Search Results (585)

Head-mounted eye-tracking systems play a critical role in virtual reality, human–computer interaction, and clinical applications, yet achieving both high angular accuracy and precise 3D gaze position estimation with low-cost hardware remains challenging. This paper proposes a lightweight, training-free geometric 3D gaze tracking framework for binocular 3D gaze tracking using consumer-grade hardware, which leverages stereo geometric triangulation and a simplified physiological eye model to achieve robust 3D gaze estimation, requiring only standard infrared cameras and dichroic mirrors without additional specialized hardware. The method was evaluated in controlled indoor conditions with 30 participants, where it achieved an angular error ranging from 1.1° to 2.82° and a 3D gaze position error below 13.24 mm. Compared to two state-of-the-art academic non-deep-learning methods, the proposed framework delivers competitive angular accuracy while significantly reducing 3D position error, outperforming the baselines by 34% to 56% in depth estimation precision. These results demonstrates that the proposed geometric framework is a practical and effective solution for high-precision 3D gaze tracking on low-cost hardware, suitable for both research and consumer applications. Full article

Understanding how interface layout influences visual search performance is important for optimizing virtual reality (VR) interfaces. This study investigated visual search efficiency and gaze behavior in simulated VR menu interfaces using a screen-based eye-tracking experiment. To enable controlled measurement of gaze behavior and isolate layout-driven perceptual effects, the interfaces were evaluated using a desktop-based VR simulation. The experiment examined two independent variables: menu layout structure and target spatial quadrant. Two representative VR menu layout structures were compared: a grid-based layout arranged as a 4 × 4 matrix and a gallery-based layout consisting of four large and twelve small cards, forming a size-based visual hierarchy. Target locations were distributed across four spatial quadrants: lower-left, lower-right, upper-left, and upper-right. Participants (N = 39) completed visual search tasks while accuracy (ACC), reaction time (RT), and eye-tracking metrics, including total visit duration (TVD) and total fixation count (TFC), were recorded. The results showed that the gallery-based layout supported more efficient visual search than the grid-based layout, as reflected in shorter RTs, reduced overall TVD, and lower overall TFC. Behavioral and eye-tracking analyses also revealed systematic spatial asymmetries, with the upper-right quadrant showing the fastest responses and reduced gaze-based search effort. Importantly, the advantage of the gallery-based layout was most consistent in the upper-right quadrant, indicating that layout structure and target spatial quadrant jointly shaped visual search efficiency. Gaze-distribution heatmaps provided qualitative visual support for these patterns. These findings provide early-stage perceptual evidence for optimizing layout hierarchy in simulated VR menu interfaces and demonstrate the value of screen-based eye-tracking sensors as quantitative tools for evaluating attentional allocation before further validation in immersive HMD-based VR environments. Full article

This study aims to provide insight into consumer purchasing decisions by integrating eye-tracking data with forecasting techniques. First, the study investigates how consumption motives (hedonic vs. utilitarian) and purchasing purposes (for oneself vs. for others) influence visual attention and decision-making processes. An experimental design was conducted with 128 participants in a simulated online shopping environment, where eye-tracking data were collected based on fixation counts and durations across defined Areas of Interest (AOIs). Second, a total of 20 input features were collected, comprising fixation counts and fixation durations for 10 review-related Areas of Interest (AOIs), and these features were evaluated across the experimental scenarios, while the binary output variable represented the participant’s purchase decision. These biometric features, together with scenario information, were used to forecast purchasing decisions using six machine-learning methods, including Artificial Neural Networks, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naive Bayes, and Logistic Regression. The results indicate that consumers’ visual attention aligns with their consumption motives and purchasing purposes, revealing distinct gaze patterns across different scenarios. In the forecasting phase, the accuracy of different methods for predicting purchasing decisions using review-related eye-tracking data is evaluated. Support Vector Machines achieved the highest overall accuracy, approximately 59–60% across the evaluated datasets, compared with a validation-specific majority-class baseline of 53.85%. This corresponds to a modest improvement of approximately 5.15–6.15 percentage points over the naive benchmark. Overall, the findings suggest that objectively recorded review-related eye-tracking data can be operationalized as behavioral input features in a machine-learning-based purchase-decision classification framework, highlighting the methodological value of integrating eye-tracking insights with consumer behavior forecasting. Full article

