In this study we compared the latencies and amplitudes of pupillary light responses, recorded by a Tobii Pro Spark eye-tracker and a setup combining PupilEXT, a free open-source software for pupillometry, with an industrial camera. Pupil size was recorded simultaneously with both systems while illumination was provided by the Spark. Results show that neither pupillary response latencies nor amplitudes differ systematically between the two, supporting the claim that PupilEXT can be used in event-related pupillometry.

Wearable eye tracking in unconstrained settings is often compromised by slippage, yet the direct relationship between physical frame displacement and gaze error remains unexplored. This study addresses this gap combining eye tracking and motion capture to evaluate the slippage robustness of Pupil Labs Neon, Tobii Pro Glasses 3 and ViewPointSystems Lite eye trackers. Data were collected as twelve participants performed tasks involving facial expressions, induced glasses motion and locomotion. Results indicate that the Tobii Pro Glasses 3 are the most slippage-robust, maintaining an average gaze error below 2.5° regardless of movement. Conversely, the ViewPointSystems Lite exhibited large errors scaling with displacement (up to 29.3° on average) during induced motion tasks. The Pupil Labs Neon demonstrated resilience against large errors (<3.5° on average). Our findings enable researchers to anticipate the gaze error in unconstrained environments, and push manufacturers to provide realistic specifications of slippage instead of claims of “slippage-robust“ eye tracking.

We present PyeLink and SyeLink, two complementary open-source Python tools for running eye-tracking experiments and parsing data from SR Research Ltd. EyeLink eye tracking hardware. PyeLink simplifies experiment creation through plug-and-play support for multiple display backends (Pygame, PsychoPy, Pyglet) while enabling two features not available in existing tools: recording data during calibration and validation phases, and simultaneously recording raw P-CR data alongside calibrated gaze data. These enriched data files cannot be processed by existing parsers, which motivated the development of SyeLink. SyeLink parser processes recorded files and makes the data accessible through both Python API and command-line interface. SyeLink converts raw ASC data into structured JSON and CSV formats and supports visualization of calibration and validation results. Together, these tools provide a complete Python workflow for running eye-tracking experiments with enriched data recording capabilities while exposing all configurable aspects of EyeLink eye trackers.

We explore whether SAM2, a vision foundation model, can be used for accurate localization of eye image features that are used in lab-based eye tracking: corneal reflections (CRs), the pupil, and the iris. We prompted SAM2 via a typical hand annotation process that consisted of clicking on the pupil, CR, iris and sclera for only one image per participant. SAM2 was found to support better spatial precision in the resulting gaze signals for the pupil (> 44% lower RMS-S2S), but not the CR and iris, than traditional image-processing methods or two state-of-the-art deep-learning tools. Providing more frames with prompts to initialize SAM2 did not improve performance. We conclude that SAM2’s powerful zero-shot segmentation capabilities provide an interesting new avenue to explore in high-resolution lab-based eye tracking. We provide our adaptation of SAM2’s codebase that allows segmenting videos of arbitrary duration and prepending arbitrary prompting frames.

In contemporary research, microsaccade detection is typically performed using the calibrated gaze-velocity signal acquired from a video-based eye tracker. To generate this signal, the pupil and corneal reflection (CR) signals are subtracted from each other and a differentiation filter is applied, both of which may prevent small microsaccades from being detected due to signal distortion and noise amplification. We propose a new algorithm where microsaccades are detected directly from uncalibrated pupil-, and CR signals. It is based on detrending followed by windowed correlation between pupil and CR signals. The proposed algorithm outperforms the most commonly used algorithm in the field (Engbert & Kliegl, 2003), in particular for small amplitude microsaccades that are difficult to see in the velocity signal even with the naked eye. We argue that it is advantageous to consider the most basic output of the eye tracker, i.e. pupil-, and CR signals, when detecting small microsaccades.

Current video-based eye trackers are not suited for calibration of patients who cannot produce stable and accurate fixations. Reliable calibration is crucial in order to make repeatable recordings, which in turn are important to accurately measure the effects of a medical intervention. To test the suitability of different calibration polynomials for such patients, inaccurate calibration data were simulated using a geometric model of the EyeLink 1000 Plus desktop mode setup. This model is used to map eye position features to screen coordinates, creating screen data with known eye tracker data. This allows for objective evaluation of gaze estimation performance over the entire computer screen. Results show that the choice of calibration polynomial is crucial in order to ensure a high repeatability across measurements from patients who are hard to calibrate. Higher order calibration polynomials resulted in poor gaze estimation even for small simulated fixation inaccuracies.

This study addresses differences in the design of computer-based learning materials—text with or without pictures—and the aim is to show in what way these differences affect learning outcomes. In total, 46 young adults participated: 19 with dyslexia and 27 controls. Approximately half of each group received the condition text only, and half received the text and an integrated picture. The learning material was presented on computer screens, and the participants’ viewing patterns were registered by eye-tracking. The respondents answered text-based and picture-based questions, as well as oral questions, during the experiment. The assumptions about learning are based on variation theory and on the importance of contrasts in discerning important aspects of the learning material. The results show that whether material of the same content (surrealism) is presented in text only (without explicit contrasts embedded in the text) or in text and picture form (which offers a contrast) affects learning outcomes, particularly for the participants with dyslexia, who showed a fourfold increase from pre-test to post-test (from .10 to .40).

Data quality is essential to the validity of research results and to the quality of gaze interaction. We argue that the lack of standard measures for eye data quality makes several aspects of manufacturing and using eye trackers, as well as researching eye movements and vision, more difficult than necessary. Uncertainty regarding the comparability of research results is a considerable impediment to progress in the field. In this paper, we illustrate why data quality matters and review previous work on how eye data quality has been measured and reported. The goal is to achieve a common understanding of what data quality is and how it can be defined, measured, evaluated, and reported.

A great need exists in many fields of eye-tracking research for a robust and general method for scanpath comparisons. Current measures either quantize scanpaths in space (string editing measures like the Levenshtein distance) or in time (measures based on attention maps). This paper proposes a new pairwise scanpath similarity measure. Unlike previous measures that either use AOI sequences or forgo temporal order, the new measure defines scanpaths as a series of geometric vectors and compares temporally aligned scanpaths across several dimensions: shape, fixation position, length, direction, and fixation duration. This approach offers more multifaceted insights to how similar two scanpaths are. Eight fictitious scanpath pairs are tested to elucidate the strengths of the new measure, both in itself and compared to two of the currently most popular measures - the Levenshtein distance and attention map correlation.

Offline foveation is a technique to improve the compression efficiency of digitized video. The general idea behind offline foveation is to blur video regions where no or a small number of previewers look without decreasing the subjective quality for later viewers. It relies on the fact that peripheral vision is reduced compared to central vision, and the observation that during free-viewing humans' gaze positions generally coincide when watching video. In this article, we conduct two experiments to assess how offline foveation affects viewing behavior and subjective quality. In the first experiment, 15 subjects free-viewed six video clips before and after offline foveation whereas in the second experiment we had 17 subjects assessing the quality of these videos after one, two, and three consecutive viewings. Eye movements were measured during the experiments. Results showed that, although offline foveation prior to encoding with H.264 yielded data reductions up to 52% (20% average) on the tested videos, it had little or no effect on where people looked, their intersubject dispersion, fixation duration, saccade amplitude, or the experienced quality during first-time viewing. However, seeing the videos more than once increased the intersubject dispersion and decreased the subjective quality. In view of these results, we discuss the usage of offline foveated video in practical applications.