One of the least understood issues in language evolution is how hominins were able to
ground and establish a shared lexicon. Recently, researchers have explored this issue using
a variety of computational models, whose results have suggested that a shared lexicon
could have emerged spontaneously through a process of self-organisation. However,
these models have used psychologically unrecognised concept representations and an
oversimplified environment. In this dissertation, I present a new computational model
in an attempt to address these problems. Agents' category representations are inspired
by prototype theory, having central members and graded membership. The environment
consists of an infinite number of objects, and has a probabilistic structure which
can be easily manipulated through model parameters. Despite the relatively complex
model, simulation results are generally in line with previous ones and add further support
to the self-organisation hypothesis. In addition, the speed and level of lexical convergence
depend on the world structure, confirming that this is an aspect of past models
which has seen too little attention. Future work should investigate the vast parameter
space in further detail, and extend the simulations in various new directions
