Before revision:  Our comparison of these two models demonstrates thatit is advantageous to include information about the persistence of features as the camera translates (e.g. distant features persist) . It is likely that non-Cartesian representations of this sort will be increasingly important in the search for robust models of human spatial perception and navigation .
After revision:  Our comparison of these two models demonstrates thatit is advantageous to include information about the persistence of features as the camera translates (e.g. distant features persist) can improve performance on the geometric tasks. These examples avoid Cartesian (in this case, 2D) representations of space. Non-Cartesian, learned representations provide an important stimulus in neuroscience to the search for robust models of human spatial perception and navigation .