Assessing the Impact of Distractions Using a Virtual-Reality-Based GO/NOGO Task

Abstract

The GO/NOGO task provides an objective assessment of a subject's attention and response inhibition and is typically given to subjects without any unexpected distractions. Studying the impact of distractions is important from the therapeutic viewpoint as distractions may occur during exposure therapy and degrade treatment efficacy. In this study, we utilized a virtual classroom integrated with electroencephalogram (EEG) for a GO/NOGO task with multimode environmental distractions to study the impact of distractions on behavioral and neuronal activities. Thirty healthy male adults were recruited. Statistical analysis and machine learning methods were employed to analyze the behavioral and neuronal data. The results demonstrated no significant behavioral differences between conditions with and without distractions. However, the impacts of distractions manifested in the enhancement of frequency-specific power, including theta, alpha, and gamma oscillations in GO trials, as well as beta power and the N200 peak in NOGO trials, highlighting their role in attention regulation and response inhibition. Finally, machine learning result analysis identified significant differences between conditions with and without distractions using EEG features, achieving an accuracy rate of 98.3%. In conclusion, we found that introducing distractions into a GO/NOGO task provides a deeper understanding of the neuronal correlates of distractions, and these findings can inform the development of therapeutic strategies for attention-related disorders.