Yupana Inka Tawa Pukllay arithmetic eye tracking analysis: novices

Diagnosing autism spectrum disorder (ASD) in children poses significant challenges due to its complex nature and impact on social communication development. While numerous data analytics techniques have been proposed for ASD evaluation, the process remains time-consuming and lacks clarity. Eye tracking (ET) data has emerged as a valuable resource for ASD risk assessment, yet existing literature predominantly focuses on predictive methods rather than descriptive techniques that offer human-friendly insights. Interpretation of ET data and Bayley scales, a widely used assessment tool, is challenging for ASD assessment of children. It should be understood clearly to perform better analytic tasks on ASD screening. Therefore, this study addresses this gap by employing linguistic summarization techniques to generate easily understandable summaries from raw ET data and Bayley scales. By integrating ET data and Bayley scores, the study aims to improve the identification of children with ASD from typically developing children (TD). Notably, this research represents one of the pioneering efforts to linguistically summarize ET data alongside Bayley scales, presenting comparative results between children with ASD and TD. Through linguistic summarization, this study facilitates the creation of simple, natural language statements, offering a first and unique approach to enhance ASD screening and contribute to our understanding of neurodevelopmental disorders.

Situation Awareness (SA) is the perception of the current situation, comprehension of its meaning, and projection of what is going to happen in the near future. It is crucial for navigators to possess high SA in a navigational Bridge to mitigate the risk of human errors and to improve navigational safety. However, the current methodology to assess SA mainly rely on human experts, which might bring in potential problems such as bias, work overload, and it is also hard for the human experts to capture every fine detail of the behaviour of the seafarers being assessed. To overcome these, an objective and automated way to assess Situation Awareness is needed. In this work, eye-tracking data is used for the assessment of SA. With the eye tracking device, it is possible to localize where the navigator is looking at, and by applying computer vision with deep learning algorithm, the ongoing activity being executed by the navigator could be identified. In total 7 activities (using RADAR, ECDIS, checking of ship’s heading, and speed, checking data on Echo Sounder, and data related to ships maneuvering, and others) can be recognized which are used as indicators of SA. A set of training data was recorded using Tobii Pro Glasses 3 to train the deep learning algorithm and test the classification accuracy. To further verify the proposed eye-tracking based assessment, a preliminary experiment has been designed and carried out. Five subjects were recruited for data collection. A full-mission Advanced Navigation Research Simulator (ANRS) was used to provide scenarios for both training data collection and preliminary experiment. From the initial results, it shows that a recognition accuracy of > 99% can be achieved, which gives positive support to the eye-tracking based recognition. The analytics results using data from preliminary experiment also show great potential in using eye-tracking to assess SA of navigators. The proposed assessment could be used in both simulator and on-board and for multiple purposes such as performance evaluation, promotion to the next rank, and Continuing Professional Development.

Despite its numerous scientifically cited benefits, traceability is still rarely established in industrial settings. Years of research in the area have brought several different approaches to create traceability links including Information Retrieval (IR) and Machine Learning approaches. However, their accuracy and overall traceability support is not sufficient yet to be properly applied in practice. In my research, I want to investigate the usage of eye tracking and interaction data in the field of traceability. By tracking how software engineers interact with documents, where they focus on and recording gaze links between those documents, an algorithm is designed to obtain trace links between artifacts from these data. Eye tracking and interaction data have the advantage that they can be recorded in an automatic, non-intrusive way without requiring manual effort. They give detailed insight about where people focus on when working on tasks. However, software support of eye tracking is still limited, especially in the context of dynamic content such as switching between, scrolling or editing documents. Therefore, one essential step of my research is to provide software support for recording eye tracking data in dynamic document environments. By combining eye tracking data with additionally recorded metadata such as interactions, this eye tracking framework shall enable the automatic capturing of gaze links and gaze durations during software engineering tasks. The approach of using eye tracking in the context of traceability will be evaluated in several usage scenarios such as requirements coverage assessment.

