You are not a Gadget: A Manifesto (25 page)

In fish and amphibians (the earliest vertebrates), the olfactory system sits right next to multimodal areas of the cerebral cortex, where the processing of the different senses overlaps. The same is true in reptiles, but in addition, their cortex has new regions in which the senses are separated. In mammals, incoming sights, sounds, and sensations undergo
many processing steps before ending up in the region of overlap. Think of olfaction as a city center and the other sensory systems as sprawling suburbs, which grew as the brain evolved and eventually became larger than the old downtown.

All of which has led Jim and me to wonder: Is there a relationship between olfaction and language, that famous product of the human cerebral cortex? Maybe the dictionary analogy has a real physical basis.

Olfaction, like language, is built up from entries in a catalog, not from infinitely morphable patterns. Moreover, the grammar of language is primarily a way of fitting those dictionary words into a larger context. Perhaps the grammar of language is rooted in the grammar of smell. Perhaps the way we use words reflects the deep structure of the way our brain processes chemical information. Jim and I plan to test this hypothesis by studying the mathematical properties that emerge during computer simulations of the neurology of olfaction.

If that research pans out, it might shed light on some other connections we’ve noticed. As it happens, the olfactory system actually has two parts: one detects general odors, and the other, the
pheremonic
system, detects very specific, strong odors given off by other animals (usually of the same species), typically related to fear or mating. But the science of olfaction is far from settled, and there’s intense controversy about the importance of pheromones in humans.

Language offers an interesting parallel. In addition to the normal language we all use to describe objects and activities, we reserve a special language to express extreme emotion or displeasure, to warn others to watch out or get attention. This language is called swearing.

There are specific neural pathways associated with this type of speech; some Tourette’s patients, for instance, are known to swear uncontrollably. And it’s hard to overlook the many swear words that are related to orifices or activities that also emit pheremonic olfactory signals. Could there be a deeper connection between these two channels of “obscenity”?

Lngwidge iz a straynge thingee. You can probably read that sentence without much trouble. Sentence also not this time hard.

You can screw around quite a bit with both spelling and word order and still be understood. This shouldn’t be surprising: language is flexible enough to evolve into new slang, dialects, and entirely new tongues.

In the 1960s, many early computer scientists postulated that human language was a type of code that could be written down in a neat, compact way, so there was a race to crack that code. If it could be deciphered, then a computer ought to be able to speak with people! That end result turned out to be extremely difficult to achieve. Automatic language translation, for instance, never really took off.

In the first decade of the twenty-first century, computers have gotten so powerful that it has become possible to shift methods. A program can look for correlations in large amounts of text. Even if it isn’t possible to capture all the language variations that might appear in the real world (such as the above oddities I used as examples), a sufficiently huge number of correlations eventually yields results.

For instance, suppose you have a lot of text in two languages, such as Chinese and English. If you start searching for sequences of letters or characters that appear in each text under similar circumstances, you can start to build a dictionary of correlations. That can produce significant results, even if the correlations don’t always fit perfectly into a rigid organizing principle, such as a grammar.

Such brute-force approaches to language translation have been demonstrated by companies like Meaningful Machines, where I was an adviser for a while, and more recently by Google and others. They can be incredibly inefficient, often involving ten thousand times as much computation as older methods—but we have big enough computers in the clouds these days, so why not put them to work?

Set loose on the internet, such a project could begin to erase language barriers. Even though automatic language translation is unlikely to become as good as what a human translator can do anytime soon, it might get good enough—perhaps not too far in the future—to make countries and cultures more transparent to one another.

These experiments in linguistic variety could also inspire a better understanding of how language came about in the first place. One of Charles
Darwin’s most compelling evolutionary speculations was that music might have preceded language. He was intrigued by the fact that many species use song for sexual display and wondered if human vocalizations might have started out that way too. It might follow, then, that vocalizations could have become varied and complex only later, perhaps when song came to represent actions beyond mating and such basics of survival.

Language might not have entirely escaped its origins. Since you can be understood even when you are not well-spoken, what is the point of being well-spoken at all? Perhaps speaking well is still, in part, a form of sexual display. By being well-spoken I show not only that I am an intelligent, clued-in member of the tribe but also that I am likely to be a successful partner and helpful mate.

Only a handful of species, including humans and certain birds, can make a huge and ever-changing variety of sounds. Most animals, including our great-ape relatives, tend to repeat the same patterns of sound over and over. It is reasonable to suppose that an increase in the variety of human sounds had to precede, or at least coincide with, the evolution of language. Which leads to another question: What makes the variety of sounds coming from a species increase?

As it happens, there is a well-documented case of song variety growing under controlled circumstances. Kazuo Okanoya of the Riken Institute in Tokyo compared songs between two populations of birds: the wild white-rump munia and its domesticated variant, the Bengalese finch. Over several centuries, bird fanciers bred Bengalese finches, selecting them for appearance only. Something odd happened during that time: domesticated finches started singing an extreme and evolving variety of songs, quite unlike the wild munia, which has only a limited number of calls. The wild birds do not expand their vocal range even if they are raised in captivity, so the change was at least in part genetic.