Modern flight decks incorporate advanced automation and real-time data systems to improve safety and efficiency. However, maintaining situational awareness under a high workload remains a critical challenge, particularly when attentional resources are stretched. This study investigates how adaptive interfaces, using real-time eye-tracking data, can help pilots maintain their concentration and stay aware of all indications on large-area displays. To this end, we developed a gaze-responsive display that monitors whether the pilot has visually focused on objects displayed on a moving map. When the system detects that the pilot has not noticed an object on the map, it automatically adjusts the interface, highlighting the missed object, thus helping to prevent critical information from being overlooked. The interface was evaluated in a controlled simulation study with 34 participants. By capturing pilots’ gaze behaviour, the system reveals how attention shifts under workload and how adaptive visual cues can complement natural scanning patterns. Participant feedback indicated that adaptive behaviour provided supportive guidance without imposing additional cognitive load. Overall, the study highlights the potential of adaptive gaze-based interfaces to enhance attention strategies and contribute to more resilient situational awareness in aviation. Full article

With the development of human–computer interaction technology, non-contact interaction based on gaze tracking and facial movements has become a research hotspot. Traditional mouse-and-keyboard methods pose challenges for people with disabilities or limited hand movements, while existing gaze-tracking systems often rely on expensive hardware or lack sufficient accuracy. This paper designs and implements a real-time system using ordinary cameras, achieving natural, efficient interaction via multimodal input combination. The system uses an improved MobileNetV2 backbone to construct GazeTrackNet for gaze estimation. It adopts MediaPipe Face Mesh to detect facial landmarks. Meanwhile, it applies geometric feature analysis, including eye aspect ratio and mouth aspect ratio, to identify actions such as blinking and mouth opening. It adopts a hybrid control strategy that combines gaze jumping and head fine-tuning, using mouth state as the main control switch. Key contributions include a lightweight gaze-tracking algorithm that enables stable and efficient gaze detection on consumer-grade hardware, a multimodal interaction strategy based on facial movement that improves system stability and ease of use, and a complete prototype system that achieves real-time performance on standard laptops. Experimental results show an average gaze average angle error of 3.0°, 97% eye state recognition accuracy, and end-to-end latency below 70 ms. The system can satisfy the requirements of daily desktop interaction under normal indoor lighting, and shows potential for future barrier-free interaction applications after further validation with target users. Existing gaze-tracking methods either suffer from low precision on lightweight devices or bring heavy computational overhead. Common facial recognition approaches also face frequent false trigger interference. Compared with them, our scheme achieves balanced accuracy and real-time performance via an attention-enhanced structure, and the designed dual anti-shake mechanism effectively suppresses misjudgment, delivering a more stable hands-free interaction experience. Full article

Virtual avatars have shown potential as supports in Autism Spectrum Disorder (ASD) interventions, but many existing systems provide largely standardized interactions that do not account for individual variability. This study presents an exploratory evaluation of a virtual puppet system that enables real-time interaction by synchronously transmitting a human model’s movements, facial gestures, and voice to a digital avatar. The system was personalized using each participant’s restricted interests (RIs), identified through a clinical triangulation process involving therapist input, caregiver reports, and observation. After an initial technical validation with 16 neurotypical children, the system was evaluated in a proof-of-concept sample of 11 children with ASD (7 in an experimental group exposed to RI-based personalization and 4 in a control group interacting with a standard interface). Data sources included eye tracking and therapist-completed observational questionnaires. Across sessions, descriptive patterns in gaze fixation and therapist reports suggested that RI-based personalization may help sustain attention to the screen and support engagement with the therapeutic environment relative to non-personalized interaction. Heatmap patterns further indicated that children under the personalized condition visually explored RI-related elements within the scene. This study provides evidence of technical and procedural feasibility and generates hypotheses for future research. Full article

