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Is the Brain a Digital Computer?
John R. Searle
I. Introduction. Strong AI, Weak AI and Cognitivism.
There are different ways to present a Presidential Address to the
APA; the one I have chosen is simply to report on work
that I am doing right now, on work in progress.
I am going to present some of my further explorations into
the computational model of the mind.\**
The basic idea of the computer
model of the mind is that the mind is the
program and the brain the hardware of a computational system.
A slogan one often sees is: "the mind is to the brain as
the program is to the hardware." \**
Let us begin our investigation of this claim by distinquishing three questions:
- Is the brain a digital computer?
- Is the mind a computer program?
- Can the operations of the brain be simulated on a digital computer?
I will be addressing 1 and not 2 or 3.
I think 2 can be decisively
answered in the negative. Since programs are defined purely formally
or syntactically and since minds have an intrinsic mental content, it
follows immediately that the program by itself cannot constitute
the mind. The formal syntax of the program does not by itself
guarantee the presence of mental contents.
I showed this a decade ago in the Chinese Room Argument (Searle,1980).
A computer, me for example, could run the steps in the program
for some mental capacity, such as understanding Chinese, without
understanding a word of Chinese.
The argument
rests on the simple logical truth that syntax is not the same as,
nor is it
by itself sufficient for, semantics. So the answer to the second question is
obviously "No".
The answer to 3. seems to me
equally obviously "Yes", at least on a natural interpretation.
That is, naturally interpreted, the question means: Is there some description of the
brain such that under that description you could do a computational
simulation of the operations of the brain. But since according to
Church's thesis, anything that can be given a precise enough
characterization as
a set of steps can be simulated on a digital computer, it
follows trivially that the question has an affirmative
answer. The operations of the brain can be simulated
on a digital computer in the same sense in which
weather systems, the behavior of the
New York stock market or the pattern
of airline flights over Latin America can.
So our question is not, "Is the mind a program?" The answer
to that is, "No". Nor is it, "Can the brain be simulated?" The answer
to that is, "Yes". The question is, "Is the brain a digital computer?"
And for purposes of this discussion I am taking that question as
equivalent to: "Are brain processes computational?"
One might think that this question
would lose much of its interest if question 2
receives a negative answer. That is, one might
suppose that unless the mind is a program, there is no
interest to the question whether the brain is a computer.
But that is not really the case.
Even
for those who agree that programs by themselves
are not constitutive of mental phenomena, there
is still an important question:
Granted that there is more to the mind than the
syntactical operations of the digital computer;
nonetheless, it
might be the case that mental states are
at least
computational states and
mental processes are computational processes operating
over the formal structure of these mental states. This,
in fact, seems to me the position
taken by a fairly large number of people.
I am not saying that the view is fully clear,
but the idea is something like this: At some level of description
brain processes are syntactical; there are so to speak, "sentences
in the head". These need not be sentences in English or Chinese, but
perhaps in the "Language of Thought" (Fodor, 1975). Now, like any
sentences, they have a syntactical structure and a semantics or
meaning,
and the problem of syntax can be separated
from the problem of semantics.
The problem of semantics
is: How do these sentences in the head get
their meanings?
But that question can be discussed independently of the question:
How
does the brain work in processing these sentences?
A typical answer to that latter question is:
The brain works as a digital computer performing computational
operations over
the syntactical structure
of sentences in the head.
Just to keep the terminology straight, I call the view that
all there is to having a mind is having a program, Strong AI,
the view that brain processes (and mental processes)
can be simulated computationally , Weak AI.
and the view that the brain is a digital computer, Cognitivism.
This paper is about Cognitivism,
and I had better say at the beginning what motivates it. If you
read books about the brain (say Shepherd (1983) or Kuffler and
Nicholls (1976)) you get a certain picture of what is going on in the
brain.
If you then turn to books about computation (say Boolos and Jeffrey, 1989)
you get a picture of the logical structure of the theory of
computation. If you then turn to books about cognitive science,
(say Pylyshyn, 1985) they
tell you that what the brain books describe is really the same as
what the computability books were describing.
Philosophically speaking, this does not smell right to me and I have
learned, at least at the beginning
of an investigation, to follow my sense of smell.
II. The Primal Story
I want to begin the discussion by trying to state as strongly as I
can why cognitivism has seemed intuitively appealing.
There
is a story about the relation of human intelligence to
computation that goes back at least to Turing's classic paper (1950),
and I believe it is the foundation
of the Cognitivist view. I will call it the Primal Story:
We begin with two results in mathematical logic, the Church-Turing
thesis (or equivalently, the Churchs's thesis) and Turing's theorem.
For our purposes, the Church-Turing thesis states that for any algorithm
there is some Turing machine that can implement that algorithm.
