The First Word: The Search for the Origins of Language (34 page)

This has obvious implication for stages of language evolution, where a new level of complexity replaces a previous level without any conscious agreement by protospeakers. Loreto and his colleagues suggest some interesting ways to exploit semiotic dynamics. For example, scientists could deploy groups of robots with such capabilities in situations where contact with humans is unreliable or impossible. Such robots might explore distant planets or deep seas, creating a way to communicate about, and respond to, events that were completely unforeseeable by their human programmers.

The involvement of a physicist like Loreto in a project connected to language evolution is a striking sign of just how many tentacles this problem has. Another language evolution researcher with a surprising background is Ramon Ferrer i Cancho. He is a former computer scientist who now works in the Department de Física Fonamental at Universidad de Barcelona. Ferrer i Cancho uses Zipf’s law to model language, exploring the trade-offs between speakers and hearers during communication.

Speakers must make an effort in order to be understood. For a speaker to be as clear as possible and avoid ambiguous meanings, greater effort is required. Listeners, on the other hand, must make an effort to interpret the correct meaning of an utterance, and they must work harder to decipher the intent of a speaker who has devoted less effort to clarity. Accordingly, Ferrer i Cancho’s models explore what happens when there are small shifts in the balance between the effort of the speaker and the hearer. In fact, a tiny change in the balance between the two can dramatically alter the properties of a communication system. Says Ferrer i Cancho, it’s possible that similarly small changes may underlie a dramatic shift from a communication system with a simple vocabulary made up of a few precise words to a larger vocabulary with varying levels of semantic precision.

The history of animal language research has been a turbulent one, but that may also be changing. Of language evolution conferences, Heidi Lyn said, “If I go to talks by some of the more established people, it tends to be either that they don’t mention the ape language research at all or they dismiss it. And there are people who consistently stand up and get things wrong. For example, an older linguist at the Harvard language evolution conference in 2002 who was asked about Kanzi dismissed him. ‘Kanzi’s an aberration,’ he said. ‘He is the only example that we’ve ever seen of this.’” At the same conference, Herb Terrace stood up and asked Lyn if Kanzi was trained with food rewards. Lyn explained that they didn’t do this, yet Terrace persisted with that line of questioning. “It’s different with scholars my age or younger,” Lyn observed. The next generation gives a lot more credence to ape language research, and to work like Sue Savage-Rumbaugh’s. “They are willing to look at the data,” said Lyn. “It’s not just a matter of age. It’s the difference between people who lived through the Terrace criticism and the people who didn’t.”

For more than two decades Savage-Rumbaugh herself has been working closely with scholars from a language research program in Atlanta to apply the picture keyboards and other techniques she has used for communicating with the bonobos to communication with mentally retarded individuals whose levels of language skills have reached only those of small children. They have had great success with some individuals, equipping them with an ability to connect with other human beings that they wouldn’t have otherwise had.
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Other applications of language evolution research are completely futuristic but, at the same time, surprisingly practical. Philip Lieberman’s experiments on Everest not only illuminate the path that language evolution took but are serving as a model for NASA to monitor the well-being of astronauts on their way to Mars. The brain damage that Everest climbers suffer when they experience oxygen deprivation is similar to the kind of damage that a Mars-bound astronaut would incur from exposure to cosmic rays. If scientists back on Earth are able to detect subtle or profound neural damage in astronauts simply by listening to how they pronounce certain vowels and consonants, they’ll be able to react, and, it is hoped, treat them accordingly. This same project is also promising to improve the early diagnosis of Parkinson’s disease, not to mention help the mountain climbers of the world.

The way that evolutionary research is redefining language has social consequences as well. Lieberman argues that if language were a true instinct, if it simply flowed from every single one of us regardless of the environment into which we were born, then our governments would have very little responsibility to promote its expression. Because language is a skill, and one that is closely connected to thinking, he says, it is improved by practice and training and environments that are conducive to learning. This creates a civic responsibility to help all students hone their language skills.

At the Evolution of Language conference in Rome in 2006, Tecumseh Fitch listed the many ways in which the field had made progress since the 1866 ban on the subject. He started by noting that for the first time at the language evolution meeting, no one had mentioned the ban.

F
ive years after Pinker and Bloom wrote about the evolution of the eye and its lessons for language evolution, Dan-Eric Nilsson and Susanne Pelger of Lund University in Sweden published a paper called “A Pessimistic Estimate of the Time Required for an Eye to Evolve.” Nilsson and Pelger digitally modeled the trajectory of the eye, beginning with a flat light-sensitive patch of cells—the kind of simple eye that we know some creatures have—and inflated it over time into a fully functioning mammalian eye.
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The scientists worked out a sequence of very small changes that had to occur if the light-detecting cells were to evolve into the separate specialized parts that interact with one another in an eye. For their model to be realistic, each small evolutionary step had to confer some survival advantage and therefore improvement in vision. Even though the changes were extremely tiny (no more than 1 percent change at any one time), each slightly modified eye was able to detect more and more spatial information. As the title of the paper suggests, Nilsson and Pelger erred on the side of pessimism, always assuming that it would take more generations for the eye to evolve rather than fewer. Given this, they calculated that it would take about 1,829 separate evolutionary steps for the flat-patch eye to evolve into a stereo-vision globe. That amounts to less than 364,000 years, not long at all from an evolutionary perspective.

