Peter Kiefer

In a mobile world where many aspects of life are enabled by advanced geospatial technologies, reliance on mobile maps in daily decision-making situations and for various spatial behaviors, such as ...

Pupil size is a key eye-based indicator of mental processing and internal states. Nonetheless, pupil size is also influenced by light, causing challenges for monitoring internal states with eye tracking in dynamic and naturalistic settings. We investigated how convolution-based modeling can capture the influence of luminance and emotional arousal on pupil size, and conversely, how pupil dynamics inform arousal prediction. In a lab study, we analyzed data collected from 19 participants who watched travel-themed videos with different arousal levels and their pixelated counterparts. We present and evaluate a convolution-based approach for pupil size modeling that integrates low-level visual features and higher-order emotional factors, as previous work has shown effectiveness of such methods in modeling pupil light reflex. Our results show that incorporating luminance and contrast indeed enhances arousal prediction from pupil data, although performance varies due to different user behavior and stimuli.

Recent advancements in deep learning models for object segmentation have significantly enhanced the analysis of mobile eye-tracking data, enabling automatic dataset annotation. However, many of these models rely solely on raw gaze and fixation points to indicate annotation targets on individual video frames, thereby neglecting the valuable temporal contexts provided by gaze features. In this paper, we introduce a pipeline that integrates temporal gaze features into the SAM 2 model to improve the segmentation accuracy of automatic annotations for mobile eye-tracking data. Specifically, we investigate different types of encoding temporal gaze information, used as prompts, to help the model learn gaze patterns at various levels of object granularity and to account for inaccuracies in gaze location introduced by mobile scenarios. Our experiments show that adding temporal context largely benefits the segmentation performance. In addition, our proposed fusion strategy allows us to combine different types of prompts and shows promising results.

This special issue of the Proceedings of the ACM on Human-Computer Interaction includes accepted full papers from the ACM Symposium on Eye Tracking Research and Applications (ETRA). ETRA is the premier eye-tracking conference that brings together researchers from across disciplines to present advances in eye-tracking systems and methods, oculomotor research, eye movement data analysis, gaze-based interaction, and eye-tracking applications. A total of 24 full papers were accepted from 80 submissions after a rigorous reviewing process (30% acceptance rate). Accepted contributions were split into special issues in two journals, depending on the fit of topic and authors' preferences. 16 accepted papers are included in this issue of the Proceedings of the ACM on Human-Computer Interaction. 8 will be published in the Proceedings of the ACM on Computer Graphics and Interactive Techniques. All accepted papers are invited to present at ETRA 2025 (May 26 - May 29, 2025, in Tokyo). We would like to thank all members of the Editorial Board and all external reviewers for their effort and dedication, as well as all authors for their high-quality contributions.