Abstract
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.
Highlights
One aim of these efforts is the development of valid diagnostics that can, for example, identify the gaze behavior of experts engaged in successful decision-making
We presented gazePatchNet, a model that, based on by-transfer learning, adapted convolutional neural networks (CNNs) for feature extraction and bidirectional long short-term memory networks (BiLSTM) for temporal dependency identification, automating classification in the broadest sense
We recorded the gaze behavior of soccer goalkeepers during build up in a 360◦ video environment on a head-mounted displays (HMD) and used their fixation image patches on the stimuli as input signals to classify three groups of expertise
Summary
Expertise research spans many different areas. Expertise research based on behavioral data has found its way into several fields, i.e., dentistry (Castner et al, 2020), surgery (Eivazi and Bednarik, 2011; Kübler et al, 2015; Hosp et al, 2021b), and sports (Discombe and Cotterill, 2015; Fegatelli et al, 2016; Kredel et al, 2017; Moran et al, 2018; Snegireva et al, 2018; Hosp et al, 2021a). Since the physical strain of the athletes in high-level sports is very high due to training several times a day, enhancing cognitive factors like decision-making without additional physical training is gaining in importance (Appelbaum and Erickson, 2016) For this reason, research efforts to identify the major cognitive factors leading to differences in performance, especially with regard to decision-making in the sports game have increased in recent years. Research efforts to identify the major cognitive factors leading to differences in performance, especially with regard to decision-making in the sports game have increased in recent years One aim of these efforts is the development of valid diagnostics that can, for example, identify the gaze behavior of experts engaged in successful decision-making. By teaching this gaze behavior it may be possible to design training programs that lead to improved decision-making
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
