Ed Vul

Associate Professor

I work at the intersection of the computational and algorithmic descriptions of human cognition, to reconcile models of cognition as statistically optimal computations with the resource limitations that cognitive psychology has documented. Specifically, I focus on two questions: How can we approximate optimal statistical computations despite our limited cognitive resources? And, do we use our limited resources optimally?
  • Griffiths TL., Vul E. & Sanborn AN. (2012) Bridging Levels of Analysis for Probabilistic Models of Cognition, Current Directions in Psychological Science, 21(4), 263-268
  • Teglas E., Vul E., Girotto V., Gonzalez M., Tenenbaum JB. & Bonatti LL. (2011) Pure reasoning in 12-month-olds as probabilistic inference., Science, 332(6033), 1054-1059
  • Vul E. & Rich A. (2010) Independent sampling of features enables conscious perception of bound objects, Psychological Science
  • Vul E., Frank M.C., Alvarez G.A. & Tenenbaum J.B. (2010) Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model, Advances in Neural Information Processing Systems 22
  • Vul E. & Pashler H. (2008) Measuring the crowd within: Probabilistic representations within individuals, Psychological Science, 19(7), 645-647
  • Walker D. & Vul E. (2013) Hierarchical Encoding Makes Individuals in a Group Seem More Attractive , Psychological Science
  • Vul E., Goodman N., Griffiths T.L. & Tenenbaum J.B. (2014) One and Done? Optimal decisions from very few samples, Cognitive Science, 38(4), 599-637

Updated Jan 2015