Corneal reflection (glint) detection plays an important role in pupil-corneal reflection (P-CR) eye tracking, but in practice it is often handled as heuristics embedded within larger systems, making reproducibility difficult across hardware setups. We introduce a 2D geometry-driven, constellation-based pipeline for mulit-glint detection and matching, focusing on reproducibility and clear evaluation. Inspired by lost-in-space star identification, we treat glints as structured constellations rather than independent blobs. We propose a Similarity-Layout Alignment (SLA) procedure which adapts constellation matching to the specific constraints of multi-LED eye tracking. The framework brings together controlled over-detection, adaptive candidate fallback, appearance-aware scoring, and optional semantic layout priors while keeping detection and correspondence explicitly separated. Evaluated on a public multi-LED dataset, the system provides stable identity-preserving correspondence under noisy conditions. We release code, presets, and evaluation scripts to enable transparent replication, comparison, and dataset annotation.

Vergence is widely used as a proxy for depth perception and spatial attention in immersive and real-world eye-tracking studies. In this paper, we investigate how pupil size artefacts affect vergence estimates during real physical depth viewing with a head-mounted eye tracker. Using a beamsplitter setup with physically near and far targets, we elicited controlled convergent and divergent eye movements under static, luminance-modulated, and blockwise fixation conditions. Near and far targets were reliably separable in vergence angle across participants. However, pupil-vergence coupling varied substantially across individuals and conditions. Static illumination produced large inter-participant variability, while luminance modulation reduced this spread, yielding more clustered estimates. Blockwise and audio-cued recordings further showed that pupil-vergence coupling persists even without visual depth onsets. These results suggest that pupil size fluctuations can systematically influence vergence estimates, and that controlled viewing conditions can reduce—but not eliminate—this effect.

Reproducibility in eye-tracking research is increasingly important as researchers conduct diverse experiments and seek to validate or replicate findings. However, exact replication remains challenging due to differences in laboratory practices and experimental setups. Inconsistent stimulus presentation can yield divergent metrics from identical oculomotor behavior, yet the stimulus layer remains largely unstandardized. Existing tools often require programming expertise or depend on specific hardware vendors. We introduce VIVA Stimuli, a web-based platform for standardized eye-tracking stimulus presentation. It provides configurable task types, including fixation, smooth pursuit, cognitive load, blink, slippage, content display, and questionnaires within a unified environment. The platform supports any eye-tracking technology, including wearable and screen-based VOG trackers, LFI sensors, and EOG devices. ArUco markers enable synchronization for trackers with scene cameras, while a WebSocket architecture ensures temporal synchronization for those without. A visual experiment flow editor allows protocols to be exported and shared, enabling identical stimulus replication across laboratories.

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.

We introduce to VR a novel imperceptible gaze guidance technique from a recent discovery that human gaze can be attracted to a cue that contrasts from the background in its perceptually non-distinctive ocularity, defined as the relative difference between inputs to the two eyes. This cue pops out in the saliency map in the primary visual cortex without being overtly visible. We tested this method in an odd-one-out visual search task using eye tracking with 31 participants in VR. When the target was rendered as an ocularity singleton, participants' gaze was drawn to the target faster. Conversely, when a background object served as the ocularity singleton, it distracted gaze from the target. Since ocularity is nearly imperceptible, our method maintains user immersion while guiding attention without noticeable scene alterations and can render object's depth in 3D scenes, creating new possibilities for immersive user experience across diverse VR applications.

Image classification models are becoming a popular method of analysis for scanpath classification. To implement these models, gaze data must first be reconfigured into a 2D image. However, this step gets relatively little attention in the literature as focus is mostly placed on model configuration. As standard model architectures have become more accessible to the wider eye-tracking community, we highlight the importance of carefully choosing feature representations within scanpath images as they may heavily affect classification accuracy. To illustrate this point, we create thirteen sets of scanpath designs incorporating different eye-tracking feature representations from data recorded during a task-based viewing experiment. We evaluate each scanpath design by passing the sets of images through a standard pre-trained deep learning model as well as a SVM image classifier. Results from our primary experiment show an average accuracy improvement of 25 percentage points between the best-performing set and one baseline set.