Hippocampal interictal spikes disrupt theta-band hippocampal-frontal eye field connectivity: fixation skewness as an indicator of transient network instability.

Data from psychophysiological measures can offer new insight into control room operators’ behaviour, cognition, and mental workload status. This is particularly helpful when assessing capacity to respond to critical plant conditions such as alarm response scenarios. However, wearable tools such as eye tracking and electroencephalography caps can be perceived as intrusive and unsuitable for daily operations. Therefore, this article examines the potential of using real-time data from process and operator-system interactions during abnormal scenarios, recorded and retrieved from the distributed control system’s historian or process log, to provide insight into operator behaviour and predict their response outcomes without intruding on daily tasks. Data for this study were obtained from a design of experiment using a formaldehyde production plant simulator and four human-in-the-loop support configurations. A comparison between configurations in terms of both behaviour and performance is presented. Then, a step-wise logistic regression and a Bayesian network model were used to predict operator error. The results identified predictive metrics, discussed in terms of their value as precursors of overall system performance in alarm response scenarios. Knowledge of relevant and predictive behavioural metrics accessible in real time can better equip decision-makers to predict outcomes and provide timely support measures for operators.

High-frequency gaze tracking and estimation with event-based camera and lightweight framework for edge interactive devices

Glaucoma patients of similar age and educational level, compared with healthy individuals, exhibit a greater number of saccades and fixations during eye-tracking-aloud reading, resulting in reduced reading speed. To evaluate reading performance in patients with glaucoma compared with controls using an eye tracker and to explore the potential confounding effects of patients' contrast sensitivity and cognition. A cross-sectional study was conducted with 111 participants (57 with glaucoma and 54 controls) with a best-corrected visual acuity of ≥0.5 logMAR. Cognition was assessed using the Montreal Cognitive Assessment, and contrast sensitivity was measured with the Freiburg Visual Acuity and Contrast Test. A reading performance evaluation was conducted using the Minnesota Low Vision Reading Test displayed on slides on a computer screen. Reading speed was calculated in words per minute, and an eye tracker was used to analyze saccade and fixation patterns during the reading task. Mean age was 61.8 (±11.6) and 66.5 (±13.7) in the glaucoma and control groups, respectively ( P =0.05). Best-corrected visual acuity was 0.18 (±0.16) and 0.04 (±0.10) logMAR in the glaucoma and control groups, respectively ( P <0.001). Montreal Cognitive Assessment score was 21.8 (±3.5) in the glaucoma and 21.4 (±4.0) in the control group ( P =0.566). A total of 26% of the glaucoma group and 33% of the control group had at least a primary education. Controls read faster and showed fewer saccades and fixations than patients with glaucoma ( P <0.05) across all 5 slides. Patients with glaucoma exhibit poorer reading performance, as evidenced by eye-tracking data, compared with controls of similar age, cognitive function, and educational level.

Coordination of caregivers' (N = 50, five fathers and 45 mothers) and 9-month-old infants' (15 girls, 34 boys, one not reported) gaze during naturalistic play with a puzzle and activity board was examined using the Actor-Partner Interdependence Model within a Dynamic Structural Equation Modeling framework. This approach allows for the simultaneous estimation of each partner's effect on their own subsequent looking and on their partner's subsequent looking. Analysis of gaze data from head-mounted eye trackers worn by North American, middle-class, racially and ethnically diverse caregivers and infants (21 infants and 25 parents were White and not Hispanic; the remaining identified as Asian, Black, Multiracial, or another race, and/or as Hispanic) revealed that infants' and caregivers' looks at a moment was related both to their own subsequent looks and to their partners' subsequent looks. Moreover, infants' looks to their parents' faces more strongly predicted parents' subsequent looks to their infants' faces than the reverse, suggesting that mutual gaze was primarily driven by infants' attention. In contrast, there was no difference in infants' or parents' influence on their partners' looking at objects, indicating that joint attention emerged through mutual coordination. Furthermore, there was a stronger effect of gaze on subsequent gaze for infants than for parents, suggesting that infants' attention was more sustained and less flexible than that of their caregivers. This work highlights the co-constructed nature of the moment-to-moment coordination of attention between caregivers and infants. SUMMARY: We applied the Actor-Partner Interdependence Model within Dynamic Structural Equation Modeling to capture second-by-second coordination of attention between caregivers and their 9-month-old infants. We replicated in two naturalistic play settings that infants rarely look at parents' faces and look more towards objects than caregivers. We demonstrated that parents and infants influence each other's subsequent in-the-moment attention during episodes of mutual gaze and joint attention. Mutual gaze was primarily infant-driven, whereas object-focused joint attention was reciprocal between partners; infants also showed "stickier" attention than caregivers.