Eye-tracking has been utilized to characterize visual behavior in medical image visualization and interpretation, yet neurosurgeons remain underrepresented. Characterizing neurosurgery-specific visual expertise is important for understanding expert search strategies, informing training, and developing computational models. This study examined gaze behavior in naïve observers (N_p = 29), neurosurgery registrars (N_p = 16), and consultant neurosurgeons (N_p = 24), viewing normal (N_p = 20) and pathological (N_p = 19) brain MR images under a free-viewing paradigm. To capture expertise-related characteristics, we analyzed two features at each fixation location: (i) fixation duration, reflecting temporal allocation of visual attention, and (ii) three-dimensional fractal dimension (3DFD) around each fixation location, quantifying local structural complexity. To assess pathological-type effects, we grouped similar pathologies into five stimulus groups. Linear mixed-effects modelling revealed systematic expertise-related differences, with experts exhibiting longer fixation durations in pathological stimulus groups and pathology-type-dependent complexity sampling. Combined fixation duration and 3DFD features captured complementary aspects of visual expertise, improving Random Forest classifier’s accuracy (>93%) compared to individual features, for all five stimulus groups. These findings highlight neurosurgery-specific markers of visual expertise and demonstrate that combining behavioral and image-derived features could underpin computational models and training tools that emulate expert-level strategies in neurosurgical image interpretation. Future work should evaluate its applicability to other medical domains. Full article

Age-related changes in visual perception alter attentional deployment, yet computational models of visual attention have been validated almost exclusively on younger populations. This limits both the theoretical investigation of age-specific mechanisms and practical applications in age-inclusive design, where researchers depend on specialised eye-tracking equipment to observe such differences. Therefore, we present the Elderly Visual Attention Estimation (EVAE) model, a computational framework that predicts early visual attentional orienting in older adults by combining stimulus-driven image features with age-specific top-down priors. The framework models six dimensions of elderly visual attention from cross-age eye-tracking data: colour brightness sensitivity, centre bias, foreground–background differentiation, depth detection, early attentional prior, and sustained-attention spatial prior. On public datasets, EVAE achieves an AUC-Judd of 0.92, which outperforms existing saliency models and deep learning approaches such as DeepGaze II. The framework is optimised for an input resolution of 128 × 96 pixels, producing fixation probability maps that are upsampled to match the original stimulus resolution for practical interface evaluation. Cross-age validation confirms the model’s specificity, as EVAE predicts attentional behaviour in older adults but does not generalise to younger adults. An ablation study shows that image features and top-down spatial priors each contribute independently to prediction accuracy, and that bottom-up saliency alone cannot account for age-related attentional patterns. Centre bias and early attentional prior are the strongest predictors, indicating that visual ageing involves greater reliance on spatial strategies and compensatory processing. As an alternative to hardware-based eye-tracking, EVAE widens the scope of empirical research into older adults’ visual attention and informs the design of accessible digital interfaces. Full article

This study examined visual exploration strategies in phishing-email detection by integrating conventional AOI-based eye-tracking measures with a complementary scene-based indicator, the Nearest Neighbor Index (NNI), to capture the global spatial organization of fixations. Thirty-two volunteers completed an email-classification task involving 106 static email stimuli; data from 30 participants were included in the final analyses. For each stimulus, participants judged whether the email was authentic or phishing, allowing for the computation of eye-tracking metrics across Signal Detection Theory classification outcomes. Concerning the NNI, the results showed that the spatial distribution of fixations was higher for suspicious than for non-suspicious emails, indicating a broader visual exploration pattern under higher task demands. More importantly, correct and incorrect responses differed reliably: hits were associated with more dispersed and regular fixation patterns, whereas false alarms were associated with more clustered scanning; misses showed a descriptively similar tendency that did not survive correction for multiple comparisons. Participants also responded faster when correct than when incorrect. When cybersecurity awareness (CAIN) was included as a mean-centered covariate, the primary effects of Signal and Outcome on NNI and decision time remained significant, indicating that the experimental effects are robust to individual differences in cybersecurity knowledge. However, CAIN did not emerge as a reliable predictor of eye-tracking measures within these models, suggesting that its role operates more at the level of classification performance than moment-by-moment gaze organization. Full article

