  Human communication systems, such as language, evolve culturally; their
components undergo reproduction and variation. However, a role for selection in
cultural evolutionary dynamics is less clear. Often neutral evolution (also
known as 'drift') models, are used to explain the evolution of human
communication systems, and cultural evolution more generally. Under this
account, cultural change is unbiased: for instance, vocabulary, baby names and
pottery designs have been found to spread through random copying.
  While drift is the null hypothesis for models of cultural evolution it does
not always adequately explain empirical results. Alternative models include
cultural selection, which assumes variant adoption is biased. Theoretical
models of human communication argue that during conversation interlocutors are
biased to adopt the same labels and other aspects of linguistic representation
(including prosody and syntax). This basic alignment mechanism has been
extended by computer simulation to account for the emergence of linguistic
conventions. When agents are biased to match the linguistic behavior of their
interlocutor, a single variant can propagate across an entire population of
interacting computer agents. This behavior-matching account operates at the
level of the individual. We call it the Conformity-biased model. Under a
different selection account, called content-biased selection, functional
selection or replicator selection, variant adoption depends upon the intrinsic
value of the particular variant (e.g., ease of learning or use). This second
alternative account operates at the level of the cultural variant. Following
Boyd and Richerson we call it the Content-biased model. The present paper tests
the drift model and the two biased selection models' ability to explain the
spread of communicative signal variants in an experimental micro-society.
