Language adapting to the brain: a study of a Bayesian iterated learning model

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.