September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Object size affects multiple object tracking performance (but not via frequency of close encounters)
Author Affiliations
  • Shanmukha Upadhyayula
    Department of Psychological & Brain Sciences, Johns Hopkins University
  • Jonathan Flombaum
    Department of Psychological & Brain Sciences, Johns Hopkins University
Journal of Vision September 2018, Vol.18, 1020. doi:10.1167/18.10.1020
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      Shanmukha Upadhyayula, Jonathan Flombaum; Object size affects multiple object tracking performance (but not via frequency of close encounters). Journal of Vision 2018;18(10):1020. doi: 10.1167/18.10.1020.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Multiple object tracking (MOT) is a useful paradigm for understanding the causes of performance limits in visual cognition. Among the several debates about the causes of performance limits in the task, one point of consensus is that the frequency with which objects approach close to one another —close encounters— is a primary factor. A corollary implication is that display factors which increase encounter frequency should impair performance, as has been demonstrated, for example, in the cases of speed and trial duration. A feature that should have a similar effect is object size: in a fixed space larger objects should be near one another more frequently. Surprisingly object size has never been investigated as a factor that interacts with performance. We therefore manipulated object size in our experiments. Participants performed a standard MOT task: track and later identify targets among a set of featurally indistinguishable nontargets. In the experiments, we varied the sizes of the discs tracked, within participant, as well as speed and tracking load. But we observed that smaller objects were actually (and significantly) more difficult to track than larger ones. As a sanity check, we computed the frequency of close encounters as a function of size, finding that bigger items were indeed more likely to collide with one another. These results are important because they undermine the assumption that objects are tracked as though they are single points, an assumption that has been implemented in computational models of the task (including our own). We therefore propose an updated model, which incorporates object size in the uncertainty associated with tracking object positions.

Meeting abstract presented at VSS 2018

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