Aug 2026· Advanced Electromagnetics· 0 citations· 10 references
Abstract
To overcome the limitations of conventional design assessment in capturing instant cognition and visual dynamics, this paper proposes a multimodal evaluation method for visual design effectiveness based on eye-tracking technology. By integrating dynamic region-of-interest sequence analysis with retrospective cognitive attribution, the method captures both the spatial flow and temporal dynamics of visual attention. A contextual task is designed to simulate realistic viewing behavior while maintaining experimental control, guiding participants through goal-oriented browsing under standardized presentation conditions. Dynamic region-of-interest sequence analysis is then used to measure gaze retracing rate and evaluate the clarity of information hierarchy. Retrospective interviews and visual entropy analysis are combined to link objective eye-movement trajectories with subjective semantic interpretation. Experimental results show that the method effectively differentiates design effectiveness under different contrast ratios. The high-contrast group has the shortest initial fixation time (180 ± 38 ms) and the lowest gaze retracing rate (26.1 ± 5.9%). Gaze retracing rate is negatively correlated with semantic comprehension accuracy (r = -0.81), supporting continuous evaluation from perception to cognition.
The ability to comprehend and interpret visualizations has garnered increasing attention in recent years. Individuals with specialized knowledge and experience in a particular field exhibit distinct patterns in how they perceive, process, and retain information related to that area. Extensive research across various domains has demonstrated that expertise confers perceptual and cognitive benefits in engaging with domain-relevant information. Eye-tracking methodology has provided significant insight into some of the perceptual mechanisms underlying performance differences between experts and novices in various professional settings, including aviation, car driving, medicine, arts, cartographic research and sports. The opportunity to access tacit expert knowledge, to improve efficiency of task performance and potentially to enhance safety measures has gained growing attention over the past years. This article conducts a systematic literature review to consolidate literature describing the application of gaze behavior as an adjunct tool to traditional teaching methods. Building on the proposed conceptual framework, we systematically reviewed the training potential of eye movements. Our results reveal that gaze-trained individuals exhibit a performance advantage over traditionally trained counterparts and that the impact and feasibility of expert skills transfer through gaze behaviors are moderated by different factors. We summarize the benefits of using expert gaze as a visual cue (e.g. EMMEs) in terms of attention-modeling processes, human performance and safety in work environment.
Elena Lupia, A. Bortolotti, Riccardo Palumbo· Trends in Psychology· 0 citations
Level 3 automated driving necessitates rapid driver reengagement following a takeover request, making human–machine interface design critical for safety. As a sensor-based in-vehicle interface, the augmented reality head-up display (AR-HUD) integrates real-time environmental sensing with visual information delivery, yet how different information presentation strategies influence driver visual attention during takeover remains underexplored. We examined the effects of presentation form (static/dynamic) and presentation timing (concurrent/progressive) using a 2 × 2 within-subject design. In the experiment, twenty-one licensed drivers viewed prerecorded automated driving takeover scenarios. Eye tracking measured mean fixation duration, mean saccade amplitude, time to first fixation, and fixation count, while ratings assessed usability, acceptance, and intention comprehension. Progressive presentation significantly reduced all eye-tracking measures and improved perceived usability ratings compared with concurrent presentation. This pattern indicates more concentrated and orderly gaze allocation and is consistent with lower visual-search and information-integration demands. However, the lower fixation count may partly reflect reduced information exposure. Static presentation reduced mean fixation duration but showed no consistent overall advantage on the remaining measures. Significant form-by-timing interactions showed that progressive presentation reduced saccade amplitude and time to first fixation and improved intention comprehension under static, but not dynamic, presentation. Among the four combinations, static-progressive presentation yielded the most favorable overall pattern. Overall, presentation timing affected more measured outcomes than presentation form. These findings provide preliminary evidence that presentation form and presentation timing shape gaze behavior and subjective evaluations of AR-HUD takeover displays.
Kexin Chen, Jun-Feng Li, Mo Chen· Journal of Eye Movement Rese...· 0 citations
A publicly available, goal-driven, interactive guide called Generalized Eye Tracking Metrics (GEMs), which supports the learning of metrics, relevant terminology, and analysis approaches as it relates to the user’s research questions and experimental setup.
