The last two decades have seen a great deal of theorising and speculation about the modular nature of human intelligence, as well as a rise in use of modular architectures in artificial intelligence. Nevertheless, whether such models of natural intelligence are well supported is still an issue of debate. In this paper, I propose that the most important criteria for modularity is specialised representations. I present a modular model of primate learning of the transitive inference task, and propose an extension to this model which would explain task-learning results in other domains. I also briefly relate this work to both neuroscience and established AI learning architectures. 1. Introduction — Modularity