Turing's thesis says that there is a Universal
Turing Machine which can simulate any Turing Machine. Now if we
put these two together we have the result that a Universal Turing
Machine can implement any algorithm whatever.
But now, what made this
result so exciting? What made it send shivers up and down
the spines of a whole generation of young workers in artificial
intelligence is the following thought: Suppose the brain
is a Universal Turing Machine.
Well, are there any good
reasons for supposing the brain might be a Universal Turing Machine?
Let us continue with the Primal
Story
It is clear that at least some human mental abilities are
algorithmic. For example, I can consciously do long division by
going through the steps of an algorithm for solving long division
problems. It is furthermore a consequence of the Church - Turing thesis
and Turing's theorem
that anything a human can do algorithmically can be done
on a Universal Turing Machine.
I can implement, for example, the very same algorithm that I use for
long division on a digital computer.
In such a case, as described by Turing (l950), both I, the
human computer, and the
mechanical computer are implementing the same algorithm, I am doing it
consciously, the mechanical computer nonconsciously. Now it seems
reasonable to suppose there might also be a whole lot of mental processes
going on in my brain nonconsciously
which are also computational.
And if so, we could
find out how the brain works by simulating these very processes on
a digital computer. Just as we got a computer simulation of the
processes for doing long division, so we could get a
computer simulation of the processs
for understanding language, visual perception, categorization, etc.
"But what about the semantics?
After all, programs are purely syntactical."
Here another set of logico-mathematical results comes into play
in the Primal Story.
The development of proof theory showed that within certain well known
limits the semantic relations between propositions can be entirely
mirrored by the syntactic relations between the sentences that express
those propositions. Now suppose that mental contents in the head are
expressed syntactically in the head, then all we would need to
account for mental processes would be computational processes between
the syntactical elements in the head. If we get the proof theory right
the semantics will take care of itself; and that is what computers do:
they implement the proof theory.
We thus have a well defined research program.
We try to
discover the programs being implemented in the brain by
programming computers to implement the same programs. We do this
in turn by getting the mechanical computer to match the performance
of the human computer (i.e. to pass the Turing Test) and then getting
the psychologists to look for evidence that the internal processes
are the same in the two types of computer.
Now in what follows I would like the reader to keep this Primal Story in
mind - notice especially Turing's contrast between the conscious
implementation of the program by the
human computer and the nonconscious implementation of programs, whether
by the brain or by the mechanical computer; notice furthermore the
idea that we might just
discover
programs running in nature, the very same programs that we put
into our mechanical computers.
If one looks at the books and articles supporting Cognitivism one finds certain common
assumptions, often unstated, but nonetheless pervasive.
First,
It is often assumed that the only alternative to the
view that the brain is a digital computer is some form of
dualism.
The idea is that unless you believe in the existence of
immortal Cartesian souls, you must believe that the brain is
a computer.
Indeed, it often seems to be assumed that the question
whether the brain is a physical mechanism determining our mental
states and whether the brain is a digital computer are
the same question.
Rhetorically speaking,
the idea is to bully the reader into thinking
that unless he accepts the idea
that the brain
is some kind of computer, he is committed to
some weird antiscientific views.
Recently the field has opened up a bit to allow that
the brain might not
be an old fashioned von Neumann style digital computer, but
rather a more sophisticated kind of parallel processing
computational equipment.
Still, to deny that the brain is computational
is to risk losing your membership in the scientific community.
Second,
it is also assumed that the question whether brain processes are
computational is just a plain empirical question. It is to be
settled by factual investigation in the same way that such questions
as whether the heart is a pump or whether green leaves do
photosynthesis were settled as matters of fact.
There is no room for logic chopping or conceptual analysis,
since we are talking about matters of hard scientific fact. Indeed
I think many people who work in this field would doubt that
the title of this paper poses an appropriate philosophic question
at all. "Is the brain really a digital computer?" is no more a
philosophical question than "Is the neurotransmitter at
neuro-muscular junctions really acetylcholene?"
Even people who are unsympathetic to Cognitivism,
such as Penrose and Dreyfus, seem to treat it as a
straightforward factual issue.
They do not seem to be worried about the
question what sort of claim it might be that they are doubting.
But I am puzzled by the question:
What sort of fact about the brain could constitute its
being a computer?
Third,
another stylistic feature of this literature is the
haste and sometimes even carelessness with which the foundational
questions are glossed over. What exactly are the anatomical and
physiological features of brains that are being discussed? What
exactly is a digital computer? And how are the answers
to these two questions supposed
to connect?
The usual procedure in these books and articles is to make
a few remarks about 0's
and 1's, give a popular summary of the Church-Turing thesis, and
then get on with the more exciting things such as computer
achievements and failures.
