (Ritvo et al., 2011), with the ND group showing markedly elevated scores (ND: M = 128.72, SD = 38.36; NT: M = 17.24, SD = 12.47). Cognitive profiles indicated a high-functioning ASD subgroup based on CFT-20R fluid intelligence scores (Weiß, 2008; ND: M = 116.95, SD = 11.22; NT: M = 114.48, SD = 13.05).Participants viewed four individual actors expressing all six emotions in real and virtual face videos, each lasting 2 seconds. The first 2 seconds following stimulus onset were analyzed relative to a 500 ms baseline period. Pupillometric features included peak dilation latency, dilation amplitude, and time-to-recovery.For emotion ‘anger’ expressed by real faces, ND participants showed a delayed time-to-peak dilation of approximately 200 ms, increased dilation amplitude by ~0.1 mm, and prolonged recovery time by ~600 ms compared to NT individuals. Neutral real faces produced group differences only in peak latency (~200 ms). Virtual anger stimuli yielded comparable effects: ND participants again demonstrated delayed peak latency (~200 ms), larger dilation amplitude (~0.1 mm), and delayed recovery (~500 ms).These results indicate that both real and virtual emotional face videos elicit distinct pupillometric signatures in high-functioning ASD. Latency to peak dilation, amplitude increase, and delayed recovery consistently differentiated ND from NT participants. The findings suggest that pupil dynamics may serve as reliable biomarkers of emotion regulation and attentional processing in ASD and highlight the feasibility of using virtual stimuli for scalable assessment in digital and game-based environments.

Keywords: Digital Health, Autism Spectrum Disorder, Pupillometry, Emotional Regulation

DOI: 10.54941/ahfe1007374