Journal of Eye Movement Research最新文献

Eye tracking has become a central method in human-computer interaction (HCI), supported by advances in sensing technologies and AI-based gaze analysis. Despite this rapid growth, a comprehensive and up-to-date overview of eye-tracking research across the broader HCI landscape remains lacking. This study combines records from Web of Science (WoS) and Scopus to analyse 1033 publications on eye tracking in HCI published between 2020 and 2025. After merging and deduplicating the datasets, we conducted bibliometric network analyses (keyword co-occurrence, co-citation, co-authorship, and source mapping) using VOSviewer and performed a qualitative content analysis of the 50 most-cited papers. The literature is dominated by journal articles and conference papers produced by small- to medium-sized research teams (mean: 3.9 authors per paper; h-index: 29). Keyword and overlay visualisations reveal four principal research axes: deep-learning-based gaze estimation; XR-related interaction paradigms within HCI; cognitive load and human factors; and usability- and accessibility-oriented interface design. The most-cited studies focus on gaze interaction in immersive environments, deep learning for gaze estimation, multimodal interaction, and physiological approaches to assessing cognitive load. Overall, the findings indicate that eye tracking in HCI is evolving from a measurement-oriented technique into a core enabling technology that supports interaction design, cognitive assessment, accessibility, and ethical considerations such as privacy. This review identifies research gaps and outlines future directions for benchmarking practices, real-world deployments, and privacy-preserving gaze analytics in HCI.

In complex urban traffic environments, the design of multimodal prompts in augmented reality head-up displays (AR-HUDs) plays a critical role in driving safety and operational efficiency. Despite growing interest in audiovisual navigation assistance, empirical evidence remains limited regarding when prompts should be delivered and whether visual and auditory information should remain temporally aligned. To address this gap, this study aims to examine how audiovisual prompt timing and prompt mode influence driving behavior in AR-HUD navigation systems at complex F-type-5 m intersections through a within-subject experimental design. A 2 (prompt mode: synchronized vs. asynchronous) × 3 (prompt timing: -1000 m, -600 m, -400 m) design was employed to assess driver response time, situational awareness, and eye-movement measures, including average fixation duration and fixation count. The results showed clear main effects of both prompt mode and prompt timing. Compared with asynchronous prompts, synchronized prompts consistently resulted in shorter response times, reduced visual demand, and higher situational awareness. Driving performance also improved as prompt timing shifted closer to the intersection, from -1000 m to -400 m. But no significant interaction effects were found, suggesting that prompt mode and prompt timing can be treated as relatively independent design factors. In addition, among the six experimental conditions, the -400 m synchronized condition yielded the most favorable overall performance, whereas the -1000 m asynchronous condition performed worst. These findings indicate that in time-critical and low-tolerance scenarios, such as F-type-5 m intersections, near-distance synchronized multimodal prompts should be prioritized. This study provides empirical support for optimizing prompt timing and cross-modal temporal alignment in AR-HUD systems and offers actionable implications for interface and timing design.

Over the past decades, eye movements and blinks have been integrated into Concealed Information Test (CIT) paradigms as indicators of deception. Recent findings suggested that fixation patterns in CITs depend on stimulus layout, particularly the distinction between sequential and simultaneous stimulus presentation. In addition, the impact of social presence on deceptive eye movements, critical for application of the CIT in real-world social settings, remains insufficiently examined. The present study addresses these issues through two experiments. In both, participants selected a card and had to reveal, conceal, or fake its value while all possible cards were displayed in pairs. Experiment 1 examined whether deceptive intentions could be differentiated using fixations and blinks, and extended previous findings on the effect of stimulus layout. Experiment 2 assessed the stability of deception-related eye movements and blinks across various levels of social presence (without, per video, being observed by a real person). Our findings replicate effects previously observed with simultaneous stimulus presentation of more cards, demonstrating how stimulus layout modulates deception-related eye movement patterns in CITs. The levels of social presence realised in this study did not significantly alter these patterns, indicating that deception-related eye movements and blinks in CITs remain stable under passive social presence.

