Eye movement dysfunction is one of the non-motor symptoms of Parkinson\'s disease (PD). An accurate analysis method for eye movement is an effective way to gain a deeper understanding of the nervous system function of PD patients. However, currently, there are only a few assistive methods available to help physicians conveniently and consistently assess patients suspected of having PD. To solve this problem, we proposed a novel visual behavioral analysis method using eye tracking to evaluate eye movement dysfunction in PD patients automatically. This method first provided a physician task simulation to induce PD-related eye movements in Virtual Reality (VR). Subsequently, we extracted eye movement features from recorded eye videos and applied a machine learning algorithm to establish a PD diagnostic model. Then, we collected eye movement data from 66 participants (including 22 healthy controls and 44 PD patients) in a VR environment for training and testing during visual tasks. Finally, on this relatively small dataset, the results reveal that the Support Vector Machine (SVM) algorithm has better classification potential.

Impaired joint attention is a common feature of autism spectrum disorder (ASD), affecting social interaction and communication. We explored if group basketball learning could enhance joint attention in autistic children, and how this relates to brain changes, particularly white matter development integrity. Forty-nine autistic children, aged 4-12 years, were recruited from special education centers. The experimental group underwent a 12-week basketball motor skill learning, while the control group received standard care. Eye-tracking and brain scans were conducted. The 12-week basketball motor skill learning improved joint attention in the experimental group, evidenced by better eye tracking metrics and enhanced white matter integrity. Moreover, reduced time to first fixation correlated positively with decreased mean diffusivity of the left superior corona radiata and left superior fronto-occipital fasciculus in the experimental group. Basketball-based motor skill intervention effectively improved joint attention in autistic children. Improved white matter fiber integrity related to sensory perception, spatial and early attention function may underlie this effect. These findings highlight the potential of group motor skill learning within clinical rehabilitation for treating ASD.

Flight safety in helicopters is a critical aspect of overall aircraft operational safety management, particularly during engine failures requiring autorotative glide, which makes it extremely challenging for the pilot to land the helicopter successfully. In this study, we evaluated the workload and attention allocation of helicopter pilots under such circumstances. In the experiment, a helicopter flight simulator was used to simulate level flight followed by autorotative glide, with the two phases divided into time segments for data collection. First, the data were visualized using heat maps and saccade sequence diagrams, while changes in eye movement metrics (such as peak value and standard deviation) were statistically analyzed. Finally, the criteria through the inter-criteria correlation (CRITIC) method was used to calculate the weight coefficient for each area of interest. This evaluation system was further applied to analyze and compare the changes in eye-movement data and attention to areas of interest during the two phases. The results revealed a shorter fixation duration, but a greater fixation number during the autorotative glide phase. Further, the mean pupil diameter changed over a larger range than during level flight (in level flight, the mean was 5.229 mm, while the standard deviation was 0.059 mm; in autorotative glide the corresponding values were 5.326 mm and 0.126 mm, respectively). For the tachometer, the weight coefficient matched the color of the heat map (2.7 % and colorless during level flight, but 23.8 % and red during autorotative glide), while those for the airspeed indicator and forward view differed significantly between the two phases. This discrepancy stemmed from the fact that during autorotative glide, the pilots prioritized monitoring aircraft rotation speed and attitude, with a particular focus on the forward view, rotor speed, and airspeed, resulting in a more concentrated attention distribution compared to that achieved during level flight. These results confirmed a significant increase in pilot workload during autorotative glide landing, while a shift was observed from low-frequency long gaze time during level flight to high-frequency short gaze time during autorotative glide. Furthermore, the pilots allocated 81 % of their attention to the tachometer, airspeed indicator, and forward views. Adopting this strategy can improve pilots\' landing success and provide flight students with valuable training advice to prevent landing failures when helicopters lose power.

Reliance on digital navigation aids has already shown negative impacts on navigators\' innate spatial abilities. How this happens is still an open research question. We report on an empirical study with twenty-four experienced (male) taxi drivers to evaluate the long-term impacts of in-car navigation system use on the spatial learning ability of these navigation experts. Specifically, we measured cognitive load by means of electroencephalography (EEG) coupled with eye tracking to assess their visuospatial attention allocation during a video-based route-following task while driving through an unknown urban environment. We found that long-term reliance on in-car navigation aids did not affect participants\' visual attention allocation during spatial learning but rather limited their ability to encode viewed geographic information into memory, which, in turn, led to greater cognitive load, especially along route segments between intersections. Participants with greater dependence on in-car navigation aids performed worse on the spatial knowledge tests. Our combined behavioral and neuropsychological findings provide evidence for the impairment of expert navigators\' spatial learning ability when exposed to long-term use of digital in-car navigation aids.

Cognition is crucial to brain function, and accurately classifying cognitive load is essential for understanding the psychological processes across tasks. This paper innovatively combines functional near-infrared spectroscopy (fNIRS) with eye tracking technology to delve into the classification of cognitive load at the neurocognitive level. This integration overcomes the limitations of a single modality, addressing challenges such as feature selection, high dimensionality, and insufficient sample capacity. We employ fNIRS-eye tracking technology to collect neural activity and eye tracking data during various cognitive tasks, followed by preprocessing. Using the maximum relevance minimum redundancy algorithm, we extract the most relevant features and evaluate their impact on the classification task. We evaluate the classification performance by building models (naive Bayes, support vector machine, K-nearest neighbors, and random forest) and employing cross-validation. The results demonstrate the effectiveness of fNIRS-eye tracking, the maximum relevance minimum redundancy algorithm, and machine learning techniques in discriminating cognitive load levels. This study emphasizes the impact of the number of features on performance, highlighting the need for an optimal feature set to improve accuracy. These findings advance our understanding of neuroscientific features related to cognitive load, propelling neural psychology research to deeper levels and holding significant implications for future cognitive science.

