Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model
Name
3828-explaining-human-multiple-object-tracking-as-resource-constrained-approximate-inference-in-a-dynamic-probabilistic-model.pdf
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602.75 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Alvarez, George
Vul, Edward
Frank, Michael C.
Tenenbaum, Joshua B
Date Issued
2009
Journal
Advances in Neural Information Processing Systems (NIPS)
Publisher
Neural Information Processing Systems Foundation
Citation
Vul, Edward et al. "Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model." Advances in Neural Information Processing Systems (NIPS) (2009) © 2009 Neural Information Processing Systems Foundation
Version
Final published version
Abstract
Multiple object tracking is a task commonly used to investigate the architecture of human visual attention. Human participants show a distinctive pattern of successes 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 performance phenomena, measured through novel behavioral experiments, are naturally produced by the operation of our ideal observer model (a Rao-Blackwelized particle 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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://papers.nips.cc/paper/3828-explaining-human-multiple-object-tracking-as-resource-constrained-approximate-inference-in-a-dynamic-probabilistic-model
