In M.H. Christiansen and Kirby, S. (Eds.), Language evolution (pp. 272-294). Oxford, U.K.: Oxford University Press, 2003.

From language learning
to language evolution

Introduction

The frog's response to mating calls is determined by its genes, which have been tuned by natural selection. There is an inevitability to the use of this signal. Barring some kind of disaster in the development of the frog, we can predict its response from birth. If we had some machine for reading and translating its DNA, we could read-off its communication system from the frog genome. We cannot say the same of a human infant. The language, or languages, that an adult human will come to speak are not predestined in the same way. The particular sounds that a child will use to form words, the words themselves, the ways in which words will be modi ed and strung together to form utterances - none of this is written in the human genome.

Whereas frogs store their communication system in their genome, much of the details of human communication are stored in the environment. The information telling us the set of vowels we should use, the inventory of verb stems, the way to form the past tense, how to construct a relative-clause, and all the other facts that make up a human language must be acquired by observing the way in which others around us communicate. Of course this does not mean that human genes have no role to play in determining the structure of human communication. If we could read the genome of a human like we did with the frog, we would find that, rather than storing details of a communication system, our genes provide us with mechanisms to retrieve these details from the behaviour of others.

From a design point of view, it is easy to see the advantages of providing instructions for building mechanisms for language acquisition rather than the language itself. Human language cannot be completely innate because it would not t in the genome. Worden (1995) has derived a speed-limit on evolution that allows us to estimate the maximum amount of information in the human genome that codes for the cognitive di erences between us and chimpanzees. He gives a paltry gure of approximately 5 kilobytes. This is equivalent to the text of just the introduction to this chapter.

The implications of this aspect of human uniqueness are the subject of this chapter. In the next section we will look at the way in which language learning leads naturally to language variation, and what the constraints on this variation tell us about language acquisition. In section three, we introduce a computational model of sequential learning and show that the natural biases of this model mirror many of the human learner's biases, and help to explain the universal properties of all human languages.

If learning biases such as those arising from sequential learning are to explain the structure of language, we need to explore the mechanism that links properties of learning to properties of what is being learned. In section four we look in more detail at this issue, and see how learning biases can lead to language universals by introducing a model of linguistic transmission called the Iterated Learning Model. We go on to show how this model can be used to understand some of the fundamental properties of human language syntax.

Finally, we look at the implications of our work for linguistic and evolutionary theory. Ultimately, we argue that linguistic structure arises from the interactions between learning, culture and evolution. If we are to understand the origins of human language, we must understand what happens when these three complex adaptive systems are brought together.

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