Regulation of eye movements and pupil size in natural scenes.

GiTNet: A graph-based trajectory-informed network for gaze-supervised medical image segmentation.

This study investigated the extent to which the role of lexical tone is contingent upon phonemic segments. Employing the printed-word paradigm, this study presented native Mandarin speakers with a visual display comprising a target word (e.g., "" /chai2huo3/, "firewood"), a same-tone competitor (e.g., "" /cha2dao4/, "tea ceremony"), a different-tone competitor (e.g., "" /cha1kong3/, "socket"), and two distractors. Simultaneously, participants listened to an auditory target word. Critically, the phonological competitors shared partial segmental information with the spoken target words. Participants were then required to select the visual target word in the visual display. Results revealed substantial phonological competition effects in both the same-tone and different-tone conditions, with the former exhibiting a more pronounced effect. These results elucidate that the role of lexical tone partially depends on the availability of segmental information. Even in cases where certain segments are accessible, lexical tone can still influence the activation degree of the lexical candidate based on the tonal similarity, where the more similar candidates are activated to a higher degree than the less similar ones.

Exergames integrate motor, cognitive, and postural-control demands; however, how specific task-demand manipulations influence gaze allocation during exergame performance remains insufficiently characterized. This exploratory pilot study used a within-participant design to examine whether gaze allocation and task performance differed across baseline, concurrent cognitive-task, and unilateral-squat conditions during squat-based exergaming in healthy young adults. Eight healthy adults (mean age 20, SD 1 years; 7 men and 1 woman) used a squat-based exergame (Ring Fit Adventure; Nintendo) under 3 randomized conditions: baseline, concurrent cognitive task, and unilateral squats. In the concurrent cognitive task condition, participants performed serial subtraction during squatting. In the unilateral-squat condition, participants performed single-leg squats, which were intended to increase support-leg muscular demand as well as postural and motor-control requirements. Execution time, squat score, arithmetic performance, and eye-tracking metrics were recorded. Primary gaze outcomes were the proportion of fixation time and fixation counts allocated to predefined areas of interest (AOIs; command, avatar, and score) and to regions outside these areas across the entire trial. Differences from the baseline condition were examined using Dunnett tests or paired Wilcoxon signed-rank tests with Holm adjustment, and effect sizes were reported. Exergame execution time increased from 31.0 (SD 3.3) seconds in the baseline condition to 38.2 (SD 6.1) seconds in the concurrent cognitive task condition and to 34.3 (SD 6.0) seconds in the unilateral-squat condition. In contrast, the squat score decreased from 98.8 (SD 2.8) in the baseline condition to 69.6 (SD 18.3) in the unilateral-squat condition. For fixation counts, allocation to the command AOI decreased from 44.8% (SD 11.4%) in the baseline condition to 32.1% (SD 8.9%) in the concurrent cognitive task condition and 36.2% (SD 8.5%) in the unilateral-squat condition. Outside AOI fixation counts increased in the concurrent cognitive task condition (55.7%, SD 8.6%) relative to the baseline condition (36%, SD 16.9%). In this exploratory within-participant pilot study, adding a concurrent cognitive task and performing unilateral squats resulted in different patterns of performance change and fixation count redistribution during squat-based exergaming. These preliminary findings suggest that AOI-based gaze allocation metrics may help characterize task demand-related attentional shifts in this setting. Larger confirmatory studies with more diverse samples and individual-level validation are needed before these metrics can be considered for adaptive or clinical applications.

