Language Evolution and Computation Bibliography

A distinctive feature of all human languages is the diverse and arbitrary nature of the sign (signifier). This can be interpreted as stating that the mapping between signals and referents is established by convention rather than by functional constraints. This property of the sign provides for a great deal of linguistic flexibility and is a key component of symbolic communication. Game theoretic models to describe signal imitation are investigated with a view to understanding how non-arbitrary (indexical) animal-style signals might 'evolve' culturally into diverse, arbitrary signs. I explore the evolutionary hypothesis that private, arbitrary signs emerge as a result of selective imitation within a socially structured population. Once arbitrary signs have emerged, they contribute towards greater assortative interactions among individuals using a shared sign system. In natural populations, the models for imitation will very often be close kin. Hence, kinship provides one mechanism for the creation of true symbols. An imitation-structured population can support many more sign systems than an equivalent non-structured population and is one in which symbols become the dominant force in assortative interactions.

Evolutionary models of communication are used to shed some light on the selective pressures involved in the evolution of simple referential signals, and the constraints hindering the emergence of signs. Error-prone communication results from errors in transmission (in which individuals learn the wrong associations) and communication (in which signs are mistaken for one another). We demonstrate how both classes of errors are required to generate diversity and subsequently impose limits on the sign repertoire within a population. We then explore the influence of geographic structuring of a population on the evolution of a shared sign system and the importance of such structure for the maintenance of sign diversity. Deceit tends to erode conventional signs systems thereby reducing signal diversity, we demonstrate that population structure can act as a hedge against deceit, thereby ensuring the persistence of sign systems.

On the evolutionary trajectory that led to human language there must have been a transition from a fairly limited to an essentially unlimited communication system. The structure of modern human languages reveals at least two steps that are required for such a transition: in all languages (i) a small number of phonemes are used to generate a large number of words; and (ii) a large number of words are used to a produce an unlimited number of sentences. The first (and simpler) step is the topic of the current paper. We study the evolution of communication in the presence of errors and show that this limits the number of objects (or concepts) that can be described by a simple communication system. The evolutionary optimum is achieved by using only a small number of signals to describe a few valuable concepts. Adding more signals does not increase the fitness of a language. This represents an error limit for the evolution of communication. We show that this error limit can be overcome by combining signals (phonemes) into words. The transition from an analogue to a digital system was a necessary step toward the evolution of human language.

The emergence of language was a defining moment in the evolution of modern humans. It was an innovation that changed radically the character of human society. Here, we provide an approach to language evolution based on evolutionary game theory. We explore the ways in which protolanguages can evolve in a nonlinguistic society and how specific signals can become associated with specific objects. We assume that early in the evolution of language, errors in signaling and perception would be common. We model the probability of misunderstanding a signal and show that this limits the number of objects that can be described by a protolanguage. This 'error limit' is not overcome by employing more sounds but by combining a small set of more easily distinguishable sounds into words. The process of 'word formation' enables a language to encode an essentially unlimited number of objects. Next, we analyze how words can be combined into sentences and specify the conditions for the evolution of very simple grammatical rules. We argue that grammar originated as a simplified rule system that evolved by natural selection to reduce mistakes in communication. Our theory provides a systematic approach for thinking about the origin and evolution of human language.

We explore how evolutionary game dynamics have to be modified to accomodate a mathematical framework for the evolution of language. In particular, we are interested in the evolution of vocabulary, that is associations between signals and objects. We assume that successful communication contributes to biological fitness: individuals who communicate well leave more offspring. Children inherit from their parents a strategy for language learning (a language acquisition device). We consider three mechanisms whereby language is passed from one generation to the next: (i) parental learning: children learn the language of their parents; (ii) role model learning: children learn the language of individuals with a high payoff; and (iii) random learning: children learn the language of randomly chosen individuals. We show that parental and role model learning outperform random learning. Then we introduce mistakes in language learning and study how this process changes language over time. Mistakes increase the overall efficacy of parental and role model learning: in a world with errors evolutionary adaptation is more efficient. Our model also provides a simple explanation why homonomy is common while synonymy is rare.

Conflicts of interest arise between signaller and receiver in most kinds of biological communication. Some authors have argued that this conflict is likely to give rise to deceit and exploitation, as receivers lag behind in the coevolutionary 'arms race' with signallers. Others have argued that such manipulation is likely to be short-lived and that receivers can avoid being deceived by paying attention to signals that are costly and hence 'unfakeable.' These two views have been hard to reconcile. Here, we present results from simulations of signal evolution using artificial neural networks, which demonstrate that honesty can coexist with a degree of exploitation. Signal cost ensures that receivers are able to obtain some honest information, but is unable to prevent exploitative signalling strategies from gaining short-term benefits. Although any one receiver bias that is open to exploitation will subsist for only a short period of time once signallers begin to take advantage of it, new preferences of this kind are constantly regenerated through selection and random drift. Hidden preferences and sensory exploitation are thus likely to have an enduring influence on the evolution of honest, costly signals. At the same time, honesty and cost are prerequisites for the evolution of exploitation. When signalling is cost-free, selection cannot act to maintain honesty, and receivers rapidly evolve to ignore signals. This leads to a reduction in the extent of hidden preference, and a consequent loss of potential for exploitation.

Models of animal signalling stress that among unrelated individuals the transfer of honest information normally requires that signals are costly, and costly in a way related to the true information revealed by the signal. In the absence of such a cost, `cheats', that lie about their states or needs, are able to evolve and exploit the preferences of receivers. We show here that spatial constraints imposed on the interactions between signallers and receivers favour honest signalling even in the absence of any costs: `islands' of honesty coexist in `seas' of dishonesty. The extent to which honest or dishonest strategies are favoured, is shown to depend upon the relative payoffs from signalling and receiving. As the receiving component of fitness becomes greater than the signalling component of fitness, as might be true in `life-dinner' type interactions, honesty is increasingly favoured. In addition, in spatial populations, honesty can be favoured locally even when the mean global payoffs to honesty are lower than the mean payoffs to dishonesty. Our model provides a general framework for analysing signals in spatially structured populations and might therefore apply to signalling in both natural and cultural situations.