Is This It? Benchmarking Scanpath Metrics for Information Display

Yao Wang, Junichi Nagasawa, Danqing Shi, Chuhan Jiao, Yue Jiang, Andreas Bulling

Proc. Eye Tracking Metrics Workshop (METR), 2026.

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Abstract

Scanpath prediction is a fundamental task in human visual attention research, aiming to simulate user viewing behaviour for given stimuli. While scanpath prediction methods have matured in natural scenes, recent research has expanded to information displays, such as graphical user interfaces and data visualisations. However, there is currently no consensus on which scanpath metrics to use for evaluation, raising concerns regarding the validity and comparability of the proposed methods. This paper benchmarks ten commonly used scanpath metrics across the MASSVIS and UEyes datasets by comparing model predictions with empirical gaze data. We evaluate these metrics with subjective expert ratings of scanpath similarity. Our analysis reveals that vector-based and region-based metrics align more closely with expert ratings than pixel-based and recurrence-based metrics. Based on these findings, we provide best practices for evaluating visual scanpaths in information displays, emphasising the urgent need for appropriate metrics to ensure the validity of future research.

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