Christine Kenneally
The First Word
The search for the origins of language
Viking 2007

Kenneally 226 – culture evolves

Human culture is an intensely complicated accumulation of techniques and tools. In the same way that an animal’s physical development is constrained by its genome, and therefore the genome of its parents, human culture constantly produces new forms of technology and material design by building on what came before.

The Way we live now is determined not solely by our genes but also by the course of cultural history. Even though the apparent gap between animal and human minds shrinks with each year, there is at this stage little evidence that the social and material traditions of other animals ever move beyond a simple level, in contrast of our own constantly churning culture. Reasearchers like Simon Kirby at the University of Edinburgh look at the way in which language is a product of culture as well as biology, asking not just how it evolved but how it might have evolved itself.

Imagine if you could watch this process unfold from the dawn of humanity, watch the first speakers speak and the first listeners listen, and see how meaning and structure develop, overtime, words proliferate and begin to emerge. This grand view of the history of language is a little like what Kirby seeks in his research. His speciality is computer modeling of the evolution of language.

Until the 1990s changes within and between languages could be tracked only by using the comparative method of linguistic reconstruction. But that technique has limitations. No single language from which all the world’s dialects are known to have descended has been reconstructed. The comparative method can am after traces of language from as early as six thousand years ago, but not much farther back than that. (historical linguistics, language families, Nostratic)

Computer modeling starts from the opposite end of the language chain. Instead of beginning with contemporary language and reconstructing past versions from it, Kirby creates populations of digital individuals called agents. He hands them some small amount of meaning, maybe a few rules, and then steps back and watches what they do with it.

Even though the science has been getting better and better at tracking the elusive clues to our biological language suit, we still don’t know how language itself got here in the first place. Computer modeling promises to be a most useful tool in this quest. In addition to the godlike allure of creating populations than watching them evolve into different kinds of creatures, this technique became so popular so quickly because modeling proposes the answers to such questions as: How did the wordlike items that our ancestors used proliferate to become many tens of thousands of words with many rules about how they can be combined today? Why does language have structure, and why does it have its particular structure? How is it that the meaning of a sentence arises from the way it is put together, not just from the meaning of the words alone?

In just a few years computer modeling of language evolution has produced findings that are counterintuitive to a traditional view of language. The most fundamental idea driving this research is that there are at least two different kinds of evolution – biological and linguistic, meaning that as we evolved, language evolved on its own path.

Kirby starts his models by building a single individual,and then creating a whole population of them. “I’ll have them communicating with each other than transmitting their knowledge culturally over thousands or tens of thousandsgenerations and very long periods of time. In some extensions of the model, I allow those agents to evolve biologically as well,” What he and other researchers in the field have found is that from little things, big things grow. In these accelerated models, from the small was beginning – agents with the ability to make sound but not words, agents who start out not knowing what are the speakers mean – comes incredible structural complexity that looks a lot like language.

This cultural evolution, said Kirby, is simply the repeated learning by individuals of other individuals behavior: “The idea is that you’ve got iterated learning whenever your behavior is the result of observing and other agents particular behavior. Language is the perfect example of this. The reason I speak in the way I do it is because when I was younger I was around people who spoke and tried to speak like that. And what we’ve been finding and models is, to some extent, that is all you need. It’s very surprising. But if you make some very, very simple assumptions like that, you can get linguistic structure to emerge out of nothing – just from the assumption that the agents basically learned to speak on the basis of having seen the population speak before them.”

Strangely enough, the most languagelike structures arise from beginnings that are constrained or not full of information. Kirby discovered that if the agents had only limited access to one another’s utterances – either because he made the language so big that they could observe only a small part of it at any one time or because they made sure they listen to only a few sentences at the time – then a lot of syntactic structure would eventually arise over the generations of agents. “It’s a kind of irony that you get this complex and structured language precisely when you make it difficult for the agents to learn. If you make it easy for them, then nothing interesting happens.”

It would not be possible for Kirby, or anyone for that matter, to sit down and calculate the ways in which thousands of generations of different individuals may have interacted, and this is what makes digital modeling such a powerful tool. It offers a strong contrast to the armchair models that linguists have used for many years. For example, mainstream linguistics saw language is taking place between an idealized speaker and an idealized hearer. These two were representatives of the population of individuals who spoke pretty much the same language and were basically identical to one another. But this model blurs the distinction between the population and its constituent individuals. Digital modeling allows researchers to account for individuals within language communities. Modelling, then, can consist of a least two tiers of interactions – between individual agents within a population and between populations of these agents.

“If you look at the lifetimes of individuals, you see massive changes in there, from nothing to for language user,” explains Kirby. “It’s a hugely complex process that leads from one state to another. Then, on top of that, language changes in the community. So the new thing that is emerging is this desire to link individuals with populations in the model directly, by saying: Let’s put together lots of agents that are seriously individual, and see what happens when there is a population of these.”

Kirby and a number of other researchers find one metaphor especially useful for thinking about language: imagine that it is a virus, a non-conscious life form that evolved independently of the animals infected by it. Just as a standard virus adapts to survival in its physical environment, the language virus adapts to survival in its environment – a complicated landscape that includes the semi-linguistic mind of the infant, the individual mind of the speaking adults, and the collective mind of communicating humans.

According to Terrence Deacon, language and its human host are parasitic upon each other. “Modern humans need the language parasite in order to flourish and reproduce just as much as it needs humans to reproduce”. It’s an analogy that goes straight to the heart of how much language means to us as a species.

The most exciting implications of the language as virus metaphor is the finding that some features of language have less to do with the need of individuals to communicate with one another than with the need of the language virus to ensure its own survival. The features of language structure reflects its struggle to survive in its environment – the human mind. Reproduction is still the driving force of the evolutionary process, but it’s not our reproduction: it’s the reproduction of language itself.

236 Kirby, Deacon, and the computation modeler Morton Christiansen are especially interested in why languages are learnt so readily by children. Their approach flips the old notion of poverty of stimulus on its head: if languages driven to survive, and the language learners of the world children, language must be adapted to the quirks and traits of the child’s mind. Language is designed to be “particularly effective for the child's brain”.

So if language in its very structure has all or most of the clues that children require to learn it, then the need for some kind of language organ starts to look dubious. In its strongest version, this approach means there is no support for the argument that grammar is so complicated that children simply can’t learn it without a grammar specific device. It makes more sense to talk about language learning than about language acquisition, argue Kirby and Christiansen

Kenneally