Abstract
This study explores visuomotor control in athletes for collision avoidance using virtual reality. Thirty-nine athletes navigated dynamic scenarios, pursuing a virtual target while avoiding two to five virtual defenders. Confirmatory Factor Analysis validated a model that captured two features of scanning behavior based on head movements recorded during activity: the overall amount and temporal pattern. Linear mixed models showed that these features significantly differentiated successful from unsuccessful defender avoidance (p < .05), suggesting that efficient environmental scanning is crucial for collision avoidance while highlighting the potential of visuomotor interventions to reduce collision-related sport injuries.
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