By focusing on high experimental control and realistic presentation, the latest research in expertise assessment of soccer players demonstrates the importance of perceptual skills, especially in decision making. Our work captured omnidirectional in-field scenes displayed through virtual reality glasses to 12 expert players (picked by DFB), 10 regional league intermediate players, and13 novice soccer goalkeepers in order to assess the perceptual skills of athletes in an optimized manner. All scenes were shown from the perspective of the same natural goalkeeper and ended after the return pass to that goalkeeper. Based on the gaze behavior of each player, we classified their expertise with common machine learning techniques. Our results show that eye movements contain highly informative features and thus enable a classification of goalkeepers between three stages of expertise, namely elite youth player, regional league player, and novice, at a high accuracy of 78.2%. This research underscores the importance of eye tracking and machine learning in perceptual expertise research and paves the way for perceptual-cognitive diagnosis as well as future training systems.

Virtual reality and eye-tracking technologies are nowadays standard research tools. A growing number of researchers from different disciplines are utilizing these technologies. Currently, access to eye-tracking hardware in virtual reality glasses is usually provided as APIs to call functions of the eye-tracking device. Proper implementation is device-specific and left to the user. Especially non-computer scientists are left alone with this problem, which impedes eye-tracking research in virtual reality for many scientists. This paper describes a generic open-source interface for everyone to efficiently and easily utilize common eye trackers in virtual reality. The interface is published under a friendly CC BY 4.0 license that allows for integration, modification, and extension of the code. It includes a standardized interface for several eye-tracking devices in virtual reality, is ready to be used out of the box, and allows easy addition of APIs from other manufacturers. The code is available at https://bitbucket.org/benediktwhosp/zvsl-zero

The focus of expertise research moves constantly forward and includes cognitive factors, such as visual information perception and processing. In highly dynamic tasks, such as decision making in sports, these factors become more important to build a foundation for diagnostic systems and adaptive learning environments. Although most recent research focuses on behavioral features, the underlying cognitive mechanisms have been poorly understood, mainly due to a lack of adequate methods for the analysis of complex eye tracking data that goes beyond aggregated fixations and saccades. There are no consistent statements about specific perceptual features that explain expertise. However, these mechanisms are an important part of expertise, especially in decision making in sports games, as highly trained perceptual cognitive abilities can provide athletes with some advantage. We developed a deep learning approach that independently finds latent perceptual features in fixation image patches. It then derives expertise based solely on these fixation patches, which encompass the gaze behavior of athletes in an elaborately implemented virtual reality setup. We present a CNN-BiLSTM based model for expertise assessment in goalkeeper-specific decision tasks on initiating passes in build-up situations. The empirical validation demonstrated that our model has the ability to find valuable latent features that detect the expertise level of 33 athletes (novice, advanced, and expert) with 73.11% accuracy. This model is a first step in the direction of generalizable expertise recognition based on eye movements.

In the rapidly advancing field of vision science, traditional research approaches struggle to accurately simulate and evaluate vision correction methods, leading to time-consuming evaluations with limited scope and flexibility. To overcome these challenges, we introduce ‘VisionaryVR’, a virtual reality (VR) simulation framework designed to enhance optical simulation fidelity and broaden experimental capabilities. VisionaryVR leverages a versatile VR environment to support dynamic vision tasks and integrates comprehensive eye-tracking functionality. Its experiment manager’s scene-loading feature fosters a scalable and flexible research platform. Preliminary validation through an empirical study has demonstrated VisionaryVR’s effectiveness in replicating a wide range of visual impairments and providing a robust platform for evaluating vision correction solutions. Key findings indicate a significant improvement in evaluating vision correction methods and user experience, underscoring VisionaryVR’s potential to transform vision science research by bridging the gap between theoretical concepts and their practical applications. This validation underscores VisionaryVR’s contribution to overcoming traditional methodological limitations and establishing a foundational framework for research innovation in vision science.

Neurological degenerative conditions can affect motor functions, making mobility daunting. Recent configurations of mobility devices that leverage artificial intelligence (AI) show its ability to handle complex information like user input. We create a virtual reality environment to measure participants’ reactions to correct and incorrect feedback from an AI-assistance system. Using gaze to evaluate these reactions, we investigate whether we can automatically predict an upcoming system error. Our results show that gaze reactions occur within 300 ms when the system highlights user input, but the delay extends to 1 second without highlighting. Subject dependent gaze behavior proved complicated for developing a generalizable model based on previous work using TCNs for online recognition of upcoming errors. Therefore, more adaptable models for individuals may be a better alternative for gaze-based accessibility systems.