Introduction.- Section I: Eye Tracking and the Visual System.- The Active Eye: Perspectives on Eye Movement Research, Benjamin W. Tatler, Clare Kirtley, Ross G. Macdonald, Katy M. A. Mitchell, Steven W. Savage.- Eye Movements from Lab to Life, Benjamin W. Tatler.- Guidance of Attention by Feature Relationships: The End of the Road for Feature Map Theories? Stefanie I. Becker.- Gaze And Speech: Pointing Device and Text Entry Modality, T.R. Beelders and P.J. Blignaut.- Improving the Accuracy of Video-Based Eye-Tracking in Real-Time through Post-Calibration Regression, Pieter Blignaut, Kenneth Holmqvist, Marcus Nystrom, Richard Dewhurst .- Gaze Shifts and Pen Velocity Minima during Line Copying with Consideration to Signature Simulation, Avni Pepe and Jodi Sita.- Degree of Subject's Indecisiveness Characterized by Eye Movement Patterns in Increasingly Difficult Tasks, Yannick Lufimpu-Luviya, Djamel Merad, Veronique Drai-Zerbib, Thierry Baccino and Bernard Fertil. The Use of an Infrared Eye Tracker in Evaluating the Reading Performance in a Congenital Nystagmus Patient Fitted with Soft Contact Lens: A Case Report, M. M. Shahimin, N. H. Saliman, N. Mohamad-Fadzil, Z. Mohammed, N. A. Razali, H. A. Mutalib and N. Mennie.- Section II: Aligning Eye Tracking and EEG Data.- Triangulating the Reading Brain: Eye Movements, Computational Models, and EEG, Ronan G. Reilly.- Oculomotor Control, Brain Potentials, and Timelines of Word Recognition during Natural Reading, Reinhold Kliegl, Michael Dambacher, Olaf Dimigen, Werner Sommer.- Measuring Neuronal Correlates of Reading with Novel Spread-Spectrum Protocols, Ronan G. Reilly.- The Quest for Integrating Data in Mixed Research: User Experience Research Revisited, Annika Wiklund-Engblom and Joachim Hogvag.- Section III: Eye Tracking and Marketing and Social Applications.- Eye Tracking as a Research Method in Social and Marketing Applications, Mike Horsley.- Mobile Eye-Tracking in Retail Research, Dr Tracy Harwood, Martin Jones.- Private and Public: Eye Tracking Applications in Private and Academic Sector Marketing Research, En Li, James Breeze, Mike Horsley, Donnel A. Briely.- Eye Movement Evaluation of Signature Forgeries: inSights to Forensic Expert Evidence, A. G. Dyer, B. Found, M. L. Merlino, A. L. Pepe, D. Rogers and J. C. Sita.- A Role for Eye Tracking Research in Accounting and Financial Reporting? Lyn Grigg and Amy L. Griffin.- Eye Tracking during a Psychosocial Stress Simulation: Insights into Social Anxiety Disorder, Nigel Chen.- Using Saccadic Eye Movements to Assess Cognitive Decline with Ageing, Alison Bowling and Anja Draper.- Comparing Personally-Tailored Video- and Text-Delivered Web-Based Physical Activity Interventions - The Medium and the Message: An Eye-Tracking Study, Corneel Vandelanotte, Naya Persaud, Stephanie Bland, Mike Horsley.- Benefits of Complementing Eye Tracking Analysis with Think Aloud Protocol in a Multilingual Country with High Power Distance, Ashok Sivaji and Wan Fatimah Wan Ahmad.- Section IV: Eye Tracking and Education.- Eye Tracking and the Learning System: An Overview, Bruce Allen Knight, Mike Horsley & Matt Eliot.- A New Approach to Cognitive Metrics: Analysing the Visual Mechanics of Comprehension using Eye Tracking Data in Student Completion of High Stakes Testing Evaluation, Bruce Allen Knight and Mike Horsley.- Comparing Novice and Expert Nurses in Analysing Electrocardiographs (ECGs) Containing Critical Diagnostic Information: An Eye Tracking Study of the Development of Complex Nursing Visual Cognitive Skills, Marc Broadbent, Mike Horsley, Melanie Birks, Naya Persaud.- The Development and Refinement of Student Self-Regulatory Strategies in Online Learning Environments, Nayadin Persaud and Matt Eliot.

