Identifying fixations and saccades in eye-tracking protocols

TLDR

Fixation identification, the separation of fixations and saccades in eye‑tracking data, is crucial for higher‑level analyses, yet existing algorithms are often informally described and rarely compared. This study introduces a taxonomy that classifies fixation‑identification algorithms based on their use of spatial and temporal information. Using the taxonomy, the authors describe five representative algorithms and evaluate them qualitatively across several characteristics. The comparisons reveal insights that inform the selection and application of fixation‑identification algorithms in future research.

Abstract

The process of fixation identification—separating and labeling fixations and saccades in eye-tracking protocols—is an essential part of eye-movement data analysis and can have a dramatic impact on higher-level analyses. However, algorithms for performing fixation identification are often described informally and rarely compared in a meaningful way. In this paper we propose a taxonomy of fixation identification algorithms that classifies algorithms in terms of how they utilize spatial and temporal information in eye-tracking protocols. Using this taxonomy, we describe five algorithms that are representative of different classes in the taxonomy and are based on commonly employed techniques. We then evaluate and compare these algorithms with respect to a number of qualitative characteristics. The results of these comparisons offer interesting implications for the use of the various algorithms in future work.