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This paper presents a computational model of language learning, transmission, and evolution. We contrast two explanations for the observed fit of language universals with language function that are prominent in the linguistics literature, and which appear to rely on very different explanatory mechanisms --- innate constraints on the one hand, and parsing influenced language change on the other. We show using our model that both explanations can be subsumed under one mechanism of differential take up of competing forms in the language community and subsequent evolution of the learning mechanism to efficiently learn regularities in the input. 1 Introduction One of the interesting challenges facing linguistics today is the explanation of the observed constraints on crosslinguistic variation. 1 Traditional linguistic typology (e.g. [14, 9, 16, 10]) as well as generative theories of language acquisition (e.g. [5, 15]) highlight the fact that the languages of the world appear to fall int...

this paper is how can we go about explaining the observed constraints on variation across languages --- in other words, language universals.

Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute a posterior distribution over languages by combining a prior (representing their inductive biases) with the evidence provided by linguistic data. We show that when learners sample languages from this posterior distribution, iterated learning converges to a distribution over languages that is determined entirely by the prior. Under these conditions, iterated learning is a form of Gibbs sampling, a widely-used Markov chain Monte Carlo algorithm. The consequences of iterated learning are more complicated when learners choose the language with maximum posterior probability, being affected by both the prior of the learners and the amount of information transmitted between generations. We show that in this case, iterated learning corresponds to another statistical inference algorithm, a variant of the expectation-maximization (EM) algorithm. These results clarify the role of iterated learning in explanations of linguistic universals and provide a formal connection between constraints on language acquisition and the languages that come to be spoken, suggesting that information transmitted via iterated learning will ultimately come to mirror the minds of the learners.

It is the basic tenet of the functional approach to typology that at least some linguistic universals may be explained by appealing to features of language use. But the mechanics of the mapping between function and distribution are seldom made explicit. In this paper, a theory of linguistic adaptation is set out in which universals are treated as phenomena of the third kind in Keller's (1994) terms, i.e. objects which are the result of human actions but not the goal of their intentions. In this view universals emerge at a global level from the interactions of individuals whose actions locally can be described in terms of functional motivations. Computational models of this process (an invisible hand process) can be set up to see what universals emerge with different functional pressures in place. Using this model, Keenan and Comrie's accessibility hierarchy is shown to emerge when competing functional pressures relating to structural complexity and morphological complexity are taken in...

Similarities between human languages are often taken as evidence of constraints on language learning. However, such similarities could also be the result of descent from a common ancestor. In the framework of iterated learning, language evolution converges to an equilibrium that is independent of its starting point, with the effect of shared ancestry decaying over time. Therefore, the central question is the rate of this convergence, which we formally analyze here. We show that convergence occurs in a number of generations that is O(nlogn) for Bayesian learning of the ranking of n constraints or the values of n binary parameters. We also present simulations confirming this result and indicating how convergence is affected by the entropy of the prior distribution over languages.

This thesis investigates language acquisition and evolution, using the methodologies of Bayesian inference and expression-induction modelling, making specific reference to colour term typology, and syntactic acquisition. In order to test Berlin and Kay's (1969) hypothesis that the typological patterns observed in basic colour term systems are produced by a process of cultural evolution under the influence of universal aspects of human neurophysiology, an expression-induction model was created. Ten artificial people were simulated, each of which was a computational agent. These people could learn colour term denotations by generalizing from examples using Bayesian inference, and the resulting denotations had the prototype properties characteristic of basic colour terms.

this paper we will be examining one aspect of linguistic constraints: the appearance of design. Many attempts at explaining universals have pointed out their fit to the functions of language. Hawkins [17, 18] for example, attempts to explain a whole range of universals relating to word order in terms of the processing load on the human parser. Although this kind of research (known as functionalist explanations in linguistics) is important, we believe they leave the real problem unanswered -- how exactly do functional pressures end up being expressed as cross-linguistic constraints on variation? Another influential strand of research (known as formal or innatist linguistics) treats language universals as the direct consequence of the structure of a domain specific language acquisition device (LAD) [5]. Although this bypasses the problem of how the constraints emerge, it fails to explain why the constraints appear to be designed for the purpose of making language easier to parse, for example.

In 1969, Berlin and Kay proposed that there exist crosscultural universals in the form of basic color terms. To test this hypothesis, the World Color Survey (WCS) collected color naming data from 110 non-industrial societies, identifying regularities in the structure of languages with different numbers of terms. This leaves us with the question of where these universals come from. We use a simple model of cultural evolution known as “iterated learning ” to explore the hypothesis that universals emerge from human perceptual and learning biases. We conducted an experiment simulating the process of cultural transmission in the laboratory, and compared the results to the systems of color terms that appear in the WCS data. Our results show that cultural evolution results in convergence of systems of color terms towards a form consistent with the WCS, supporting the hypothesis that universals are the result of perceptual and learning biases.