The human brain has distinct and highly specialized functional regions to understand languages, recognize faces and plan ahead. However, neuroscientists must still decipher the high degree of functional specialization observed in the cortex. In a new study now published in Science Advances, Katharina Dobs and a team of scientists at the department of brain and cognitive sciences, MIT and the Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, U.S., investigated face perception with artificial neural networks to test the hypothesis that functional segregation of facial recognition in the brain reflected computational optimization for broader applications of visual facial recognition. The team showed how functional visual segregation revealed a widespread tendency for optimization to create functional specialization in machines, and also further investigate the complexity of the phenomenon relative to brains.