Abstract—A computationally implemented model of the transmission of linguistic behavior over time is presented. In this model [the iterated learning model (ILM)], there is no biological evolution, natural selection, nor any measurement of the success of the agents at communicating (except for results-gathering purposes). Nevertheless, counter to intuition, significant evolution of linguistic behavior is observed. From an initially unstructured communication system (a protolanguage), a fully compositional syntactic meaning-string mapping emerges. Furthermore, given a nonuniform frequency distribution over a meaning space and a production mechanism that prefers short strings, a realistic distribution of string lengths and patterns of stable irregularity emerges, suggesting that the ILM is a good model for the evolution of some of the fundamental features of human language. Index Terms—Cultural selection, evolution, grammar induction, iterated learning, language. I.

The paper discusses efforts to understand the self-organisation and evolution of language from a cognitive modeling point of view. It focuses in particular on efforts that use connectionist components to synthesise some of the major stages in the emergence of language and possible transitions between stages. The paper does not introduce new technical results but discusses a number of dimensions for mapping out the research landscape. 1 1

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In this paper an experiment is presented in which two mobile robots develop a shared lexicon of which the meanings are grounded in the real world.

Research into the evolution of grammar requires that we employ formalisms and processing mechanisms that are powerful enough to handle features found in human natural languages. But the formalism needs to have some additional properties compared to those used in other linguistics research that are specifically relevant for handling the emergence and progressive co-ordination of grammars in a population of agents. This document introduces Fluid Construction Grammar, a formalism with associated parsing, production, and learning processes designed for language evolution research. The present paper focuses on a formal definition of the unification and merging algorithms used in Fluid Construction Grammar. The complexity and soundness of the algorithms and their relation to unification in logic programming and other unification-based grammar formalisms are discussed.

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.

The paper proposes that grammar emerges in order to reduce the computational complexity of semantic interpretation and discusses some details of simulations based on Fluid Construction Grammars.

Human language is unique among the communication systems of the natural world. The vocabulary of human language is unique in being both culturally-transmitted and symbolic. In this paper I present an investigation into the factors involved in the evolution of such vocabulary systems. I investigate both the cultural evolution of vocabulary systems and the biological evolution of learning rules for vocabulary acquisition.

The recruitment theory of language origins argues that language users recruit and try out different strategies for solving the task of communication and retain those that maximise communicative success and cognitive economy. Each strategy requires specific cognitive neural mechanisms, which in themselves serve a wide range of purposes and therefore may have evolved or could be learned independently of language. The application of a strategy has an impact on the properties of the emergent language and this fixates the use of the strategy in the population. Although neurological evidence can be used to show that certain cognitive neural mechanisms are common to linguistic and non-linguistic tasks, this only shows that recruitment has happened, not why. To show the latter, we need models demonstrating that the recruitment of a particular strategy and hence the mechanisms to carry out this strategy lead to a better communication system. This paper gives concrete examples how such models can be built and shows the kinds of results that can be expected from them.

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 cultural norms, can only be learned from other people, who themselves learned from previous generations. The prevalence of this process of “iterated learning” as a mode of cultural transmission raises the question of how it affects the information being transmitted. Analyses of iterated learning utilizing 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 confirmed this prediction, providing insight into the consequences of intergenerational knowledge transmission and a method for discovering the inductive biases that guide human inferences. Knowledge changes as it is passed from one person to the next and from one generation to the next. Sometimes the change is dramatic: The deaf children of Nicaragua have transformed a fragmentary protolanguage into a real language in the brief time required for one generation of signers to mature within the new language’s community (see, e.g., Senghas & Coppola, 2001). Language is only one example, although it is perhaps the most striking, of the intergenerational transmission of cultural knowledge. In many cases of cultural transmission, one learner serves as the next learner’s teacher. Languages, legends, superstitions, and social norms are all transmitted by such a process of “iterated learning ” (see Figure 1A), with each generation learning from data produced by the one that