Eye-gaze synchrony-the temporal coordination of eye movements between individuals-has been widely studied in dyadic interactions, but its operationalization remains inconsistent and its extension to multi-person settings is methodologically challenging. This proof-of-concept study examines whether eye-gaze synchrony can be captured in a single triadic interaction using mobile eye tracking and proposes a reproducible workflow for doing so. Three participants wore Tobii Pro Glasses 3 during a 7-min spontaneous face-to-face conversation. Independent recordings were synchronized via a router-based setup, and gaze points were re-expressed within a common two-dimensional reference frame anchored by a fiducial marker. To illustrate how different operationalizations can be applied in this context, we compared an event-based approach quantifying discrete occurrences of mutual gaze and joint attention with an index-based approach assessing temporal alignment of gaze signals via Surrogate Synchrony methods. The two approaches yielded different descriptive patterns, suggesting that they may emphasize different features of gaze coordination in this interaction. These results support the technical feasibility of capturing gaze coordination in triadic interactions using mobile eye tracking and underscore the importance of carefully considering how synchrony is operationalized. The proposed workflow provides a foundation that future studies can evaluate and extend across interactions and contexts.

Eye-tracking signals such as pupil diameter and gaze behavior have been widely used for stress detection, yet most approaches rely on task-specific features, controlled laboratory settings, or multimodal sensor combinations, limiting scalability in less controlled environments. This work investigates whether unimodal eye-tracking time-series data can support task-agnostic stress detection beyond static laboratory tasks. We analyze stress classification across two complementary datasets: a virtual reality goalkeeper task with moderate visuomotor activity and stable recording conditions, and a virtual job interview dataset reflecting less controlled settings with uncalibrated signals. The results show that these signals alone contain informative patterns related to stress-associated autonomic and oculomotor responses. Under favorable conditions, performance reaches up to [Formula: see text] macro-averaged F1-score. At the same time, performance varies substantially across datasets, indicating that effective learning depends strongly on data quality, calibration, signal characteristics, and task design. Overall, the findings demonstrate the potential of unimodal eye tracking as a lower-burden alternative to more complex multimodal systems, while highlighting that reliable stress detection is fundamentally conditioned by the interplay of data, signal representation, and modeling approach.

Intracytoplasmic sperm injection (ICSI) is a highly complex procedure that involves injecting a single sperm into an oocyte, requiring extensive training and advanced technical expertise, making it a task performed by specialists. Acquiring specialized microinjection skills alone often requires several years of training. ICSI performance has been primarily evaluated based on developmental outcomes, with little detailed assessment of operators' intrinsic skills. We analyzed expertise during microinjection using eye-tracking technology, which was recently employed in surgical and sports domains to evaluate expert performance. Eye tracking was used to compare the fixation patterns and eye-gaze behaviors of experts and novices during microinjection. Our results showed that the experts had shorter and more consistent procedural times than the novices did. In contrast, the novices initially took longer times; although their time gradually decreased, they remained unstable. This difference was particularly noticeable during oocyte rotation. Similar patterns were observed for fixation duration and the number of saccades. The heat maps and gaze plots revealed interesting distinctions between experts and novices. The experts exhibited efficient and highly consistent eye gaze patterns. Their eye-gaze data may contribute to developing AI-driven automated ICSI skill evaluation systems and AI- and robotics-based ICSI technical support and automation methods.

