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David E. Fencsik, Todd S. Horowitz, Sarah B. Kliege, Jeremy M. Wolf; Target reacquisition strategies in multiple object tracking. Journal of Vision 2004;4(8):370. doi: https://doi.org/10.1167/4.8.370.
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© ARVO (1962-2015); The Authors (2016-present)
Observers in multi-element tracking experiments can successfully track a subset of randomly moving identical objects, even if all of the objects disappear for 300–400 ms (e.g., Alvarez, Wolfe, Horowitz, & Arsenio (VSS 01)). This interval is too long for apparent motion mechanisms to bridge the gap. How does the visual system reacquire the target objects when they reappear? Here we test two possible explanations: According to the location-matching account, the system stores the location of each target at the time of disappearance. After the gap, the object closest to each target's pre-gap location is identified as a target. According to the trajectory-matching account, the system stores the target trajectories at the time of disappearance and searches along each object's predicted trajectory after the gap. In the experiments reported here, observers tracked 5 out of 10 dark gray disks moving randomly on a light gray background for 5000 ms. At a random time during the tracking interval, all the objects disappeared for 300 ms, then reappeared at various distances from their pre-gap locations. In Experiment 1, all of the objects reappeared either at the point of disappearance (position 0) or at the location expected if each had continued moving at a constant velocity during the gap (position 1). Performance was superior at position 0, supporting a location-matching account. In Experiment 2, we added position −1, which was as far behind position 0 along the trajectory as position 1 was ahead. The location-matching account predicts that performance should be best at position 0 and equal for positions −1 and 1. Instead, we found that position −1 produced performance superior to position 1, and not significantly different from position 0. This result indicates that the visual system takes into account the history of an object's motion trajectory when reacquiring targets.
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