Classification of schizophrenia based on RAnet-ET: resnet based attention network for eye-tracking

In this paper we present a study aimed at distinguishing elderly (over 65 years) and young (under 25) participants in driving environment by observing solely their eye movements. Selected groups of elderly and young drivers were asked to drive 30 km on suburban, urban and regional roads in a high-fidelity motion-based driving simulator. During the drive their gaze behaviour and eye movements were recorded using the Tobii Pro Glasses 2 eye tracker, providing data on gaze position, blink rate and pupil size. The data was processed with the PyGaze library, which was adapted to be compatible with the Tobii Pro data output format. In the next step, a decision tree-based binary classification method was applied to distinguish between the two age groups based solely on their eye movements and pupillary responses. The machine learning approach showed an overall accuracy of 0.8 which means that eye tracking data can be a very good predictor of driver’s age in a driving environment.

The importance of teacher leaders has been increasingly emphasized in innovative educational practices. In this study, we present the experiences of innovative teacher leaders (N = 10) in Lithuania. The sample of respondents is qualitative-purposive. Teachers who have been awarded for their innovative practice took part in the study. In addition, teachers nominated by their school communities as innovative educators who are making a difference in their schools were also included. The data analyzed by the method of critical analysis of qualitative research discourse will help the reader understand what helps teacher leaders develop innovative educational culture practices. Additionally, the data reveal how teacher leaders themselves understand leadership, which motivates them to work innovatively. We found that innovative teachers in Lithuania perceive the phenomenon of transformational leadership in four categories, namely: attention to others, initiating change, community building, development, and improvement. The participants in the study also described the meaning of innovative teacher behavior, which is revealed in the analysis of two categories: quality of education and educational change. The third component, innovative organizational culture, was discussed by presenting teachers’ insights on three categories: planning, implementation, and motivation. Teacher leaders support, create and nurture a culture of innovation by motivating and encouraging their colleagues to improve. The findings of this study are useful for school communities wanting to make changes, as they provide answers to what motivates teachers to change their practices and to mobilise other educators for common goals.

To develop and evaluate the concept of a non-invasive machine learning (ML) approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and eye tracking (ET) data. We first developed and tested our ML approach in pronounced hypoglycaemia, and then we applied it to mild hypoglycaemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes. In study 1 (n=18), we collected CAN and ET data in a driving simulator during euglycaemia and pronounced hypoglycaemia (blood glucose [BG] 2.0-2.5 mmol L-1 ). In study 2 (n=9), we collected CAN and ET data in the same simulator but in euglycaemia and mild hypoglycaemia (BG 3.0-3.5 mmol L-1 ). Here, we show that our ML approach detects pronounced and mild hypoglycaemia with high accuracy (area under the receiver operating characteristics curve 0.88 ± 0.10 and 0.83 ± 0.11, respectively). Our findings suggest that an ML approach based on CAN and ET data, exclusively, enables detection of hypoglycaemia while driving. This provides a promising concept for alternative and non-invasive detection of hypoglycaemia.

Visual-spatial skills contribute to Chinese reading and arithmetic for different reasons: A three-wave longitudinal study

Eye tracking has been used in usability testing for many years to gain objective measurements to inform label, instruction, and product design. With many different testing environments, possible participants, hardware, and software, key metrics can vary greatly. These metrics can also vary for different studies, so a standardized test method, metrics, and calculation method are proposed in this study. The Tobii Pro Glasses 2 is a mobile eye tracker that does not significantly affect user mobility compared to other eye-trackers. This study aims to build a testing method, which can modify to better fit the varying conditions found in usability testing. This study was performed with Tobii Pro Glasses 2; however, this test method can be used with any mobile eye tracking units. Even with poor testing conditions, this test method results in reliable metrics which can be utilized to inform expectation and decisions with eye tracking. These methods are recommended to be performed prior to eye tracking testing to determine testing-specific performance.

Changes in pupil size can lead to apparent gaze shifts in data recorded with video-based eye trackers in the absence of physical eye rotation. This is known as the pupil-size artifact (PSA). While the PSA is widely reported in desktop eye trackers, it is unknown whether and to what extent it occurs in head-mounted eye trackers. In this paper, we examined the effects of pupil size variations on eye-tracking data quality in four head-mounted eye trackers: the Pupil Core, the Pupil Neon, the SMI ETG 2w, and the Tobii Pro Glasses 2, in addition to a widely used desktop eye tracker, the SR Research EyeLink 1000 Plus. Participants viewed a central target on a monitor while we systematically varied the screen brightness to induce controlled pupil size changes. All head-mounted eye trackers exhibited PSA, with apparent gaze shifts ranging from 0.94^circ for the Pupil Neon to 3.46^circ for the Pupil Core. Except for the Pupil Neon, all eye trackers exhibited a significant change in accuracy due to pupil size variations. Precision measures showed device-specific effects of pupil size changes, with some eye trackers performing better in the bright condition and others in the dark condition. These findings demonstrated that, just like desktop eye trackers, head-mounted video-based eye trackers exhibited PSA.