This study investigates how different rural landscape types shape visual attention and physiological responses, with the aim of informing more targeted rural landscape renewal. Four typical rural landscape types in the suburbs of Hefei, China, were examined: Flat Farmland (FF), Hilly Forest (HF), Developed Plain (DP), and Water-network Lowland (WNL). All four study villages are project villages in the suburban area of Hefei where rural revitalization is currently being advanced. This study therefore treats them as empirical cases within the context of rural revitalization in China, using them to examine perceptual differences among rural landscape types and their implications for rural landscape renewal. A two-stage research design was adopted to balance field realism and laboratory control. In the first stage, 40 representative scene images were selected by combining field video records with fluctuations in on-site skin conductance response (SCR). In the second stage, laboratory experiments were conducted while participants viewed the selected images, during which eye-tracking, skin conductance, and heart rate data were recorded simultaneously. These measures were used to characterize visual attention allocation and autonomic physiological responses across different rural landscape types, rather than to directly measure landscape preference. For Area of Interest (AOI) analysis, each image was coded into six landscape element categories: vegetation, buildings, roads, sky, vernacular buildings, and water bodies. The results revealed significant typological differences in overall visual search patterns and autonomic responses. Gaze hotspots were concentrated on identifiable targets and boundary regions in the foreground and midground, whereas the sky attracted relatively limited attention. FF primarily emphasized vernacular buildings and farmland boundaries, HF emphasized settlement interfaces and spatial transition nodes, DP emphasized road junctions and facilities along routes, and WNL emphasized water bodies and water–land interface zones. These findings suggest that a two-stage multimodal design can provide supporting evidence for understanding type-specific perceptual responses and can support more targeted strategies for rural landscape renewal. Full article

Background: Proficient second language (L2) reading relies on complex neurocognitive processes. Neuroimaging studies have identified key brain regions recruited during L2 reading, including the left inferior parietal lobule (LIPL) and the calcarine cortex (CAL). The LIPL has been suggested to be involved in phonological decoding during L2 reading, whereas the CAL has been implicated in early-stage visual processing. However, given the correlational nature of neuroimaging techniques, it remains unclear whether these regions play causal roles in L2 reading or are merely epiphenomenal. Methods: To address this issue, the present study used transcranial magnetic stimulation (TMS) to modulate neural activity in these regions and eye-tracking technology to assess subsequent reading performance in Chinese–English bilinguals. Specifically, ninety-seven participants were randomly assigned to one of three offline TMS conditions: LIPL, CAL or vertex (as a control site) stimulation, after which they performed a natural sentence reading task in English. Results: The results showed that, compared to the control condition, TMS over the LIPL significantly reduced first fixation duration, whereas no significant effects emerged on gaze duration, regression path reading time, or total reading time. TMS over the CAL produced no significant effects on any eye-movement measures. Conclusions: These findings suggest that the LIPL plays a causal role in L2 reading for early-stage lexical processing through phonological decoding. Overall, this study is the first to employ TMS and eye-tracking to investigate the neural mechanisms underlying natural L2 reading. Full article

Background. Facial expression recognition depends on how visual information is sampled across the face over time. Static area-of-interest (AOI) measures describe where observers look but provide limited information about the sequential organization of gaze. This study examined how gaze is organized during facial expression recognition and whether this organization remains comparable across two conditions differing in the temporal order of contextual and facial stimuli. Methods. Eye-tracking data were collected from 27 participants performing a facial expression recognition task. Fixations on faces were mapped onto three AOIs: Upper Facial Zone (UFZ), Central Facial Zone (CFZ), and Lower Facial Zone (LFZ). Gaze organization was examined using first- and second-order Markov models, entropy estimates, spatial repositioning measures, and a gaze stability index. Results. Gaze transitions showed a structured, non-random organization centered on the CFZ. In the first-order Markov model, transitions from both the UFZ and LFZ were directed primarily toward the CFZ, and within-zone transitions were also most likely in the CFZ. Entropy was lower for the CFZ than for the upper and lower regions, indicating lower transition uncertainty in the central region. The second-order model showed an influence of recent fixation history while preserving the predominance of the CFZ. Spatial repositioning varied across facial zones in both conditions. However, mixed-effects analyses showed no effect of condition on gaze stability. Conclusions. Facial expression recognition was associated with a pattern of exploration in which the central facial region emerged as the most likely fixation destination, with limited evidence of condition-related differences in gaze organization. Full article

