Language Evolution and Computation Bibliography

This chapter gives an overview of language variation and how the dynamics of language are explored through formal models. It briefly outlines the dimensions over which language structures can vary, then looks at some of the very different ways in which language change has been investigated (sociolinguistics, historical linguistics, evolutionary linguistics). It describes how dynamic models of change have been successfully used in all of these fields, and how they have shed light on many aspects of language dynamics, from the properties of language change through phylogenetic analyses of language history to computational and experimental models of cultural evolution.

In this study, we tested the circumstances under which cultural evolution might lead to regularisation, even in the absence of an explicit learning bottleneck. We used an artificial language experiment to evaluate the degree of structure preservation and the extent of a bias for regularisation during learning, using languages which differed both in their initial levels of regularity and their frequency distributions. The differential reproduction of regular and irregular linguistic items, which may signal the existence of a systematicity bias, is apparent only in languages with skewed distributions: in uniformly distributed languages, reproduction fidelity is high in all cases. Regularisation does happen despite the lack of an explicit bottleneck, and is most significant in infrequent items from an otherwise highly regular language.

Traditionally, grammaticalisation has been described as being based on phenomena specific to language such as metaphorical extension or reanalysis. This characterisation is somewhat in contrast to claims that grammaticalisation is involved in the much more general process of the initial emergence of language. In this article, we provide a unified analysis of both the metaphor-based and the reanalysis-based account of grammaticalisation which is grounded in the cognitive mechanisms underlying ostensive-inferential communication. We are thus able to show that the process of grammaticalisation is an instantiation of a domain-general pre-linguistic phenomenon.

One important difference between existing accounts of protolanguage lies in their assumptions on the semantic complexity of protolinguistic utterances. I bring evidence about the nature of linguistic communication to bear on the plausibility of these assumptions, and show that communication is fundamentally inferential and characterised by semantic uncertainty. This not only allows individuals to maintain variation in linguistic representation, but also imposes a selection pressure that meanings be reconstructible from context. I argue that protolanguage utterances had varying degrees of semantic complexity, and developed into complex language gradually, through the same processes of re-analysis and analogy which still underpin continual change in modern languages.

We argue that studying grammaticalisation is useful to evolutionary linguists, if we abstract away from linguistic description to the underlying cognitive mechanisms. We set out a unified approach to grammaticalisation that allows us to identify these mechanisms, and argue that they could indeed be sufficient for the initial emergence of linguistic signal-meaning associations.

We agree that language adapts to the brain, but we note that language also has to adapt to brain-external constraints, such as those arising from properties of the cultural transmission medium. The hypothesis that Christiansen & Chater (C&C) raise in the target article not only has profound consequences for our understanding of language, but also for our understanding of the biological evolution of the language faculty.

We combine information theory and cross-situational learning to develop a novel metric for quantifying the degree of regularity in the mappings between signals and meanings that can be inferred from exposure to language in context. We illustrate this metric using the results of two artificial language learning experiments, which show that learners are sensitive, with a high level of individual variation, to systematic regularities in the input. Analysing language using this measure of regularity allows us to explore in detail how language learning and language use can both generate linguistic variation, leading to language change, and potentially complexify language structure, leading to qualitative language evolution.

Much of the current debate about the development of modern language from protolanguage focuses on whether the process was primarily synthetic or analytic. I investigate attested mech- anisms of language change and emphasise the uncertainty inherent in the inferential nature of communication. Both synthesis and analysis are involved in the complexification of language, but the most significant pressure is the need for meanings to be reconstructible from context.

We present a mathematical model of cross-situational learning, in which we quantify the learnability of words and vocabularies. We find that high levels of uncertainty are not an impediment to learning single words or whole vocabulary systems, as long as the level of uncertainty is somewhat lower than the total number of meanings in the system. We further note that even large vocabularies are learnable through cross-situational learning.

Language is a symbolic, culturally transmitted system of communication, which is learnt through the inference of meaning. In this paper, I describe the importance of meaning inference, not only in language acquisition, but also in developing a unified explanation for language change and evolution. Using an agent-based computational model of meaning creation and communication, I show how the meanings of words can be inferred through disambiguation across multiple contexts, using cross-situational statistical learning. I demonstrate that the uncertainty inherent in the process of meaning inference, moreover, leads to stable variation in both conceptual and lexical structure, providing evidence which helps to explain how language changes rapidly without losing communicability. Finally, I describe how an inferential model of communication may provide important theoretical insights into plausible explanations of the bootstrapping of, and the subsequent progressive complexification of, cultural communication systems.

In this paper, a computational model of a successful negotiated com- munication system is presented, in which language agents develop their own meanings in response to their environment and attempt to infer the meanings of others' utterances. The inherent uncertainty in the process of meaning inference in the system leads to variation in the agents' internal semantic representations, which then itself drives language change in the form of semantic generalisation.

In this thesis, I address the problem of how successful communication systems can emerge between agents who do not have innate or explicitly transferable meanings, cannot read the minds of their interlocutors, and are not provided with any feedback about the communication process. I develop a solution by focusing on the role of meanings within the framework of language evolution, and on communication through the repeated inference of meaning.

Much recent work on the evolution of language has concentrated on the emergence of compositional syntax as the crucial event which marked the genesis of language; all the experimental models which purport to demonstrate the emergence of syntax, however, rely on models of communication in which the signals are redundant and which contain pre-defined, structured meaning systems which provide an explicit blueprint against which the syntactic structure is built. Moreover, the vast majority of such meaning systems are truly semantic in name only, lacking even the basic semantic characteristics of sense and reference, and the agents must rely on mind-reading or feedback (or both) in order to learn how to communicate.

By contrast, at the heart of this thesis is a solution to the signal redundancy paradox based on the inference of meaning and the disambiguation of potential referents through exposure in multiple contexts. I describe computational models of meaning creation in which agents independently develop individual conceptual structures based on their own experiences of the environment, and show through experimental simulations that the agents can use their own individual meanings to communicate with each other about items in their environment. I demonstrate that the development of successful communication depends to a large extent on the synchronisation of the agents' conceptual structures, and that such synchronisation is significantly more likely to occur when the agents use an intelligent meaning creation strategy which can exploit the structure in the information in the environment.

Motivated by research into the acquisition of language by children, I go on to explore how the introduction of specific cognitive and lexical biases affects the level of communicative success. I show that if the agents are guided by an assumption of mutual exclusivity in word meanings, they do not need to have such high levels of meaning similarity, and can instead communicate successfully despite having very divergent conceptual structures.

This paper investigates the problem of how language learners decipher what words mean. In most models of language evolution, agents are provided with meanings {\em a priori} and explicitly transfer them to each other as part of the communication process. By contrast, we investigate how successful communication systems can emerge without innate or transferable meanings, and show that this is dependent on the agents developing highly synchronised conceptual systems. We experiment with various cognitive, communicative and environmental factors which have an impact on the likelihood of agents achieving meaning synchronisation. We show that an intelligent meaning creation strategy in a clumpy world leads to the highest level of meaning similarity between agents.

This paper investigates the development of experience-based meaning creation and explores the problem of establishing successful communication systems in a population of agents. The aim of the work is to investigate how such systems can develop, without reliance on phe- nomena not found in actual human language learning, such as the explicit transmission of meaning or the provision of reliable error feedback to guide learning. Agents develop individual, distinct meaning structures, and although they can communicate despite this, communicative success is closely related to the proportion of shared lexicalised meaning, and the communicative systems have a large degree of redundant synonymy.