We know from the fossil record that animals with modern eyes lived as early as the Cambrian period, 550 million years ago, which means there has been time for eyes to evolve more than fifteen hundred times since then. As perfect and wondrously complicated as our eyes seem to us, they are not irreducibly perfect from an evolutionary perspective.

To extend Pinker and Bloom’s analogy to language: this means that abilities and organs that seem wildly complicated from our perspective may be able to come together relatively rapidly as functioning, complex wholes. In addition to this biological potential, we know from the work of people like Deacon, Kirby, and Christiansen that language itself may also evolve and that linguistic evolution occurs even more rapidly than biological evolution. Language may have appeared very recently in the human lineage, but that doesn’t mean it was the product of a single, crucial event. No one mutation of genes or social order caused language to erupt from the mouths of our ancestors.

Even if researchers can’t pinpoint every evolutionary event that led to the language we have today, and even though we don’t know exactly what all the bends in the historical road looked like, the principles for further illuminating the path of language evolution are now self-evident. Fundamentally, the appearance of design in biology and in language can be taken as a sign of evolution, not of a designer. Additionally, where complex design does exist, it makes sense not to treat the whole as a monolith that simply developed from nothing to something in one or two quick steps. Finally, the most likely scenario is that both evolutionary novelty and derivation played a significant role in the evolution of a phenomenon as complex as language.

What does it mean that we are getting closer to the answer of how language evolved? The implications are as diverse and varied as the story of evolution itself. First, from a research perspective, it means that good data lead to better data, and there is still a great deal of data to be gathered before the big picture can be filled out. “People have been arguing about Neanderthal speech for the last thirty-five years and whether chimp sign language is really language,” said Tecumseh Fitch, “yet nobody even thought to ask what chimps do when they vocalize. We still don’t know—nobody’s put a chimp in an X-ray setup and watched it vocalize. It’s amazing how much data is out there that hasn’t been collected, like taping birdsong and whale song and doing linguistic analysis of that. We could apply this huge theoretical apparatus that phonologists have developed to birdsong. It’s not even that hard, and it’s an obvious thing to do.” Fitch added, “What amazes me coming into this field is how many things you can answer that no one even thought to look at.”

One of the biggest questions yet to be answered is posed by Ray Jackendoff: How do neurons do it? Magnetic resonance imaging and other ways of seeing the brain in action have taught us a lot about how our brains function. Overall, imaging has shown that for many higher-level activities, like language, neural activity is distributed across the brain. There are no specific areas that light up for language and language alone. Still, there’s no doubt that scientists fifty years from now will find the wonders of our neuroscience to be fairly crude. Although we can now map the brain as it works, we still have no actual idea
how
it works. How do the neurons do what they do? How do they process, store, and produce language? There is no predetermined meaning inside our heads. Neurons don’t contain symbols, but mainly pass on (or don’t pass on) activation signals to one another. So how can the patterned flare of electrical charge across our brains mean that we recognize the word “cat,” even when it is spoken by one hundred different speakers with their one hundred unique voices? How can we tell the difference between a
p
and a
b
when there is no tiny prototype of these sounds deposited in our neurons?

“We know we can’t think of the brain as a digital computer anymore,” said Jackendoff. “It’s sort of a parallel, semi-analog computer. But how does it do these digital things?” Discovering how neurons work should allow us to determine once and for all which of these frameworks for analysis—from the prototypical
p
to the syntax of English—are real and which are mirages.

It’s clear by now that the problem of language evolution is completely intractable when you approach it from the perspective of a single discipline. For all the salient questions to be answered, the multidisciplinary nature of the field will have to become even more so. So far, it has taken years for individuals in different departments to start talking, to develop research questions that make sense for more than one narrow line of inquiry, and to start to understand one another’s points of view. The field of language evolution needs students who can synthesize information from neuroscience, psychology, computer modeling, genetics, and linguistics. The more this happens, the richer and wider the field will become, instead of devolving around one or two theoretical issues.

Technology and wide-ranging discussion are not the only factors that will aid the next big leaps in understanding. Much of the impetus will come from the fact that a generation of scientists has broken free from the iron grip of some old ideas, while other notions that were once regarded as radical, or at least unpopular, have spread into the mainstream in all branches of science. The notion that animals do not think—or that, if they do, it is completely and qualitatively different from human thinking—is finally dying, if not completely dead. This idea shaped research in many different fields for decades, both in a direct way and by scaring people off the topic for fear of looking foolish.

The flip side of the animals-are-dumb belief is the idea that human thinking is boundless and that our language is infinitely expressive. Yet evolutionary theory, which tells us, first, that we are a particular type of creature, not an über-creature; second, that our brains are particular types of thinking machines, not all-purpose thinking machines; and, third, that although the structure of our language means we can be extremely creative, we are only as likely to express infinite meaning as we are to talk for eternity.

