Scanpath analysis provides a powerful window into visual behavior by jointly capturing the spatial organization and temporal dynamics of gaze. By linking perception, cognition, and oculomotor control, scanpaths offer rich insights into how individuals explore visual scenes and accomplish task goals. Despite decades of research, however, the field remains methodologically fragmented, with a wide diversity of representations and comparison metrics that complicate interpretation and methodological choice. This article reviews computational approaches for the characterization and comparison of scanpaths, with an explicit focus on their underlying assumptions, interpretability, and practical implications. We first survey representations and metrics designed to describe individual scanpaths, ranging from geometric descriptors and spatial density representations to more advanced approaches such as attention maps, recurrence quantification analysis, and symbolic string encodings that capture temporal regularities and structural patterns. We then review methods for comparing scanpaths across observers, stimuli, or tasks, including point-mapping metrics, elastic alignment techniques, string-edit distances, saliency-based measures, and hybrid approaches integrating spatial and temporal information. Across these methods, we highlight their respective strengths, limitations, and sensitivities to design choices such as discretization, spatial resolution, and temporal weighting. Rather than promoting a single optimal metric, this review emphasizes scanpath analysis as a family of complementary tools whose relevance depends on the research question and experimental context. Overall, this work aims to provide a unified conceptual framework to guide methodological selection, foster reproducibility, and support the meaningful interpretation of gaze dynamics across disciplines.
Laborde et al. (Tue,) studied this question.