Evaluations of medical artificial intelligence (AI) explanations often rely on self-reported trust, perceived usefulness, acceptance, or final decision outcomes, while less directly characterizing whether users check evidence around AI outputs during decision making. In AI-assisted chest radiograph interpretation, a critical process-level question is whether users return from the AI output to the original image evidence when further scrutiny is needed. To address this question, we examined whether verifiable explanations—explanations designed to make AI recommendations checkable against the original image evidence—are associated with process markers of visual evidence checking in AI-assisted chest radiograph interpretation using eye-tracking and human-factors process measures. A 2 × 2 between-subjects experiment manipulated verifiable explanations (present vs. absent) and risk context (high vs. low), with AI recommendation correctness embedded at the trial level. Fifty-six clinically trained participants each completed 24 interpretation trials. Analyses focused primarily on gaze transitions between the AI output and the original image and dwell time on the original image, with response time and exploratory verification-related behavioral states used as auxiliary process measures. Verifiable explanations did not simply increase acceptance of AI recommendations. Instead, when AI recommendations were incorrect, they were most clearly associated with more frequent AI–image transitions and longer absolute dwell time on the original image evidence. Exploratory state-based analyses further suggested a lower tendency toward no-verify adopt under incorrect AI recommendations, but these findings were treated as complementary rather than primary evidence. Overall, the value of verifiable explanations lies not only in final decisions but in whether they make AI recommendations more inspectable against the original evidence. These findings provide eye-tracking evidence consistent with visual evidence checking in AI-assisted diagnostic interfaces and underscore the value of process-sensitive human-factors measures in medical AI evaluation. Full article

Novice drivers demonstrate inefficient visual scanning and elevated crash risk relative to experienced drivers. Different training programmes may influence gaze behaviour and performance in distinct ways. This study compared the impact of cognitive, simulator-based, and real-world training on visual attention and driving-related outcomes in novice drivers. Thirty novice drivers (18–27 years; ≤1 year driving experience) were randomized into three training groups (n = 10 each): cognitive training (PsyToolkit, Version 3.7.0), game-based simulator training, and supervised real-world driving. Baseline and post-training assessments included visuomotor performance (Fitts’ Law), attentional cueing (valid/invalid reaction time), simulator-based driving errors, and eye-tracking measures of gaze behaviour. Eye-tracking outcomes included dwell-time percentage and first-fixation order across predefined areas of interest (AOIs). Participants completed 10 consecutive days of modality-specific training. Cognitive training improved visuomotor performance and increased forward road monitoring. Game-based simulator training yielded the largest reductions in simulator driving errors, particularly lane deviations (Z = −2.89, p = 0.004). Real-world driving altered visual scanning patterns, with significant differences in rear-view mirror prioritization (p = 0.024). Across groups, gaze shifted from dashboard view toward safety-relevant AOIs. Training modifies novice drivers’ gaze behaviour in modality-specific ways, suggesting that a multimodal training approach may enhance visual attention and driving safety Full article

Eye tracking (ET) provides process-level indices of how students sample task-relevant information during core academic activities. In school-age learners (6–18 years) with specific learning disorders (SLDs; dyslexia, dysgraphia, and dyscalculia), ET can complement behavioural assessment by quantifying oculomotor patterns linked to decoding, model–production coordination, and stepwise strategy execution. This narrative review synthesises ET findings in SLD across reading, handwriting/copying, and arithmetic and translates them into an applied framework for school-oriented use. We summarise key metrics and Areas of Interest (AOI)-based analyses, highlight technical and data-quality requirements for valid acquisition in educational settings, and outline compact functional assessment protocols integrated with standard academic and neuropsychological measures. Building on these foundations, we propose six hypothesis-driven gaze-contingent paradigms (H1–H6) as preliminary models for future experimental testing rather than as established interventions, and we map each to its current level of empirical support, specifying primary gaze outcomes and curriculum-relevant behavioural endpoints. We emphasise that eye-movement findings in specific learning disorders are heterogeneous and may vary as a function of age, task demands, and comorbidity. Accordingly, credible training effects require retention and transfer probes under standard, non-contingent display conditions, appropriate controls, and explicit developmental interpretation. Eye tracking is positioned as complementary functional evidence and as a platform for experimentally testable, mechanism-based interventions in school-age specific learning disorders. Full article