  We present a novel Bayesian approach to semiotic dynamics, which is a
cognitive analogue of the naming game model restricted to two conventions. The
one-shot learning that characterizes the agent dynamics in the basic naming
game is replaced by a word-learning process, in which agents learn a new word
by generalizing from the evidence garnered through pairwise-interactions with
other agents. The principle underlying the model is that agents, like humans,
can learn from a few positive examples and that such a process is modeled in a
Bayesian probabilistic framework. We show that the model presents some
analogies but also crucial differences with respect to the dynamics of the
basic two-convention naming game model. The model introduced aims at providing
a starting point for the construction of a general framework for studying the
combined effects of cognitive and social dynamics.
