Statistical decision theory and the allocation of cognitive resources in multiple object tracking

Abstract

Event Abstract Back to Event Statistical decision theory and the allocation of cognitive resources in multiple object tracking Multiple object tracking (MOT) is a common task used to investigate the limitations on human visual attention. However, the nature of these limitations is still unclear. In the current study, we define an ideal observer for the multiple object tracking task and test hypotheses about human limitations based on the correspondence between human performance and the observer's performance. Because the agent optimally allocates its resources, this approach allows us to estimate what resources limit human performance in MOT without making additional heuristic assumptions. MOT is computationally identical to an 'aircraft tracking' problem. Our ideal observer model observes the initial locations and labels of some number of objects, and then obtains unlabeled measurements for a series of time-steps. Its goal is to solve the correspondence problem at each time-step, determining which object label generated which observation. We implemented an online solution to this problem using a Rao-Blackwellized particle filter. Unmodified, this ideal observer was able to solve MOT tasks substantially better than humans, but it still showed many commonly observed phenomena in human MOT. For example, tracking performance suffered as object speed increased, as the number of objects in the display increased, and as the unpredictability of the object trajectories increased. However, the unmodified model did not match another phenomenon: the tradeoff between the number of targets that human observers can track and the speed of the targets. Fitting this phenomenon requires that some limit on the MOT system be allocated flexibly. We tested three potential limits on our optimal algorithm (based loosely on the literature on human MOT): 1)Limited computation. Perhaps a central processing bottleneck dictates that only some number of object state estimates may be updated within a certain amount of time. A limit on the computational resources necessary for inference in the model (e.g. by limiting the number of particles available in the particle filter) is analogous to this type of limit on state updates. 2)Limited measurement fidelity. 'Attention' is hypothesized to improve the precision of observation. Thus, a limit on measurement fidelity is analogous to hypothesizing a fixed amount of 'attention' which may be distributed across the visual field. 3)Limited memory fidelity. Most attention tasks also require some component of working memory storage. Thus, limiting the precision with which state estimates propagate through time amounts to imposing a storage limit in working memory. Our results suggest that limiting measurement fidelity or memory fidelity (but not limiting computation) produced a pattern of performance in our ideal observer corresponding to the speed/number of objects tradeoff. However, the potential increase in measurement precision due to 'attention' required to match human data is far larger than that observed in human experiments. Thus, only limitations on memory fidelity may be considered a viable candidate for the restriction on human performance. We conclude that human MOT performance is limited by the fidelity with which state estimates are stored and propagated through time: working memory. Time permitting, we will also discuss new evidence reflecting the flexibility of human resource allocation. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Oral Presentation Topic: Oral Presentations Citation: (2009). Statistical decision theory and the allocation of cognitive resources in multiple object tracking. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.232 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 03 Feb 2009; Published Online: 03 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.