This study explores the learning effects of color cues in video lectures and their underlying mechanisms. With the rapid growth of online education, lifelong learning, and blended learning, video lectures have become integral to teaching and learning. Color, a crucial element in visual design, directs attention, organizes content, and integrates information. Evaluating 78 college students, we assessed learning performance by comparing video content with no-color, single-color, and multi-color cues using eye-tracking technology and cognitive load scales. Results indicate that students viewing videos with color cues demonstrated better retention and transfer test performance, while absence or excess of color cues increased cognitive load. These findings have practical implications for video producers and provide a theoretical foundation for enhancing learners\' viewing experience and overall effectiveness. This study not only offers an in-depth analysis of color cue utilization in video lectures, highlighting their positive impact on learning outcomes but also introduces fresh perspectives for educational technology and cognitive psychology research. Future investigations should consider color cue effects in diverse cultural contexts and subject areas, exploring varied strategies to optimize the learning experience.

Gaze abnormalities are well documented in infants at elevated risk for autism spectrum disorder (ASD). However, variations in experimental design and stimuli across studies have led to mixed results. The current meta-analysis aimed to identify which type of eye tracking task and stimulus are most effective at differentiating high-risk infants (siblings of children with ASD) who later meet diagnosis criteria from low-risk infants without familial autism. We synthesized 35 studies that used eye tracking to investigate gaze behavior in infants at high genetic risk for autism before 2 years of age. We found that stimulus features, regions of interest (ROIs) and study quality moderated effect sizes across studies. Overall, dynamic stimuli and socially-relevant regions in the social stimuli (i.e. the target and activity of characters\' shared focus) reliably detected high-risk infants who later develop ASD. Attention disengagement task and stimuli depicting interactions between human and nonhuman characters could identify high-risk infants who later develop ASD and those who have autism-related symptoms but do not meet the diagnostic criteria as well. These findings provide sensitive and reliable early markers of ASD, which is helpful to develop objective and quantitative early autism screening and intervention tools.

Advancements in information technology have facilitated the emergence of mHealth apps as crucial tools for health management and chronic disease prevention. This research work focuses on mHealth apps for the management of diabetes by patients on their own. Given that China has the highest number of diabetes patients in the world, with 141 million people and a prevalence rate of 12.8% (mentioned in the Global Overview of Diabetes), the development of a usability research methodology to assess and validate the user-friendliness of apps is necessary. This study describes a usability evaluation model that combines task analysis methods and eye movement data. A blood glucose recording application was designed to be evaluated. The evaluation was designed based on the model, and the feasibility of the model was demonstrated by comparing the usability of the blood glucose logging application before and after a prototype modification based on the improvement suggestions derived from the evaluation. Tests showed that an improvement plan based on error logs and post-task questionnaires for task analysis improves interaction usability by about 24%, in addition to an improvement plan based on eye movement data analysis for hotspot movement acceleration that improves information access usability by about 15%. The results demonstrate that this study presents a usability evaluation model for mHealth apps that enables the effective evaluation of the usability of mHealth apps.

Objective. Eye-tracking research has proven valuable in understanding numerous cognitive functions. Recently, Freyet alprovided an exciting deep learning method for learning eye movements from functional magnetic resonance imaging (fMRI) data. It employed the multi-step co-registration of fMRI into the group template to obtain eyeball signal, and thus required additional templates and was time consuming. To resolve this issue, in this paper, we propose a framework named MRGazer for predicting eye gaze points from fMRI in individual space.Approach. The MRGazer consists of an eyeball extraction module and a residual network-based eye gaze prediction module. Compared to the previous method, the proposed framework skips the fMRI co-registration step, simplifies the processing protocol, and achieves end-to-end eye gaze regression.Main results. The proposed method achieved superior performance in eye fixation regression (Euclidean error, EE = 2.04°) than the co-registration-based method (EE = 2.89°), and delivered objective results within a shorter time (∼0.02 s volume-1) than prior method (∼0.3 s volume-1).Significance. The MRGazer is an efficient, simple, and accurate deep learning framework for predicting eye movement from fMRI data, and can be employed during fMRI scans in psychological and cognitive research. The code is available athttps://github.com/ustc-bmec/MRGazer.

Particle (proton, carbon ion, or others) radiotherapy for ocular tumors is highly dependent on precise dose distribution, and any misalignment can result in severe complications. The proposed eye positioning and tracking system (EPTS) was designed to non-invasively position eyeballs and is reproducible enough to ensure accurate dose distribution by guiding gaze direction and tracking eye motion. Eye positioning was performed by guiding the gaze direction with separately controlled light sources. Eye tracking was performed by a robotic arm with cameras and a mirror. The cameras attached to its end received images through mirror reflection. To maintain a light weight, certain materials, such as carbon fiber, were utilized where possible. The robotic arm was controlled by a robot operating system. The robotic arm, turntables, and light source were actively and remotely controlled in real time. The videos captured by the cameras could be annotated, saved, and loaded into software. The available range of gaze guidance is 360° (azimuth). Weighing a total of 18.55 kg, the EPTS could be installed or uninstalled in 10 s. The structure, motion, and electromagnetic compatibility were verified via experiments. The EPTS shows some potential due to its non-invasive wide-range flexible eye positioning and tracking, light weight, non-collision with other equipment, and compatibility with CT imaging and dose delivery. The EPTS can also be remotely controlled in real time and offers sufficient reproducibility. This system is expected to have a positive impact on ocular particle radiotherapy.