How Does the Brain Generate Computation?
December 19, 2001 by Marc D. Hauser
In this Edge talk, Marc D. Hauser reflects on attempts to answer this question, from Noam Chomsky’s insights to the dance of the honey bee.
Originally published December 4, 2001 at Edge. Published on KurzweilAI.net December 19, 2001.
For humans, Chomsky’s insights into the computational mechanisms underlying language really revolutionized the field, even though not all would agree with the approach he has taken. Nonetheless, the fact that he pointed to the universality of many linguistic features, and the poverty of the input for the child acquiring language, suggested that an innate computational mechanism must be at play. This insight revolutionized the field of linguistics, and set much of the cognitive sciences in motion. That’s a verbal claim, and as Chomsky himself would quickly recognize, we really don’t know how the brain generates such computation.
Some of the problems that we’ve been dealing with in the neurosciences and the cognitive sciences concerns the initial state of the organism. What do animals, including humans, come equipped with? What are the tools that they have to deal with the world as it is? There’s somewhat of an illusion in the neurosciences that we have really begun to understand how the brain works. That’s put quite nicely in a recent talk by Noam Chomsky. The title of the talk was “Language and the Brain.”
Everybody’s very surprised to hear him mention the brain word, since he’s mostly referred to the mind. The talk was a warning to the neuroscientists about how little we know about, especially when it comes to understanding how the brain actually does language. Here’s the idea Chomsky played with, which I think is quite right. Let’s take a very simple system that is actually very good at a kind of computation: the honey bee. Here is this very little insect, tiny little brain, simple nervous system, that is capable of transmitting information about where it’s been and what it’s eaten to a colony and that information is sufficiently precise that the colony members can go find the food. We know that that kind of information is encoded in the signal because people in Denmark have created a robotic honey bee that you can plop in the middle of a colony, programmed to dance in a certain way, and the hive members will actually follow the information precisely to that location. Researchers have been able to understand the information processing system to this level, and consequently, can actually transmit it through the robot to other members of the hive. When you step back and say, what do we know about how the brain of a honeybee represents that information, the answer is: we know nothing. Thus, our understanding of the way in which a bee’s brain represents its dance, its language, is quite poor. And this lack of understanding comes from the study of a relatively simple nervous system, especially when contrasted with the human nervous system.
So the point that Chomsky made, which I think is a very powerful one, and not that well understood, is that what we actually know about how the human brain represents language is at some level very trivial. That’s not to say that neuroscientists haven’t made quite a lot of impact on, for example, what areas of the brain when damaged will wipe out language. For example, we know that you can find patients who have damage to a particular part of the brain that results in the loss of representations for consonants, while other patients have damage that results in the loss of representations for vowels.
But we know relatively little about how the circuitry of the brain represents the consonants and vowels. The chasm between the neurosciences today and understanding representations like language is very wide. It’s a delusion that we are going to get close to that any time soon. We’ve gotten almost nowhere in how the bee’s brain represents the simplicity of the dance language. Although any good biologist, after several hours of observation, can predict accurately where the bee is going, we currently have no understanding of how the brain actually performs that computation.
The reason there have been some advances in the computational domain is there’s been a lot of systems where the behavior showcases what the problem truly is, ranging from echolocation in bats to long distance navigation in birds. For humans, Chomsky’s insights into the computational mechanisms underlying language really revolutionized the field, even though not all would agree with the approach he has taken. Nonetheless, the fact that he pointed to the universality of many linguistic features, and the poverty of the input for the child acquiring language, suggested that an innate computational mechanism must be at play. This insight revolutionized the field of linguistics, and set much of the cognitive sciences in motion. That’s a verbal claim, and as Chomsky himself would quickly recognize, we really don’t know how the brain generates such computation.
One of the interesting things about evolution that’s been telling us more and more is that even though evolution has no direction, one of the things you can see, for example, within the primates is that a part of the brain that actually stores the information for a representation, the frontal lobes of our brain, has undergone quite a massive change over time. So you have systems like the apes who probably don’t have the neural structures that would allow them to do the kind of computations you need to do language-processing. In our own work we’ve begun to look at the kinds of computations that animals are capable of, as well as the kind of computations that human infants are capable of, to try to see where the constraints lie.
Whenever nature has created systems that seem to be open-ended and generative, they’ve used some kind of system with a discrete set of recombinable elements. The question you can begin to ask in biology is, what kind of systems are capable of those kinds of computational processes. For example, many organisms seem to be capable of quite simple statistical computations, such as conditional probabilities that focus on local dependencies: if A, then B. Lots of animals seem capable of that. But when you step up to the next level in the computational hierarchy, one that requires recursion, you find great limitations both among animals and human infants. For example, an animal that can do if A then B, would have great difficulty doing if A to the N, then B to the N. We now begin to have a loop. If animals lack this capacity, which we believe is true, then we have identified an evolutionary constraint; humans seem to have evolved the capacity for recursion, a computation that liberated us in an incredible way.
Continued at Edge.
Copyright © 2001 by Edge Foundation, Inc.