  A simple model of language evolution, proposed in \cite{K_N}, is
characterized by a pay-off in communicative function, and by an error in
learning, that measures the accuracy in language acquisition. In the mean field
approximation, this model exhibits a critical coherence threshold, i.e. a
minimal accuracy in the learning process is required to maintain linguistic
coherence. In this work, we analyse in detail the effects of different fitness
based dynamics driving linguistic coherence and of the network of interactions
on the nature of the coherence threshold, by performing numerical simulations
and theoretical analyses of generalized replicator-mutator dynamics in
populations with two types of structure: fully connected networks and regular
random graphs. We find that although the threshold of the replicator-mutator
evolutionary model is robust with respect to the structure of the network of
contacts, the coherence threshold of related fitness driven models may be
strongly affected by this feature.
