  Speakers' referential expressions often depart from communicative ideals in
ways that help illuminate the nature of pragmatic language use. Patterns of
overmodification, in which a speaker uses a modifier that is redundant given
their communicative goal, have proven especially informative in this regard. It
seems likely that these patterns are shaped by the environment a speaker is
exposed to in complex ways. Unfortunately, systematically manipulating these
factors during human language acquisition is impossible. In this paper, we
propose to address this limitation by adopting neural networks (NN) as learning
agents. By systematically varying the environments in which these agents are
trained, while keeping the NN architecture constant, we show that
overmodification is more likely with environmental features that are infrequent
or salient. We show that these findings emerge naturally in the context of a
probabilistic model of pragmatic communication.
