Exploring Eye Tracking Measures to Understand Operator Performance

Abstract

Eye tracker technology is a growing and viable source for more sensitive, unobtrusive, and objective measures of operator performance and cognitive state. Several eye movement metrics have been validated in the empirical literature, but caution is advised when linking low level eye movements (e.g., fixations) to high level cognitive constructs (e.g., workload). Valid analysis of eye movement data is vulnerable to output interpretation, metric granularity, and incomplete views of operator performance. To address these issues, more research is needed to exploit contextual information from other performance measures, identify metric deficiencies, and develop useful composite measures. Individual eye movement metrics alone provide an insufficient picture of operator cognition and performance, but when purposefully combined with other metrics (e.g., other physiological sensor data), offer a more comprehensive look at operator performance. Understanding why operator errors occur can help researchers identify information-processing bottlenecks, possibly allowing designers to find ways to improve performance.