The traditional explanation for such a change is that it must provide an advantage in either survival or sexual selection. In this case, though, the finches were well fed and there were no predators. Meanwhile, breeders, who were influenced only by feather coloration, did the mate selection.

Enter Terry Deacon, a scientist who has made fundamental contributions in widely diverse areas of research. He is a professor of anthropology
at the University of California at Berkeley and an expert on the evolution of the brain; he is also interested in the chemical origins of life and the mathematics behind the emergence of complicated structures like language.

Terry offered an unconventional solution to the mystery of Bengalese finch musicality. What if there are certain traits, including song style, that naturally tend to become less constrained from generation to generation but are normally held in check by selection pressures? If the pressures go away, variation should increase rapidly. Terry suggested that the finches developed a wider song variety not because it provided an advantage but merely because in captivity it became possible.

In the wild, songs probably had to be rigid in order for mates to find each other. Birds born with a genetic predilection for musical innovation most likely would have had trouble mating. Once finches experienced the luxury of assured mating (provided they were visually attractive), their song variety exploded.

Brian Ritchie and Simon Kirby of the University of Edinburgh worked with Terry to simulate bird evolution in a computer model, and the idea worked well, at least in a virtual world. Here is yet another example of how science becomes more like storytelling as engineering becomes able to represent some of the machinery of formerly subjective human activities.

Recent successes using computers to hunt for correlations in giant chunks of text offer a fresh hint that an explosion of variety in song might have been important in human evolution. To see why, compare two popular stories of the beginning of language.

In the first story, a protohuman says his first word for something—maybe
ma
for “mother”—and teaches it to the rest of the tribe. A few generations later, someone comes up with
wa
for “water.” Eventually the tribe has enough words to constitute a language.

In the second story, protohumans have become successful enough
that more of them are surviving, finding mates, and reproducing. They are making all kinds of weird sounds because evolution allows experimentation to run wild, so long as it doesn’t have a negative effect on survival. Meanwhile, the protohumans are doing a lot of things in groups, and their brains start correlating certain distinctive social vocalizations with certain events. Gradually, a large number of approximate words come into use. There is no clear boundary at first between words, phrases, emotional inflection, and any other part of language.

The second story seems more likely to me. Protohumans would have been doing something like what big computers are starting to do now, but with the superior pattern-recognizing capabilities of a brain. While language has become richer over time, it has never become absolutely precise. The ambiguity continues to this day and allows language to grow and change. We are still living out the second story when we come up with new slang, such as “bling” or “LOL.”

Even if the second story happened, and is still happening, language has not necessarily become more varied. Rules of speech may have eventually emerged that place restrictions on variety. Maybe those late-arriving rules help us communicate more precisely or just sound sexy and high status, or more likely a little of both. Variety doesn’t always have to increase in every way.

Variety could even decrease over time. In
Chapter 9
, I explained how the lack of stylistic innovation is affecting the human song right now. If you accept that there has been a recent decrease in the stylistic variety, the next question is “Why?” I have already suggested that the answer may be connected with the problem of fragment liberation and the hive mind.

Another explanation, which I also think possible, is that the change since the mid-1980s corresponds with the appearance of
digital editing tools, such as MIDI, for music. Digital tools have more impact on the results than previous tools: if you deviate from the kind of music a digital tool was designed to make, the tool becomes difficult to use. For instance, it’s far more common these days for music to have a clockwork-regular beat. This may be largely because some of the most widely used music software becomes awkward to use and can even produce glitches if you vary the tempo much while editing. In predigital days, tools also influenced music, but not nearly as dramatically.

In
Chapter 2
I argued that the following question can never be asked scientifically: What is the nature of consciousness? No experiment can even show that consciousness exists.

In this chapter, I am wearing a different hat and describing the role computer models play in neuroscience. Do I have to pretend that consciousness doesn’t exist at all while I’m wearing this other hat (probably a cap studded with electrodes)?

Here is the way I answer that question: While you can never capture the nature of consciousness, there are ways to get closer and closer to it. For instance, it is possible to ask what meaning is, even if we cannot ask about the experience of meaning.

V. S. Ramachandran, a neuroscientist at the University of California at San Diego and the Salk Institute, has come up with a research program to approach the question of meaning with remarkable concreteness. Like many of the best scientists, Rama (as he is known to his colleagues) is exploring in his work highly complex variants of what made him curious as a child. When he was eleven, he wondered about the digestive system of the Venus flytrap, the carnivorous plant. Are the digestive enzymes in its leaves triggered by proteins, by sugars, or by both? Would saccharin fool the traps the way it fools our taste buds?

Later Rama graduated to studying vision and published his first paper in the journal
Nature
in 1972, when he was twenty. He is best known for work that overlaps with my own interests: using mirrors as a low-tech form of virtual reality to treat phantom-limb pain and stroke paralysis. His research has also sparked a fruitful ongoing dialogue between the two of us about language and meaning.

The brain’s cerebral cortex areas are specialized for particular sensory systems, such as vision. There are also overlapping regions between these parts—the cross-modal areas I mentioned earlier in connection with olfaction. Rama is interested in determining how the cross-modal areas of the brain may give rise to a core element of language and meaning: the metaphor.