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How Does the Brain Generate Computation?
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How Does the Brain Generate Computation?
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.
www.edge.org
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Brain's Algorithm
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I was thinking about how we could achieve the knowledge of how our minds work and suddenly I had an idea:
DNA could be made up of tiny basic codes, that when they are put together they build a more complex structures, similarly as computer programs, where a few instructions can be used again and again in different programs, but although the same tiny instructions (for, while, write) are used, they build software that are totally different.
If DNA uses an analogous functionality, understanding only a few pieces of it can make us understand the whole without needing to test each part to understand its functionality, we could read it as easy as a program.
If we understood how those small pieces work, we could simulate them into a computer, and if we load the DNA data into it we could simulate a human. And if we could do it, we will have in our PC not only a human simulation, we will have the Brain's algorithm totally unveiled. |
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Re: How Does the Brain Generate Computation?
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There are types of computation that seem universal to animals (at least mammals) such as the ability to compute trajectories (you can catch a ball and a dog can catch anything from sticks to frisbees), we all know when someone has more of something than we do, and so does my dog. Where humans diverge is in knowing how much more. But most of the strategies we've developed to measure units seem to come from a place that animals don't have. I believe this is cultural and linguistic in nature. Some cultures have no numbers for any number larger than three. It's just one, two, many. But everyone knows if my pile is bigger than your pile, even if they can't enumerate it. Number and the use of number is a cultural phenomenon. It's a skill that has to be learned and practiced. When I started using a calculator, I began to lose the ability to calculate, in my head and on paper. I can no longer compute the square root of something, for example, nor figure out the payments on a bank loan for a specific rate of interest. Even addition and subtraction have become more difficult than they were before I started using a machine to do this for me. If I don't have a caldulator handy, I have to resort to counting on my fingers or following the algorithms I learned as a child, using a pencil and paper. Twenty years ago I could do all of this in my head. The question is, where did that skill go? I suspect it was lost from lack of practice. I could probably get it back again if I stopped using machines to calculate for me, but with them being so ubiquitous and so cheap and faster than my brain for most problems, I have little incentive to go back to using drudge work to manipulate numbers. |
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Re: How Does the Brain Generate Computation?
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Hi, I am in the process of writing a research paper, and i was wondering if anyone could add some input to it. It would be greatly appreciated.
(note: by better i mean greater overall efficiency (i.e more more accurate, reliable, and faster, given x amount of time ))
I have often pondered the question why calculators are so much better doing simple computations (such as addition, subtraction, multiplication and division), than humans.
I have come to some fairly basic intuitive conclusions.
1. as stated by grantcc, a lack of practice, could be the cause. (But calculators don't practice, and their computing skill remains constant)
2. in a general case the more tasks a machine is obligated to preform the worse it performs each task. Which can also be concluded about humans, (also stated by grantcc) once a person develops more intricate mathematical skills they generally become worse at simple computation.
But what is the underlying cause of this?
3. I have also been debating whether or not this has something to do with complexity, as the complexity in a system (such as our brains) grows it becomes, less reliable when comforted with simple computations. A machine with less complexity, such as a light switch is more reliable at achieving it's intended task, than take for example the space shuttle.
But why is this?
I have also taken into consideration, if theory (4) is true then why don't computers show any error when faced with simple computations? One reason is that the program for carrying out the computations is based on fairly simple underlying rules. But if this isn't the case i don't think a computer is fundamentally more complicated than an average calculator, therefore not revealing a substantial error in its basic computation accuracy/reliability.
Basically what i am trying to get out of this is a basic underlying theory of why this happens (why are calculators better at addition, subtraction, multiplication and division than humans?) Or in a general case, why are complicated systems worse at performing simple tasks than a less complicated system (light-switch vs. spaceshuttle). |
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