Combined eye tracking and electroencephalography during referential selection in dyadic interaction.

Abstract

Understanding language in real-world interaction requires methods that integrate auditory, visual, and neural signals. We present a proof-of-concept study combining mobile electroencephalography (EEG) and eye tracking to investigate referential selection during dyadic communication. Using a simplified version of the director task, pairs of participants engaged in naturalistic object-movement instructions while EEG and gaze data were recorded and synchronized via LabStreamingLayer, allowing us to calculate overlap-corrected, regression-based event-related potentials (rERPs) time-locked to spoken nouns and to participants' fixations, focusing on the N400/P300 (300-500 ms) and later (500-800 ms) time windows. Our results show that gaze behavior and fixation-related potentials provide crucial information for interpreting language-related ERPs: Targets competing with occluded referents elicited a stronger posterior positivity consistent with greater attentional demands. Fixation timing systematically modulated neural responses. Contrary to predictions, competitors received little visual attention, indicating that participants prioritized task-relevant information. These findings highlight the potential of multimodal data integration for understanding attentional and predictive mechanisms in real-world communication. Importantly, we show that established analytic techniques, such as artifact subspace reconstruction, independent component analysis, and linear deconvolution for ERP calculation, apply to noisy, naturalistic datasets. In addition, we provide a subsampling sensitivity approach to test the stability of the effects across different sample sizes. This study provides a methodological framework linking gaze and neural activity during interactive language processing. All preprocessing and analysis scripts, along with example datasets, are openly available on OSF ( https://osf.io/5ds4z ; a modified and optimized Python implementation is also available on GitHub: https://github.com/XlinCLab/DGAME ).