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Samantha Ellner, Jonathan I. Flombaum, Brian J. Scholl; Extrapolation vs. individuation in multiple object tracking. Journal of Vision 2009;9(8):250. doi: 10.1167/9.8.250.
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© ARVO (1962-2015); The Authors (2016-present)
A central task of perception is not only to segment the visual environment into discrete objects, but also to keep track of objects as persisting individuals over time and motion. Object persistence can be studied using multiple object tracking (MOT), in which observers track several featurally identical targets that move haphazardly and unpredictably among identical distractors. How is MOT possible? One intuitive idea is that this ability is mediated in part by a form of automatic trajectory extrapolation. Some previous studies attempted to support this view by demonstrating that subjects are better able to recover targets following a gap — the momentary disappearance of the entire display — when the gap was preceded by a coherent motion trajectory rather than a static array of objects. Such demonstrations are susceptible to a simpler interpretation, though: perhaps the pre-gap motion simply serves to better individuate the objects, rather than supporting trajectory extrapolation. To address this, we studied MOT using four conditions, each involving gaps after which the objects appeared in the same locations across conditions. In the Move condition, objects moved continuously before the gap, such that the final locations were at the ‘correct’ extrapolated locations. In the Static condition, objects remained stationary before the gap. In the Vibrate condition, objects oscillated in place before the gap. And in the Orthogonal condition, objects moved continuously before the gap at a 90° angle from their post-gap positions. Compared to the Static baseline, performance was equal in the Vibrate condition, much better in the Move condition, and significantly worse in the Orthogonal condition. This provides decisive evidence that extrapolation occurs during MOT, and perhaps cannot even be ignored — since unreliable trajectories yielded worse performance than no trajectories at all. These and other conditions begin to elucidate the underlying processes that effectively ‘implement’ MOT.
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