Strabismus affects 2-4% of the global population, with horizontal cases accounting for more than 90%. Automated screening using monocular gaze estimation technology shows promise for early detection. However, existing models assume normal binocular vision, and their applicability to strabismus remains unvalidated due to the lack of evaluation platforms capable of reproducing disconjugate eye movements with known ground-truth angles. To address this gap, we developed an open-source, low-cost (approximately 200 USD) horizontal strabismus simulator. The simulator features two independently controllable artificial eyeballs mounted on a two-axis gimbal mechanism with servo motors and gyro sensors for real-time angle measurement. Mechanical accuracy achieved a mean absolute error of less than 0.1° across all axes, well below the clinical detection threshold of 1 prism diopter (≈0.57°). An evaluation of three representative AI models (Single Eye, GazeNet, and EyeNet) revealed estimation errors of 6.44-8.75°, substantially exceeding the clinical target of 2.8°. At this error level, small-angle strabismus (<15 prism diopters) would likely be missed, underscoring the need for strabismus-specific model development. Moreover, rapid accuracy degradation was observed beyond ±15° gaze angles. This platform establishes baseline performance metrics and provides a foundation for advancing gaze estimation technology for strabismus screening.

White noise has been proposed to enhance cognitive performance in children with ADHD, but findings are inconsistent, and benefits vary across tasks and individuals. Such variability suggests that diagnostic comparisons may overlook meaningful developmental differences. This exploratory study examined whether developmental characteristics and subjective evaluations of auditory and visual white noise predicted performance changes in two eye-movement tasks: Prolonged Fixation (PF) and Memory-Guided Saccades (MGS). Children with varying degrees of ADHD symptoms completed both tasks under noise and no-noise conditions, and noise benefit scores were calculated as the performance difference between conditions. Overall, white-noise effects were small and dependent on noise modality and task. In the PF task, large parent-rated perceptual difficulties and high visual noise discomfort were associated with improved performance under noise. In the MGS task, poor motor skills predicted visual noise benefit, whereas large visual noise discomfort predicted reduced noise benefit. These findings suggest that beneficial effects of white noise are influenced by developmental characteristics and subjective perception in task-dependent ways. The results highlight the need for individualized, transdiagnostic approaches in future noise research and challenge the notion of white noise as categorically beneficial for ADHD.

Despite the increasing adoption of desktop virtual reality (VR) in higher education, the specific instructional efficacy of 3D interactive prompts remains inadequately understood. This study examines how such prompts-specifically dynamic spatial annotations and 3D animated demonstrations-influence learning outcomes within a desktop virtual learning environment (DVLE). Employing a quasi-experimental design integrated with eye-tracking and multimodal learning analytics, university students were assigned to either an experimental group (DVLE with 3D prompts) or a control group (basic DVLE) while completing physics tasks. Data collection encompassed eye-tracking metrics (fixation heatmaps, pupil diameter and dwell time), post-test performance (assessing knowledge comprehension and spatial problem-solving), and cognitive load ratings. Results indicated that the experimental group achieved significantly superior learning outcomes, particularly in spatial understanding and dynamic reasoning, alongside optimized visual attention patterns-characterized by shorter initial fixation latency and prolonged fixation on key 3D elements-and reduced cognitive load. Eye-tracking metrics were positively correlated with post-test scores, confirming that 3D prompts enhance learning by improving spatial attention guidance. These findings demonstrate that embedding 3D interactive prompts in DVLEs effectively directs visual attention, alleviates cognitive burden, and improves learning efficiency, offering valuable implications for the design of immersive educational settings.

Eye movement classification, particularly the identification of fixations and saccades, plays a vital role in advancing our understanding of neurological functions and cognitive processing. Conventional modalities of data, such as RGB webcams, often face limitations such as motion blur, latency and susceptibility to noise. Neuromorphic Vision Sensors, also known as event cameras (ECs), capture pixel-level changes asynchronously and at a high temporal resolution, making them well suited for detecting the swift transitions inherent to eye movements. However, the resulting data are sparse, which makes them less well suited for use with conventional algorithms. Spiking Neural Networks (SNNs) are gaining attention due to their discrete spatio-temporal spike mechanism ideally suited for sparse data. These networks offer a biologically inspired computational paradigm capable of modeling the temporal dynamics captured by event cameras. This study validates the use of Spiking Neural Networks (SNNs) with event cameras for efficient eye movement classification. We manually annotated the EV-Eye dataset, the largest publicly available event-based eye-tracking benchmark, into sequences of saccades and fixations, and we propose a convolutional SNN architecture operating directly on spike streams. Our model achieves an accuracy of 94% and a precision of 0.92 across annotated data from 10 users. As the first work to apply SNNs to eye movement classification using event data, we benchmark our approach against spiking baselines such as SpikingVGG and SpikingDenseNet, and additionally provide a detailed computational complexity comparison between SNN and ANN counterparts. Our results highlight the efficiency and robustness of SNNs for event-based vision tasks, with over one order of magnitude improvement in computational efficiency, with implications for fast and low-power neurocognitive diagnostic systems.

