Feasibility of multimodal behavioral, gaze, and EEG measurement of natural scene segmentation in autism

Abstract

Understanding how individuals organize complex natural scenes into perceptual segments is essential for explaining both typical and atypical sensory processing. In Autism Spectrum Disorder (ASD), differences in visual segmentation are widely documented. However, existing paradigms often rely on simplified stimuli and do not capture the dynamic, naturalistic processes underlying real world scene segmentation. To overcome those limitations, in this work we present a novel, multimodal experimental framework. The framework combines precisely timed, trial-based perceptual measurements with electroencephalography (EEG) to examine how neural activity aligns temporally with segmentation decisions, and with eye-tracking to examine visual exploration strategies and attention allocation. To demonstrate the framework, we report basic characterization results from a pilot study. Neurotypical (NT) participants and participants with ASD aged 16 years and above viewed natural scenes and textures and indicated whether two cued image regions belonged to the same or different perceptual segments. Each trial used a different pair of cues, and responses across trials enabled the estimation of subjectively perceived segmentation maps and the associated uncertainty. Both cohorts performed the task successfully and produced interpretable segmentation maps. Analysis of reaction time distributions, gaze profiles, and trial-aligned neural responses give us a window into cohort differences and the opportunity to study individual heterogeneity. Together, these findings establish a reproducible method for studying the temporal and spatial components of natural scene segmentation and illustrate that meaningful behavioral and neural distinctions in ASD may be studied with this approach. This framework provides a methodological bridge between controlled experimental stimuli and real-world perception in both neurotypical and neurodivergent populations.