Lauren E. Horde, Shannon P. D. McGarry, Ciara M. Sibley et al.· Proceedings of the Human Fac...· 0 citations
Video-based eye-tracking technology has demonstrated promising potential for quantitatively assessing the mental states of maritime professionals, particularly perceptual performance related to situational awareness—a critical competency for operators of both conventional and crewless ships. Quantitative evaluation of perception can support the enhancement of training curricula for conventional operators and accelerate the development of effective training frameworks for remote operations of unmanned ships. One approach to evaluating perceptual performance is gaze similarity analysis; however, existing quantitative methods for operation-based experimental settings incorporate subjective elements, which may limit analytical accuracy and reproducibility. To address this limitation, this study proposes a conceptual, quantitative gaze similarity analysis method that does not rely on subjective judgment. The method is demonstrated through an experiment involving simulated remote ship operations. In the experiment, a participant wearing a mobile eye-tracker monitored two navigational scenarios: southbound and northbound passages through the Uraga Channel traffic route. Fixation data obtained from both scenarios were analyzed using the proposed method. The results indicate that gaze similarity can be quantitatively assessed in operation-based experimental settings using data derived directly from operational tasks, thereby providing an alternative framework that complements existing approaches for evaluating perceptual performance among maritime professionals.
Khairul Izzati bin Kamarumtham, Koji Murai· Georgian Maritime Scientific...· 0 citations
This work proposes two complementary eye tracking methodologies focused on understanding information access. The first study examines the earliest stage of search, the formation of an information need, and the formulation of a query. The second study presents participants with a pre-typed query on a Search Engine Results Page (SERP) containing GenAI content positioned above the traditional ten blue links. Across both studies, the planned analysis examines how user interactions differ from patterns reported in prior literature, and outlines future research directions aimed at deepening our understanding of search behavior in the era of GenAI. By linking visual attention to query behavior across traditional and GenAI-enhanced interfaces, this research contributes toward more intuitive and adaptive search systems that better support evolving information-seeking behavior. The design space for search engine interfaces is vast, but interaction techniques and user interfaces for information access with LLMs remain under-researched and poorly understood. Between 2020 and 2024, 750 preprints related to LLMs were published on arXiv in the field of Information Retrieval, with only 22 mentioning "user interface" in their abstracts [3]. This research will contribute to filling this gap by understanding user behavior, preferences, and cognitive effort in human-AI interactions. Specifically, the goal of this research is to explore how users seek information in the era of GenAI via a series of user lab studies. These studies will be conducted to compare traditional search engines with GenAI-based systems. Based on these lab studies, the aim is to develop new methods that better fulfill users' information needs. Key research questions for this research include: •[RQ1:] How do eye fixations on specific words read before a search session impact query formulations?•[RQ2:] How do eye fixations on specific words read before a search session, together with curiosity about a topic, influence query variations? •[RQ3:] How do individuals read information prior to formulating queries?•[RQ4:] How does user interaction with new search engine interfaces differ from the patterns of scanning search engine interfaces described in the literature? •[RQ5:] How to evaluate search behaviors in the context of information seeking?•[RQ6:] What are the fundamental factors Information Retrieval researchers should investigate when conducting user studies in the era of Generative AI? The work I have described in this research is done under a reasoned approach, but may not account for all user variables such as domain expertise, use of different devices [1], other demographics, anomalous state of knowledge, and cognitive biases [2]. Researchers have attempted to overcome the complexity of understanding human search behavior by creating personas and simulating user behavior. However, this raises concerns that simulated users may not accurately reflect real human behaviors, and assessing the validity of simulated users still remains a challenge [1]. Therefore, conducting both types of studies separately and in combination is important, as each approach can provide valuable insights and collectively contribute to the body of knowledge to understand and improve human-GenAI interactions. The challenge we expect to face when conducting this research is frequent updates to search interfaces. How can we standardize our studies to account for these changes? How do we analyze user behavior when interfaces keep evolving? Should we test multiple interface versions, present our findings, and then provide recommendations? An additional aspect is determining how participants interact with different search tools based on different types of tasks. Do they rely on traditional search engines, generative AI systems, or a combination of both?
Sara Allawati· Annual International ACM SIG...· 0 citations
This study addresses a limitation of the assumption in Kansei Engineering (KE) that morphological features contribute equally to affective evaluation by developing a KE framework based on the Feature Integration Theory (FIT). Specifically, eye-tracking data based on Areas of Interest (AOI) are used to calculate the coefficient of variation (CV) of fixation duration between stimuli, which is then applied to rescale morphological features before modeling. In practice, ten Ming-style chairs were encoded with 30 morphological features, and Kansei ratings were collected from 389 participants, along with eye-tracking data from 30 participants. Accordingly, six Partial Least Squares (PLS) models constructed based on the rescaled features demonstrated robust performance (Q2 = 0.544–0.885), supported by LOOCV, bootstrap, and permutation tests, with external validation further confirming their generalizability. Notably, the results revealed a dissociation between fixation distribution and dispersion-based scaling, indicating that stable gaze concentration does not translate into higher predictive contributions in Kansei responses. This framework provides a repeatable method for incorporating visual attention variability into feature-based KE for product evaluation.
Ting Gao, Irwan Syah Mohd Yusoff, Rosalam Che Me· Applied Sciences· 0 citations
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