Material Detail

Explaining Human Multiple Object Tracking as Resource-Constrained Approximate Inference in a Dynamic Probabilistic Model

Explaining Human Multiple Object Tracking as Resource-Constrained Approximate Inference in a Dynamic Probabilistic Model

This video was recorded at 23rd Annual Conference on Neural Information Processing Systems (NIPS), Vancouver 2009. 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.

Quality

  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Browse...

Disciplines with similar materials as Explaining Human Multiple Object Tracking as Resource-Constrained Approximate Inference in a Dynamic Probabilistic Model

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.