Patterns of Eye Movement Influenced by Multitasking Workload: A Clustering Analysis

Abstract

Although multitasking has been studied in the past few decades, there has been a lack of investigation on how individuals’ multitasking performance can be predicted using eye movement data. To address this gap, this study proposed an exploratory approach to understand the manifestation of eye movement patterns that could provide diagnostic and predictive information of multitasking performance. Nineteen participants completed Multi-Attribute Task Battery (MATB-II) experiments under both low and high workloads and their eye movement and MATB-II task performance were collected. We applied a hierarchical clustering method that classified the participants into three clusters – clusters with small, medium, and large number of fixations. Then, we compared the MATB-II performance of the three clusters. The results s howed significant differences in average reaction time to stimuli and average error count among the three clusters. Our study showed that hierarchical clustering of eye fixations can effectively predict multitasking performance.