Cultural transmission of information plays a central role in shaping human knowledge. Some of the most complex knowledge that people acquire, such as languages or religious concepts, can only be learned from other people, who themselves learned from previous generations. The prevalance of this process of "iterated learning" as a mode of cultural transmission raises an important question: What are the consequences of iterated learning for the information being transmitted? Analyses of iterated learning under the assumption that the learners are Bayesian agents predict that this process should converge to an equilibrium that reflects the inductive biases of the learners. An experiment in iterated function learning with human participants confirms this prediction, providing insight into the consequences of cultural transmission of information and a method for discovering the prior knowledge that guides human inferences.