In a simple referential game with two neural network-based agents, we analyze the object-symbol mapping trying to understand what kind of strategy was
used to develop the emergent language. We see that, when the environment is uniformly distributed, the agents rely on a random subset of features to describe the
objects. When we modify the objects making one feature non-uniformly distributed, the agents realize it is less informative and start to ignore it. Surprisingly, they make a better use of the remaining features. This interesting result suggests that more natural, less uniformly distributed environments might aid in spurring the emergence of better-behaved languages