Online Embodied Evolution is a distributed learning method for collective heterogeneous robotic swarms, in which evolution is carried out in a decentralized manner. These algorithms are well suited for open-ended evolution where the goal is to evolve efficient survival strategies in a priori unknown and changing environments. In this work, we are interested in analyzing the features of the environment that favors evolution of certain behaviors. We hypothesize that certain types of perceivable artifacts in the environment provide affordances that exert a selection pressure toward the fixation of certain behaviors. We test this hypothesis in simulation in two different settings an open-ended environment without any selection pressure and compare it to one which originality of behaviors is enforced. The experiments show that in the open-ended environment agents evolved a certain type of behavior that exploited affordances, hinting at the existence of a selection pressure from the affordances as it is claimed from ecological psychology in the case of natural systems. We present different objective and subjective measures to support this claim.