Eye-movement event detection meets machine learning

Abstract

This paper presents a comparison of 10 machine learning algorithms in eye‑movement event detection task. The goal was to build a universal algorithm, which could work with any type of the eye-tracking data. Results show that even if tested on noisy data, sampled at a broad range of sampling rates, 5 out of 10 machine learning based universal models perform better than state-of-the-art algorithms. Even more, 7 machine learning based specialist classifiers, trained to work with high quality data, outperform expert coders as reported by Larsson et al. (2015).