Cognitive neuroscience research often relies on convenience sampling of participants, which can result in biased sample demographics and an under-representation of older adults. There is a need to identify more effective routes to widen participation among older adults and to explore age-related differences in the motivators and barriers to research involvement. This mixed methods study combined qualitative data from two focus groups, conducted with N = 11 healthy older adults aged 55-73, and an online questionnaire completed by N = 336 adults aged 18-88. Analysis of the focus group discussions identified 3 main themes that were most important to older adults: a) The importance of receiving transparent information about the aims, procedures and safety of the study, b) Distinguishing between medical and non-medical research, and c) Contributing to the "collective good". The questionnaire echoed that altruism, and the prospect of scientific discovery, are increasingly important motivators with advancing age, whereas financial incentives become less important. Older adults have more free time to participate, are less deterred by the prospect of pain, and express more trust in researchers than younger people. Attitudes towards different imaging methods (MRI, EEG, NIBS and Eye tracking) varied, with fewest negative emotions for eye-tracking and most for non-invasive brain stimulation, but positive attitudes generally increased and negative attitudes reduced with age. These findings can inform age-tailored recruitment strategies to improve diversity in neuroimaging research. Improving communication, addressing practical barriers, and framing studies in a meaningful context may help increase participation among groups who are traditionally underrepresented in neuroimaging research.

Eye Tracking (ET) can help improve understanding of visual attention in computer-supported interactive environments. In attention tasks, distinguishing between relevant target objects and distractors is crucial for effective performance, yet the underlying gaze patterns that drive successful task completion remain incompletely understood. Traditional gaze analyses provide limited insight into the temporal dynamics of attention allocation and the relationship between gaze behavior and task performance. When applied to complex visual search scenarios, current gaze analysis methods face several limitations, including the isolation of measurements in dynamic environments, visual stability, search efficiency, and the task-solving processes involved. This paper proposes an analysis tool, VisiTrail, that considers time-series eye-tracking data on task performance and gaze measures; temporal pattern analysis that reveals how attention evolves throughout task performance; object-click sequence tracking that directly links visual attention to user actions; and performance metrics that quantify both accuracy and efficiency of right actions. The proposed analysis is applied to data collected from the Mushroom Hunter serious game, a multilevel visual search task in which participants identify target mushrooms among distractors of increasing complexity across three difficulty levels. This tool focuses on two scenarios: subject-specific analysis and general analysis across all subjects from which a project collected data from Romania and Portugal. Subject-specific analysis uses only one participant's data and yields two types of results: a first-level overall analysis that provides detailed insights and a multilevel analysis that compares performance across all three levels, where Level 1 presents the easiest task with few distractors, Level 2 increases difficulty by adding more similar distractors, and Level 3 is the hardest with many closely resembling distractors and more complex layouts. The generalized analysis reveals that gaze stability (Fixation %) was broadly consistent across both cohorts. Romanian participants exhibited faster median reaction times than Portuguese participants; however, given the small sample size (N=7 per cohort), the presence of data-quality anomalies in three Portuguese sessions, and the absence of inferential statistical testing, this difference should be regarded as preliminary and hypothesis-generating rather than conclusive. By using standardized parameters, the results reduce accidental interactions and hardware noise, providing a reliable methodological basis for preliminary analysis and highlighting the need for larger sample sizes to establish statistical validity.

ABSTRACTToddlers learn to recognize objects from different viewpoints with almost no supervision. During this learning, they execute frequent eye and head movements that shape their visual experience. It is presently unclear if and how these behaviors contribute to toddlers’ emerging object recognition abilities. To answer this question, we here combine head‐mounted eye tracking during dyadic play with unsupervised machine learning. We approximate toddlers’ central visual field experience by cropping image regions from a head‐mounted camera centered on the current gaze location estimated via eye tracking. This visual stream feeds a neural network model, which uses a biologically plausible unsupervised learning objective. Our experiments demonstrate that a few minutes of such first‐person experience suffice to learn strong object representations permitting invariant object recognition. Importantly, by simulating alternative gaze behaviors we show that toddlers’ eye movement patterns play a crucial role in this. Our analysis also reveals that the limited size of the central visual field where visual acuity is high plays an important role for successful learning. Together, this highlights the benefits of temporally structured visual experience arising from toddlers’ natural interactions with objects.SummaryWe combine recordings of toddlers’ first‐person central visual field experience with biologically inspired self‐supervised learning algorithms to model toddlers’ development of invariant object recognition.Just a few minutes of toddlers’ central visual field experience captured with head‐mounted eye tracking suffice to learn strong object representations.Simulated alternative gaze behaviors produce weaker representations, demonstrating the importance of toddlers’ active gaze strategies for learning.Our results emphasize the importance of toddlers’ eye movements for learning object representations.

