Language Evolution and Computation Bibliography

As in biological evolution, multiple forces are involved in cultural evolution. One force is analogous to selection, and acts on differences in the fitness of aspects of culture by influencing who people choose to learn from. Another force is analogous to mutation, and influences how culture changes over time owing to errors in learning and the effects of cognitive biases. Which of these forces need to be appealed to in explaining any particular aspect of human cultures is an open question. We present a study that explores this question empirically, examining the role that the cognitive biases that influence cultural transmission might play in universals of colour naming. In a large-scale laboratory experiment, participants were shown labelled examples from novel artificial systems of colour terms and were asked to classify other colours on the basis of those examples. The responses of each participant were used to generate the examples seen by subsequent participants. By simulating cultural transmission in the laboratory, we were able to isolate a single evolutionary force—the effects of cognitive biases, analogous to mutation—and examine its consequences. Our results show that this process produces convergence towards systems of colour terms similar to those seen across human languages, providing support for the conclusion that the effects of cognitive biases, brought out through cultural transmission, can account for universals in colour naming.

There is an ongoing debate about whether the words in the first languages spoken by humans expressed single concepts or complex holophrases. A computer model was used to investigate the nature of the protolanguages that would arise if speakers could associate words and meanings, but lacked any productive ability beyond saying the word whose past uses most closely matched the meaning that they wished to express. It was found that both words expressing single concepts, and holophrastic words could arise, depending on the conceptual and articulatory abilities of the agents. However, most words were of an intermediate type, as they expressed more than a single concept but less than a holophrase. The model therefore demonstrates that protolanguages may have been of types that are not usually considered in the debate over the nature of the first human languages.

Human language arises from biological evolution, individual learning, and cultural transmission, but the interaction of these three processes has not been widely studied. We set out a formal framework for analyzing cultural transmission, which allows us to investigate how innate learning biases are related to universal properties of language. We show that cultural transmission can magnify weak biases into strong linguistic universals, undermining one of the arguments for strong innate constraints on language learning. As a consequence, the strength of innate biases can be shielded from natural selection, allowing these genes to drift. Furthermore, even when there is no natural selection, cultural transmission can produce apparent adaptations. Cultural transmission thus provides an alternative to traditional nativist and adaptationist explanations for the properties of human languages.

Is the range of languages we observe today explainable in terms of which languages can be learned easily and which cannot? If so, the key to understanding language is to understand innate learning biases, and the process of biological evolution through which they have evolved. Using mathematical and computer modelling, we show how a very small bias towards regularity can be accentuated by the process of cultural transmission in which language is passed from generation to generation, resulting in languages that are overwhelmingly regular. Cultural evolution therefore plays as big a role as prior bias in determining the form of emergent languages, showing that language can only be explained in terms of the interaction of biological evolution, individual development, and cultural transmission.

The effect of adding noise to an expression-induction model of language evolution was investigated. The model consisted of a number of artificial people who were able to infer the denotation of basic colour terms from examples of colours which the words had been used to identify, using a Bayesian inference procedure. The artificial people would express colours to one-another, so producing data from which other people could learn. Occasionally they would be creative, which allowed new words to enter the language. When certain points in the colour space were made especially salient, so that the artificial people were more likely to remember colours at these points, the languages emerging over a number of generations in evolutionary simulations replicated the typological patterns seen in the 110 languages of the world colour survey. It was found that if random noise was added to the data from which the artificial people learned, this had no major effect on the emergent languages, demonstrating that the Bayesian inference procedure is able to learn effectively despite the presence of random noise, even when placed in an evolutionary context.

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. Conversations between these people, in which they learned from one-another, were simulated over several generations, and the languages emerging at the end of each simulation were investigated. The proportion of colour terms of each type correlated closely with the equivalent frequencies found in the World Colour Survey, and most of the emergent languages could be placed on one of the evolutionary trajectories proposed by Kay and Maffi (1999). The simulation therefore demonstrates how typological patterns can emerge as a result of learning biases acting over a period of time.

Further work applied the minimum description length form of Bayesian inference to modelling syntactic acquisition. The particular problem investigated was the acquisition of the dative alternation in English. This alternation presents a learnability paradox, because only some verbs alternate, but children typically do not receive reliable evidence indicating which verbs do not participate in the alternation (Pinker, 1989). The model presented in this thesis took note of the frequency with which each verb occurred in each subcategorization, and so was able to infer which subcategorizations were conspicuously absent, and so presumably ungrammatical. Crucially, it also incorporated a measure of grammar complexity, and a preference for simpler grammars, so that more general grammars would be learned unless there was sufficient evidence to support the incorporation of some restriction. The model was able to learn the correct subcategorizations for both alternating and non-alternating verbs, and could generalise to allow novel verbs to appear in both constructions. When less data was observed, it also overgeneralized the alternation, which is a behaviour characteristic of children when they are learning verb subcategorizations. These results demonstrate that the dative alternation is learnable, and therefore that universal grammar may not be necessary to account for syntactic acquisition. Overall, these results suggest that the forms of languages may be determined to a much greater extent by learning, and by cumulative historical changes, than would be expected if the universal grammar hypothesis were correct.

Computational models were constructed to investigate how the meanings of basic colour terms were learned, and to determine why these words have prototype properties, and why they partition the colour space. A Bayesian model of acquisition was able to learn colo ur term systems with these properties, but could equally well learn colour term systems which did not partition the colour space or have prototype properties, and so it failed to explain the empirical data concerning these words. Computational evolutionary simulations were then conducted by creating a community of artificial people using multiple copies of the Bayesian model. These artificial people then learned colour words from one-another, and colour term systems were allowed to evolve over a number of generations. The emergent colour terms always partitioned the colour space and had prototype properties. These results demonstrate that the Bayesian model is able to account for the properties of colour term systems only when it is placed in a social contex t and so they provide evidence of the importance of understanding language as a product of both psychology and social interaction.

An expression-induction model was used to simulate the evolution of basic color terms in order to test Berlin and Kay's (1969) hypothesis that the typological patterns observed in basic color term systems are produced by a process of cultural evolution under the influence of universal aspects of human neurophysiology. Ten agents were simulated, each of which could learn color term denotations by generalizing from examples using Bayesian inference. Conversations between these agents, in which agents would learn from one-another, were simulated over several generations, and the languages emerging at the end of each simulation were investigated. The proportion of color terms of each type correlated closely with the equivalent frequencies found in the world color survey, and most of the emergent languages could be placed on one of the evolutionary trajectories proposed by Kay and Maffi (1999). The simulation therefore demonstrates how typological patterns can emerge as a result of learning biases acting over a period of time.