Fluent word reading is a key literacy skill, yet the full extent of the oculomotor underpinnings in developing readers remains unknown. Rapid automatized naming (RAN) is a useful clinical measure that has been shown to predict word reading fluency. Here we use RAN scores to predict early, mid, and late local stages of word reading as measured by eye tracking in children who are at a critical time in their literacy development. Thirty-three children participated in two RAN tasks (rapid letter naming (RLN) and rapid digit naming (RDN)) and an eye-tracking task, which included sentence-level reading with an embedded target word. The eye-tracking measures of first fixation duration, regression path duration, and total word reading time were used as early, mid, and late local measures, respectively. RLN and RDN significantly predicted only the mid-stage of the reading process (regression path duration). Faster RLN and RDN times were associated with briefer regressions from target words. Preliminary results link behavioral RAN performance to a mid-stage oculomotor variable, indicating that children with slower RAN times may exhibit longer regressions during reading, suggesting possible difficulties with the integration of phonological processing skills.

The purpose of the study was to analyze, characterize, and compare the measurements of saccades that occurred during the positive and negative fusional vergence test (PFV and NFV, respectively) as a function of the disparity vergence demand. Thirty-four participants' PFV and NFV amplitudes were measured in a haploscopic setup, recording eye movements with an Eyelink 1000 Plus (SR Research). The visual stimulus was a column of letters. Break and recovery points were determined objectively offline, and saccades were detected with a velocity-threshold-based method. A total of 13,103 and 14,381 saccades were detected during the measurement of the PFV and NFV ranges, respectively. Saccades followed the main sequence (ρ = 0.97, p < 0.001). The distributions of saccadic amplitudes during PFV and NFV differed significantly (U = 4.28, p < 0.001). The amplitude of saccades that occurred while fusion was maintained (median (IQR) 0.73 (0.92) deg) was significantly smaller than that of saccades during diplopia (2.10 (3.90) deg) (U = -75.63, p < 0.001). The distributions of saccade direction during the measurement of PFV and NFV amplitudes were statistically significantly different (p < 0.01). These findings contribute to a better understanding of how the visual system adjusts saccades in response to different disparity vergence demand during fusional vergence amplitudes evaluation.

Understanding how tacit knowledge embedded in visual materials is accessed and utilized during evaluation tasks remains a key challenge in human-computer interaction and visual expertise research. Although eye-tracking studies have identified systematic differences between experts and novices, findings remain inconsistent, particularly in art-related visual evaluation contexts. This study examines whether tacit aspects of visual evaluation can be inferred from gaze behavior by comparing individuals with and without formal art education. Visual evaluation was assessed using a structured, prompt-based task in which participants inspected artistic images and responded to items targeting specific visual elements. Eye movements were recorded using a screen-based eye-tracking system. Areas of Interest (AOIs) corresponding to correct-answer regions were defined a priori based on expert judgment and item prompts. Both AOI-level metrics (e.g., fixation count, mean, and total visit and gaze durations) and image-level metrics (e.g., fixation count, saccade count, and pupil size) were analyzed using appropriate parametric and non-parametric statistical tests. The results showed that participants with an art-education background produced more fixations within AOIs, exhibited longer mean and total AOI visit and gaze durations, and demonstrated lower saccade counts than participants without art education. These patterns indicate more systematic and goal-directed gaze behavior during visual evaluation, suggesting that formal art education may shape tacit visual evaluation strategies. The findings also highlight the potential of eye tracking as a methodological tool for studying expertise-related differences in visual evaluation processes.