No matter what their particular take is on complexity or innateness, most theories of language and evolution have one thing in common: they focus on what’s happened in the past up to the present. It’s an obvious frame of reference, but sometimes that focus gives the impression that the present is an eternal moment that will stretch forward into the future, with us—and language—remaining unchanged forever. Some scientists have even argued explicitly that we have stopped evolving.

Certainly humanity is a powerful force of selection, both on other species and on ourselves. We have been manipulating the genomes of plants for thousands of years in agriculture, and we’ve been doing the same thing with livestock, as well as with dogs, cats, and other domesticated animals. The sheer weight of the human biomass and all of its accessories—its buildings, fields, roads, dams, and cell phone towers—affects the survival of other species by pushing them into smaller and smaller niches. We deselect the genomes of some animals, like the mammoth, by hunting them to extinction, and we pollute, poison, and inadvertently engineer the genomes of others—like fish whose DNA is corrupted by human estrogen in waterways. We introduce alien species into new environments, where they decimate local populations or rapidly evolve themselves in order to survive. Our use of pesticides and drugs induces the ultra-rapid evolution of resistant strains of bacteria and viruses. And of course we change the natural history of the human genome with the mass production of food, medicine, and health care. Diseases and traumas that would otherwise kill us before we had a chance to reproduce can today be completely averted. Similarly, men and women who would otherwise not be able to conceive can now bear children with the assistance of reproductive technologies. In fact, a generation of children whose parents were among the first to undergo in vitro fertilization are now a far-flung group of young adults bearing their own children and spawning a generation that in another time could never have existed.

Today humanity is tinkering inside the evolutionary machine itself, altering DNA directly. Normally, in the shuffle and flow of evolutionary change, no single genome occurs more than once—except, of course, when twins or other multiples are born. But in 2006 we cloned cats and dogs for the first time, and these animals were just the latest in a growing list. No one can reasonably expect that a cloned human is far off. We’re also tinkering with the ways genes express themselves in individuals. The intent behind this science is not just to head off illness but, for some researchers, to bioengineer designer human beings.

While all living things affect the evolution of other living things simply by virtue of trying to stay alive, humans interact with the biological evolution of other species in a much more complex and powerful fashion because of one ability: language. Nothing occurs on the human scale without language. No language means no agriculture, no animal farming, no science.

Still, as fascinating and unprecedented as this moment in the history of life on earth is, it is only a single point in time. We tend to assume that our current evolutionary stage is the inevitable endpoint of some natural drive to complexity and intelligence, but
now
is merely an arbitrary instant. The future stretches out before us, and, as the saying has it, it’s going to go for a lot longer than the past. As far as our species is concerned, this “modern” era may well be the dawn of time. Certainly, the fossil record reveals that anything can and does happen. Ice ages, meteors, killer viruses, and tsunamis occur and recur, and these are only the most dramatic and obvious events that can alter the course of a species—either by selecting some genomes over others or by extinguishing them entirely. The only real measure of success on this planet remains what it has always been: not language, but life. Our species survives. And every other type of animal that doesn’t possess human language but still exists, by definition, also survives. The notion that we may have halted evolution or stopped evolving ourselves is just another version of the seductive but empty idea that we have control over our destiny, either as individuals or as a species.

In 2005 scientists published the results of a number of experiments that indicated that humans are still evolving. In one case, a team of geneticists led by Bruce Lahn at the University of Chicago offered proof that the human brain has been continuously evolving since
Homo sapiens
first appeared. The scientists looked at two genes known as microcephalin and ASPM, both of which are known to contribute to brain growth.
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(They are also expressed in other tissue in the body.) The geneticists sequenced DNA from a collection of human cells that represents the variation in our species, and they found that one variation of each gene, called an allele, occurred with particularly high frequency. The fact that the alleles seemed to occur more than normal genetic drift would allow suggests that they have been actively selected over time. The scientists believe that the frequent allele of microcephalin appeared around thirty-seven thousand years ago and the frequent allele of ASPM appeared only fifty-eight hundred years ago. It’s not known what effect these versions of these genes have, or why they were selected. They could have shaped cognition, as Lahn argues. Other scientists suggest the genes could have had some other effect on the brain that doesn’t directly impact thought.

At the same time that Lahn’s results were published, another team of scientists based at the University of California, San Diego, announced the discovery of a positively selected gene called SIGLEC11 that is expressed in brain cells called microglia. Although they can’t yet explain the effects of the gene, it is interesting because it is one of the very few found only in humans and not in our ape cousins. This could make it a candidate for explaining some of the differences between us and them.

Another direct case study of natural selection at work in humans today is an experiment carried out by scientists in Sweden. The study showed that a chromosome with a particular arrangement known as an inversion is positively selected for in the people of Iceland. The inverted form is one of two possible arrangements of the chromosome, and it occurs rarely in other human groups (hardly ever in Africans and virtually never in East Asians). Nevertheless, it is carried by 20 percent of the population of Iceland, and the women who carry this particular form of chromosome have more children than those who do not.
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