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A simple model of language evolution, proposed in \cite{K_N}, ischaracterized by a pay-off in communicative function, and by an error inlearning, that measures the accuracy in language acquisition. In the mean fieldapproximation, this model exhibits a critical coherence threshold, i.e. aminimal accuracy in the learning process is required to maintain linguisticcoherence. In this work, we analyse in detail the effects of different fitnessbased dynamics driving linguistic coherence and of the network of interactionson the nature of the coherence threshold, by performing numerical simulationsand theoretical analyses of generalized replicator-mutator dynamics inpopulations with two types of structure: fully connected networks and regularrandom graphs. We find that although the threshold of the replicator-mutatorevolutionary model is robust with respect to the structure of the network ofcontacts, the coherence threshold of related fitness driven models may bestrongly affected by this feature.

Coherence thresholds in models of language change and evolution: the effects of noise, dynamics and network of interactions