Representation has a fundamental role in Artificial Intelligence but there is still an open debate on basic issues on this subject. Particularly, there have been various studies on the emergence of communication and language in artificial agents, where the debate on representations underlying these processes should be significant, however not much discussion and studies have been done. We propose to identify and classify possible representational processes occurring during the emergence of communication, replicating a computational experiment previously proposed and evaluating neural network activations patterns. To define representation and its classes, including icons, indexes and symbols, we rely on the semiotics of Charles Sanders Peirce. Results show that symbolic associations are established during the evolution of artificial agents and such symbolic associations benefit adaptive success.