https://doi.org/10.1140/epjs/s11734-026-02324-9
Regular Article
Identification of fixations and saccades in eye-tracking data using adaptive threshold-based methods
1
Department of Computer Science, OsloMet, Oslo Metropolitan University, P.O. Box 4, St. Olavs Plass, 0130, Oslo, Norway
2
OsloMet Artificial Intelligence Lab, OsloMet, Oslo Metropolitan University, Pilestredet 52, 0166, Oslo, Norway
3
School of Economics, Innovation and Technology, Kristiania University of Applied Sciences, Kirkegata 24-26, 0153, Oslo, Norway
4
Numerical Analysis and Scientific Computing, Simula Research Laboratory, 0164, Oslo, Norway
a
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Received:
29
December
2025
Accepted:
15
April
2026
Published online:
7
May
2026
Abstract
Properties of ocular fixations and saccades are highly stochastic during many experimental tasks, and their statistics are often used as proxies for various aspects of cognition. Although distinguishing saccades from fixations is not trivial, experimentalists generally use common ad-hoc thresholds in detection algorithms. This neglects inter-task and inter-individual variability in oculomotor dynamics, and potentially biases the resulting statistics. In this article, we introduce and evaluate an adaptive method based on a Markovian approximation of eye-gaze dynamics, using saccades and fixations as states such that the optimal threshold minimizes state transitions. Applying this to three common threshold-based algorithms (velocity, angular velocity and dispersion), we evaluate the overall accuracy against a multi-threshold benchmark as well as robustness to noise. We find that a velocity threshold achieves the highest baseline accuracy (90–93%) across both free-viewing and visual search tasks. However, velocity-based methods degrade rapidly under noise when thresholds remain fixed, with accuracy falling below 20% at high noise levels. Adaptive threshold optimization via K-ratio minimization substantially improves performance under noisy conditions for all algorithms. Adaptive dispersion thresholds demonstrate superior noise robustness, maintaining accuracy above 81% even at extreme noise levels (
px), though a precision-recall trade-off emerges that favours fixation detection at the expense of saccade identification. In addition to demonstrating our parsimonious adaptive thresholding method, these findings provide practical guidance for selecting and tuning classification algorithms based on data quality and analytical priorities.
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjs/s11734-026-02324-9.
Charles Orioma, Josef Krivan, Alexander Szorkovszky and Shailendra Bhandari have contributed equally to this work.
© The Author(s) 2026
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