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Edward Vul works at the intersection between the computational and algorithmic descriptions of human cognition, to reconcile models of human behavior as statistically optimal computations with the findings of cognitive psychology. Basically: 1. How can we approximate optimal statistical computations despite our limited cognitive resources? And 2. If we are close to statistically optimal, how would we allocate our limited resources? And what insights do we gain about when, and why, we fail in particular tasks? He will be joining the UCSD Department of Psychology in July, 2010.
Explaining Human Multiple Object Tracking as Resource-Constrained Approximate Inference in a Dynamic Probabilistic Model
as author at Conference Sessions,