this paper we want to convince you that population thinking, not natural selection, is the key to conceptualizing culture in terms of material causes. This argument is based on three well-established facts: 1. There is persistent cultural variation among human groups. Any explanation of human behavior must account for how this variation arises and how it is maintained. 2. Culture is information stored in human brains. Every human culture contains vast amounts of information. Important components of this information are stored in human brains. 3. Culture is derived. The psychological mechanisms that allow culture to be transmitted arose in the course of hominid evolution. Culture is not simply a byproduct of intelligence and social life. Most culture is information stored in human brains---information that got into those brains by various mechanisms of social learning. It follows that to explain the distribution of information stored in the brains of the members of current generation, any coherent theory will have to account for the cultural information in the brains of the previous generation. The theory will also have to explain how this information, together with genes, and environmental contingencies caused the present generation to acquire the cultural information that it did. Unfortunately, we do not understand how this process works. It may be that cultural information stored in brains takes the form of discrete memes that are replicated faithfully in each subsequent generation, or it may not. This is an empirical question which at present is unanswered, and we will see that other models are possible. In every case, the Darwinian population approach will illuminate the process by which the cultural information that is stored in a population of brains is transfor...

This paper addresses the problem of deriving distance measures between parent and daughter languages with specific relevance to historical Chinese phonology. The diachronic relationship between the languages is modelled as a Probabilistic Fi- nite State Automaton. The Minimum Mes- sage Length principle is then employed to find the complexity of this structure. The idea is that this measure is representative of the amount of dissimilarity between the two languages.

On the wrong track?: a non-standard history of non-standard /au / in English 1

Recent research on computational models of language change and cultural evolution in general has focused on the analytical study of languages as dynamic systems, thus avoiding the difficulties of analysing the complex multi-agent interactions underlying numerical simulations of cultural transmission. The same is true for the examination of the effects of inductive biases on language distributions within the Bayesian Iterated Learning Framework. The aim of this work is to test whether the strong results obtained through analytical methods in this framework also extend to finite populations of Bayesian learners, and to investigate what other effects richer population dynamics have on the results. Small world networks are introduced as a tool to model social structures which are shown to play an important role in the outcome of cultural transmission processes. The assumptions behind a Bayesian approach to language learning and its implications will be studied and compared to previous models of language change. While studying the effects of populations on convergence rates in the Bayesian model, the role of more complex population settings for the future of Iterated