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To accurately time motor responses when intercepting falling balls we rely on an internal model of gravity. However, whether and how such a model is also used to estimate the spatial location of interception is still an open question. Here we addressed this issue by asking 25 participants to intercept balls projected from a fixed location 6 m in front of them and approaching along trajectories with different arrival locations, flight durations, and gravity accelerations (0 g and 1 g ). The trajectories were displayed in an immersive virtual reality system with a wide field of view. Participants intercepted approaching balls with a racket, and they were free to choose the time and place of interception. We found that participants often achieved a better performance with 1 g than 0 g balls. Moreover, the interception points were distributed along the direction of a 1 g path for both 1 g and 0 g balls. In the latter case, interceptions tended to cluster on the upper half of the racket, indicating that participants aimed at a lower position than the actual 0 g path. These results suggest that an internal model of gravity was probably used in predicting the interception locations. However, we found that the difference in performance between 1 g and 0 g balls was modulated by flight duration, the difference being larger for faster balls. In addition, the number of peaks in the hand speed profiles increased with flight duration, suggesting that visual information was used to adjust the motor response, correcting the prediction to some extent. NEW &amp; NOTEWORTHY Here we show that an internal model of gravity plays a key role in predicting where to intercept a fast-moving target. Participants also assumed an accelerated motion when intercepting balls approaching in a virtual environment at constant velocity. We also show that the role of visual information in guiding interceptive movement increases when more time is available.

Intercepting virtual balls approaching under different gravity conditions: evidence for spatial prediction