Abstract

Mental imagery practice is widely used to help athletes prepare for competitions, as it can produce motor actions that enhance performance. The goal of imagery training for athletes is to create realistic images in their minds and to familiarize them with certain procedures, environments, and other aspects related to competition. Traditional imagery training methods use still images or videos, and athletes study the pictures or watch the videos in order to mentally rehearse. However, factors such as distractions and low realism can affect the training quality. In this paper, we present a Virtual Reality (VR) solution and a study that explores our hypotheses that H1: high-fidelity VR systems improve mental imagery skills, that H2: the presence of elements such as virtual onlookers or photographers in the VR environment arouse stronger emotional reactions and affect, and that H3: the presence of elements such as onlookers or photographers in the VR environment results in better mental imagery skill improvement. For that purpose, seven elite snow sports athletes were exposed to three training methods, Video, VR-Empty, and VR-Crowded. Our results show that a VR simulation with virtual onlookers (VR-Crowded) can significantly increase heart rate, which can induce increased emotional arousal. The results from validated questionnaires show no significant difference for the three training methods in terms of mental imagery and affect, but the results show an ascending trend for the athlete’s arousal from Video to the VR-Crowded condition. Gaze detection heat maps of interest areas for the two VR conditions support hypothesis H2 that environmental factors such as the presence of photographers, staff, and onlookers can increase head and eye movement, possibly indicating an increase in emotional arousal during imagery training. According to verbal feedback and interviews, athletes are more likely to use innovative training methods (e.g., the high-fidelity VR method) than traditional video-training methods.

Full Text
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