Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model

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Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model

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Title: Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model
Author: Vul, Edward; Frank, Michael C.; Tenenbaum, Joshua B.; Alvarez, George Angelo

Note: Order does not necessarily reflect citation order of authors.

Citation: Vul, Edward, Michael C. Frank, Joshua B. Tenenbaum, and George Alvarez. 2009. "Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model." Advances in Neural Information Processing Systems 22: 1955-1963.
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Abstract: Multiple object tracking is a task commonly used to investigate the architecture of human visual attention. Human participants show a distinctive pattern of suc- cesses and failures in tracking experiments that is often attributed to limits on an object system, a tracking module, or other specialized cognitive structures. Here we use a computational analysis of the task of object tracking to ask which human failures arise from cognitive limitations and which are consequences of inevitable perceptual uncertainty in the tracking task. We find that many human perfor- mance phenomena, measured through novel behavioral experiments, are naturally produced by the operation of our ideal observer model (a Rao-Blackwelized par- ticle filter). The tradeoff between the speed and number of objects being tracked, however, can only arise from the allocation of a flexible cognitive resource, which can be formalized as either memory or attention.
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:12410514
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