The computational theory of mind is arguably one of the greatest advances of the 20th century as well as one of the greatest challenges for the 21st century -- underscoring Alan Turing’s lasting legacy 100 years after his birth. Although there is general consensus that the mind/brain carries out computations, it remains far less clear how these operations relate to the computers we now construct: what is hard or easy for people to compute does not readily map over to what is hard or easy for the machines we build. People remain better than our computers at recognizing faces, speech, and text -- hence the current interest in “crowd sourcing.” Computers hold the edge at the casino, in working with complex probabilities coupled with elephantine memory. This workshop will explore the connections between computational complexity as it relates to cognitive abilities such as vision, language, path-following, music, and mathematics. How does the traditional notion of “efficient computation” extend into those domains? How can we best bridge the gap between our understanding of neural circuitry and these higher-level cognitive abilities? Are there basic principles and natural laws that apply to both kinds of computations? Current results on the notion of computational efficiency across cognitive sciences may lead to novel perspectives on the effectiveness of the computation of mind.