The transition toward digital pathology brings ergonomic and efficiency challenges with mouse or other hand-based input devices for digital slide assessment, requiring users to move their hands frequently, raising the risk of repetitive strain injury. This work explores non-hand-based input devices for digital pathology. To determine the feasibility of using eye and head tracking as input devices for completing representative tasks in digital pathology, compared to the computer mouse. Eight pathologists completed 5 representative digital pathology tasks: measuring, counting, zooming, panning, and target identification. Each task was first completed with the computer mouse as the input device, then repeated with eye and head tracking as the input devices, after a washout. Eye gaze data were collected throughout. Pathologists were asked to complete a survey on their experience. Measurements made with eye gaze were 0.18% more accurate than those made with mouse (P = .001). Differences in point localization accuracy for measurement and counting with eye gaze were both less than 10 μm, compared to mouse (P < .001, for both). Hand movement was reduced for counting, zooming, and panning with eye and head tracking (P = .04, P = .009, and P = .04, respectively). No targets were missed with head tracking, whereas 8 targets were missed with mouse. Most pathologists favored eye tracking for counting and zooming. Eye and head tracking are feasible input devices for digital pathology and provide comparable accuracy to the computer mouse. Head tracking suggests continuous panning of the image is a superior and safer method when an exhaustive search for targets is required.

Changes in gaze behaviors during psychomotor skill training in laparoscopic surgery: A longitudinal eye tracking study.

ABSTRACT In today’s increasingly visual world, developing systematic approaches to strengthen students’ visual literacy has become essential. Visual literacy is defined as the ability to read, interpret, and construct meaning from visual representations through the use of visual language and visual structures. This study investigated the impact of collaborative visualization on the development of visual interpretation, one of the core visual reading skills within visual literacy that involves decoding visual language, analyzing visual structures, and constructing meaning from images. Collaborative visualization involves learners jointly viewing, discussing, interpreting, and creating visuals, while visual interpretation refers to perceiving an image, analyzing it through visual language, and constructing meaning collaboratively. Using a pretest – posttest control group design, the study measured students’ visual interpretation performance and attention patterns with a test based on visual language and administered via an online eye-tracking tool. Results indicated that collaborative visualization significantly enhanced visual interpretation skills compared with individual practice. Eye-tracking data further supported these outcomes, showing earlier and more focused attention on visually and semantically relevant areas, suggesting more effective visual reading processes. Overall, the study provides a novel perspective on improving visual interpretation and broader visual literacy skills through structured collaborative learning.

