Language adapting to the brain: a study of a Bayesian iterated learning model
Vanessa Ferdinand, Willem Zuidema

Abstract:
What is the mechanism that translates the individual properties of
learners into the properties of the language they speak?  This paper
will investigate cultural transmission as this mechanism and will take
up the Iterated Learning Model as a formal framework in which to
address this claim.  This model describes language as a special
learning problem, where the output of one generation is the input for
the next. Previous research has shown that universal properties of
human language emerge from the process of cultural transmission.
However, particular biases are also necessary to obtain these
properties, and the exact interplay between individual biases and
cultural transmission is still an open question.  In the present
research, a computational, Bayesian iterated learning model is
constructed to analyze the relationship between learning biases and
what additional structure cultural transmission adds to language.