Weighted combination of size and disparity: a computational model for timing a ball catch

How do we time hand closure to catch a ball? Binocular disparity and optical looming provide two sources of information about an object's motion in depth, but the relative effectiveness of the two cues depends on ball size. Based on results from a virtual reality ball-catching task, we derive a simple model that uses both cues. The model is sensitive to the relative effectiveness of size and disparity and implicitly switches its response to the cue that specifies the earliest arrival and away from a cue that is lost or below threshold. We demonstrate the model's robustness by predicting the response of participants to some very unusual ball trajectories in a virtual reality task.