Early identification of autism spectrum disorder (ASD) is essential for improving developmental outcomes but remains challenging due to diagnostic delays, subjectivity, and resource limitations. Eye-tracking offers objective indices of social attention and could improve access to early screening when deployed on consumer devices at home. This scoping review synthesizes evidence on home-deployable eye-tracking as digital biomarkers for early ASD screening, associated machine learning methods, and feasibility of real-world implementation. Following the Arksey and O'Malley framework and Joanna Briggs Institute (JBI) methodology, reported per PRISMA Extension for Scoping Reviews (PRISMA-ScR), we searched PubMed and Scopus (2015-2025) for English-language human studies using terms for autism, eye-tracking, and home-based assessment. Using a Population, Concept, Context (PCC) framework (Population: infants/ children; Concept: eye-tracking as digital biomarker; Context: home/ clinical settings), two reviewers screened each record and charted data on participant characteristics, stimuli, devices, analytic methods, diagnostic performance, and implementation indicators. Extracted data, including diagnostic performance metrics (PPV, NPV, confidence intervals), study design classifications, validation methods, and biomarker readiness ratings, are provided in full in Supplementary Tables S2-S6. The protocol was preregistered on the Open Science Framework (OSF Registries osf.io/mdz2e/; https://doi.org/10.17605/OSF.IO/MDZ2E). Searches were last executed on 1 August 2025. A process-based estimation approach was used to quantify the EOL carbon footprint of two timber floor systems-Adhesive & Screw and Sharp Plate & Screw, following ISO 14040/44 standards. Four realistic EOL pathways (landfilling, downcycling, component reuse, and full assembly reuse) were assessed under three recovery-rate scenarios (90%, 60%, and 30%) to capture both ideal deconstruction and conventional demolition conditions. EOL impacts, biogenic carbon flows, and Stage D credits (potential environmental benefits beyond building life) were integrated to determine the EOL climate outcomes. Across 90 studies, reported discrimination typically ranged from moderate to high. Sample sizes varied from small pilots to large datasets (n = 30-1,000+), with ages from 5 months to 18 years. Studies were categorized by system type: hardware-based, wearable, webcam-based, and home-deployed. By study purpose, studies ranged from prospective screening evaluations and internally validated case-control classification studies to exploratory group-difference analyses and feasibility evaluations; the majority fall into the latter two categories. Common tasks included faces/social scenes, biological motion, and joint-attention cues; frequently reported metrics included fixation proportion, dwell time, saccade dynamics, and interest-area contrasts (eyes/face vs. objects). Supervised learning (e.g. SVM, random forests) and deep learning pipelines were used, though external validation and calibration-robustness were inconsistently reported. Practical barriers included ambient-light variability, viewing distance, caregiver facilitation, device heterogeneity, and data privacy/governance. This review mapped 90 studies across four eye-tracking platform stages to assess translational readiness for home-based ASD screening. Gaze-based biomarkers carry robust discriminative signal, but the evidence derives almost entirely from laboratory-based, case-control studies. Only three studies collected data in home-deployed settings, none with externally validated accuracy. Priorities include prospective multisite home-based trials with prespecified endpoints, standardised stimulus protocols, evaluation of model robustness to distribution shift, and navigation of jurisdiction-specific regulatory pathways. Limitations include English-only sourcing, two databases, the 2015-2025 window, and lack of critical appraisal. Not Applicable.

Proteomics represents a powerful but underutilized approach for characterizing eye aging. Here, leveraging data from three large-scale, cross-national cohorts of over 55,000 transethnic participants, we demonstrate the ability of high-throughput proteomics combined with deep learning (DL) phenotyping to track eye aging and disease in both discovery and external validation settings. Proteomic aging driven by machine learning modeling closely aligns with signals of eye aging and DL aging phenotypes. We identifiy and validate premature proteomic aging in individuals with major age-related eye diseases (AREDs), including cataract, diabetic retinopathy, age-related macular degeneration, and glaucoma, and propose evidence supporting proteomic aging acceleration as a robust biomarker for predicting these conditions beyond chronological age, with adaptability across sexes and ethnicities. We also develop a streamlined, cost-effective proteomic aging clock that preserves predictive performance while reducing assay complexity. By integrating advanced tomographic and angiographic imaging, we derive structural and functional biomarkers through DL-driven pipelines and link accelerated proteomic aging to both neuroretinal degeneration and microvascular rarefaction in the Guangzhou Diabetic Eye Study (GDES) and the High-definition Oculo-Phenomic Evaluation (HOPE) study, highlighting coupled neural-vascular decline in eye aging. Our findings position proteomic aging combined with AI as a scalable tool for tracking eye health and disease, and provides new insights into shared aging pathways underlying multiple ocular pathologies.