Quantifying Visual Attention of Teams During Workload Transitions Using AOI-Based Cross-Recurrence Metrics

Abstract

Cross-recurrence quantification analysis (CRQA) metrics may offer a means to provide information about the quality of collaboration in real-time. The goal of the present work is to use Area of Interest (AOI) based CRQA metrics to analyze the eye-tracking data of 10 pairs who participated in a shared unmanned aerial vehicle (UAV) command and control task. We are interested in how teams respond to workload transitions and how it affects AOI-based CRQA metrics. The results showed that as workload increased, team members spent a longer time on the same task which may indicate that they are coordinating together on a task, or they are not adapting and getting “trapped” in certain tasks. The findings suggest that CRQA AOI-based metrics are sensitive to workload changes and validate these metrics in unraveling the visual puzzle of how workload impacts scanpath patterns which contribute to quantifying the adaptation process of pairs over time. This also has the potential to inform the design of real-time technology in the future.