HighlightsWhat are the main findings?This study introduces a robust methodology for assessing visual fatigue in real industrial environments, specifically in a pharmaceutical packaging area by integrating subjective reports, biometric eye-tracking data, and environmental lighting measurements.Results reveal a consistent increase in visual fatigue across the workweek and shifts, with critical fatigue peaks during night shifts and tasks involving high physical and visual demand under suboptimal lighting.What is the implication of the main finding?Combining physiological, perceptual, and environmental data addresses a major gap in real-time visual fatigue monitoring on the shop floor, moving beyond control room and laboratory-based assessments.The approach contributes to Industry 5.0 goals by enabling adaptive, human-centered interventions that enhance operator well-being, task accuracy, and sustainable ergonomic design.This study investigates visual fatigue in a real-world pharmaceutical packaging environment, where operators perform repetitive inspection and packing tasks under frequently suboptimal lighting conditions. A human-centered methodology was adopted, combining adapted self-report questionnaires, high-frequency eye-tracking data collected with Tobii Pro Glasses 3, and lux-level measurements. Key eye-movement metrics—including fixation duration, visit patterns, and pupil diameter—were analyzed within defined work zones (Areas of Interest). To reduce data complexity and uncover latent patterns of visual behavior, Principal Component Analysis was applied. Results revealed a progressive increase in visual fatigue across the workweek and throughout shifts, particularly during night work, and showed a strong association with inadequate lighting. Tasks involving high physical workload under poor illumination emerged as critical risk scenarios. This integrated approach not only confirmed the presence of visual fatigue but also identified high-risk conditions in the workflow, enabling targeted ergonomic interventions. The findings provide a practical framework for improving operator well-being and inspection performance through sensor-based monitoring and environment-specific design enhancements, in alignment with the goals of Industry 5.0.

Purpose: The study aimed to compare eye tracking (ET) and manual coding (MC) measures of attention to social and nonsocial information in infants with elevated familial likelihood (EL) of autism spectrum disorder (ASD) and low likelihood of ASD (LL). ET provides a temporally and spatially sensitive tool for measuring gaze allocation. Existing evidence suggests that ET is a promising tool for detecting distinct social attention patterns that may serve as a biomarker for ASD. However, ET is prone to data loss, especially in young EL infants. Methods: To increase evidence for ET as a viable tool for capturing atypical social attention in EL infants, the current prospective, longitudinal study obtained ET and MC measures of social and nonsocial attention in 25 EL and 47 LL infants at several time points between 3 and 24 months of age. Results: ET data was obtained with a satisfactory success rate of 95.83%, albeit with a higher degree of data loss compared to MC. Infant age and ASD likelihood status did not impact the extent of ET or MC data loss. There was a significant positive association between the ET and MC measures of attention, and separate analyses of attention using ET and AC measures yielded comparable findings. These analyses indicated group differences (EL vs. LL) in age-related change in attention to social vs. nonsocial information. Conclusion: Together, the findings support infant ET as a promising approach for identifying very early markers associated with ASD likelihood.

We present GlassesViewer, open-source software for viewing and analyzing eye-tracking data of the Tobii Pro Glasses 2 head-mounted eye tracker as well as the scene and eye videos and other data streams (pupil size, gyroscope, accelerometer, and TTL input) that this headset can record. The software provides the following functionality written in MATLAB: (1) a graphical interface for navigating the study- and recording structure produced by the Tobii Glasses 2; (2) functionality to unpack, parse, and synchronize the various data and video streams comprising a Glasses 2 recording; and (3) a graphical interface for viewing the Glasses 2’s gaze direction, pupil size, gyroscope and accelerometer time-series data, along with the recorded scene and eye camera videos. In this latter interface, segments of data can furthermore be labeled through user-provided event classification algorithms or by means of manual annotation. Lastly, the toolbox provides integration with the GazeCode tool by Benjamins et al. (2018), enabling a completely open-source workflow for analyzing Tobii Pro Glasses 2 recordings.

Application of Threshold-bias Independent Analysis to Eye-tracking and FROC Data