Evolutionary robotics (ER) have successfully built robot controllers presenting a reactive behavior. However, the evolution of cognitive controllers is still a challenge. We hypothesize here that a fitness function which rewards the fulfillment of a task requiring cognitive abilities does not necessarily reward the stepping stones that lead to cognitive controllers. In other words, our hypothesis is that evolving cognitive abilities is a deceptive roblem; so that the selective pressures driving the evolutionary search are of critical importance. This paper presents some experiments to confirm this hypothesis and addresses this selective pressure problem by introducing a new helper-objective that rewards controllers with a memory. This is potentially useful for the design of controllers in which an internal representation of some data is required to solve a task. It does not assume how the memory is stored in the controller, therefore reducing the bias towards a particular solution. The new objective is tested in a multi-objective scheme on a T-maze ER task — a task involving both navigation and working memory. The efficiency of the helperobjective is studied, as well as its effects on the overall performance and generalization ability of the controller.