 The computational and laboratory experiments of Kirby and his collaborators have shown that under certain conditions of iterated learning, any given set of signifier/signified pairs in which the mapping is initially arbitrary will change to exhibit a very general kind of compositional structure. Iterated learning has been studied in both computational and laboratory experiments by means of diffusion chains, i.e., sequences of learners. A primary characteristic of such sequences of transmission is that what is transmitted from learner to learner will change in an iterated learning environment, in a way that depends on the conditions of transmission.