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Laura Thomas, Adriane Seiffert; Self-motion influences multiple-object tracking in a virtual environment. Journal of Vision 2009;9(8):242. doi: 10.1167/9.8.242.
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Investigations of multiple-object tracking aim to further our understanding of how people perform everyday activities such as playing team sports, driving down the interstate, or interacting with others in action video games. However, tracking tasks in the laboratory have largely overlooked a crucial component of real-world multiple-object tracking: self-motion. That is, as we dribble a ball down the basketball court, we must not only keep track of other players, but also plan and execute our own movements. Is our ability to track moving objects affected when we are also moving? In order to investigate the role self-motion plays in multiple-object tracking, we fitted participants with an immersive virtual reality head-mounted display. Participants tracked one to three moving target balls amidst identical distractors in a three-foot square area on the ground. On some trials, as the balls moved, participants walked in an arc from one side of the square area to an adjacent side, translating their viewpoint by ninety degrees. On other trials, participants maintained a constant viewpoint by walking in place as the balls moved. Participants were able to track multiple moving targets at an above-chance level even when they had to simultaneously translate their own viewpoint (tracking accuracy for three targets = 65%; t(12) = 4.93, p [[lt]]0.005). However, when participants changed position, their tracking accuracy suffered relative to when they stayed in the same position (tracking accuracy for three targets = 77%; t(12) = 3.68, p [[lt]]0.005). These results show that there are tracking costs associated with self-motion. Further experiments will determine the relative influence of viewpoint change and observer action. These experiments demonstrate the potential usefulness of virtual reality as a tool for studying the ways in which self-motion influences our ability to track multiple objects.
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