To my surprise in reading this literature
I have found that there seems to be
a peculiar philosophical hiatus. On the
one hand, we have a very elegant set of mathematical results ranging
from Turing's theorem to Church's thesis to recursive function theory.
On the other hand, we have an impressive set of
electronic devices which we use every day.
Since we have such advanced mathematics and
such good electronics,
we assume that
somehow somebody must have done the basic philosophical work of
connecting the mathematics to the electronics. But as far as
I can tell that is
not the case. On the contrary, we are in a peculiar
situation where there is little theoretical agreement among the practitioners
on such absolutely fundamental questions as,
What exactly is a digital computer?
What exactly is a symbol? What exactly is a computational process?
Under what physical conditions exactly are two systems implementing the
same program?
III.The Definition of Computation
Since there is no universal agreement on the fundamental
questions,
I believe it is best to go
back to the sources, back to the original definitions given
by Alan Turing.
According to Turing,
a Turing machine can carry
out certain elementary operations: It can rewrite a 0 on its tape as
a 1, it can rewrite a 1 on its tape as a 0, it can shift the tape
1 square to the left, or it can shift the tape 1 square to the right.
It is controlled by a program of instruction and each instruction
specifies a condition and an action to be carried out if the condition
is satisfied.
That is the standard definition of computation, but,
taken literally, it is at least
a bit misleading.
If you open up your home computer you are most unlikely to find any
0's and 1's
or even a tape.
But this does not really matter for the definition.
To find out if an object is really a digital computer, it
turns out that we do not actually have to look for 0's and 1's, etc.;
rather we just have to look for something that we could
treat as or
count as or
could be used to
function as a 0's and 1's. Furthermore,
to make the matter more puzzling, it turns out that this machine could
be made out of just about
anything. As Johnson-Laird says, "It could
be made out of cogs and levers like an old fashioned mechanical
calculator; it could be made out of a hydraulic system through
which water flows; it could be made out of transistors
etched into a silicon chip through which electric
current flows; it could even be carried
out by the brain. Each of these machines uses a different medium
to represent
binary symbols. The positions of cogs, the presence or absence of
water, the level of the voltage and perhaps nerve impulses" (Johnson
Laird, 1988, p. 39).
Similar remarks are made by most of the people who write
on this topic. For example, Ned Block (Block, 1990),
shows how we can have electrical gates where the 1's and
0's are assigned to voltage levels of 4 volts and 7 volts respectively.
So we might think that we should go and look for voltage levels.
But Block tells us that 1 is only "conventionally"
assigned to a certain voltage level. The situation grows more
puzzling when he informs us further that we did not need
to use electricity at all
but we could have used
an elaborate system of cats and mice and cheese and
make our gates in such as way that the cat will strain at the leash
and pull open a gate which we can also treat as if it were a 0 or 1.
The point, as Block is anxious to insist,
is "the irrelevance of
hardware realization to computational description.
These gates work in
different ways but they are nonetheless computationally
equivalent" (p. 260).
In the same vein, Pylyshyn says that a computational sequence
could be realized by "a group of pigeons trained to peck
as a Turing machine!"( Pylyshn,1985,p.57)
But now if we are trying to take seriously the idea that the
brain is a digital computer, we get the uncomfortable result that
we could make a system that does just what the
brain does out of pretty much anything.
Computationally speaking, on this view,
you can make a "brain" that functions just
like yours and mine out of cats and mice and cheese or levers or
water pipes or pigeons or anything else provided the two systems are,
in Block's sense,
"computationally equivalent" .
You would
just need an awful lot of cats, or pigeons
or waterpipes, or whatever it
might be.
The proponents of Cognitivism report this result with
sheer and unconcealed delight. But I think they ought to be
worried about it,
and I am going to try to show that
it is just the tip of a whole iceberg of problems.
IV. First Difficulty: Syntax is not Intrinsic to Physics.
Why are the defenders of computationalism not worried by the
implications of multiple realizability?
The answer is that they think it is typical of functional accounts
that the same function admits of multiple realizations.
In this respect, computers are just like carburettors
and thermostats.
Just as carburettors can be made of brass or steel, so computers can
be made of an indefinite range of hardware materials.
But there is a difference: The classes of carburettors and
thermostats are defined in terms of the production of certain
physical effects.
That is why, for example, nobody says you can make carburettors
out of pigeons. But the class of computers is defined syntactically
in terms of the
assignment of 0's and 1's.
The multiple realizability is a
consequence not of the fact that the
same physical effect can
be achieved in different physical substances,
but that the relevant properties are purely syntactical.
The physics is irrelevant except in so far as it admits of the
assignments of 0's and 1's and of state transitions between them.
But this has two consequences which might be disastrous:
- The same principle that implies multiple realizability would seem
to imply
universal realizability. If computation is defined in terms of
the assignment of syntax then everything would be a digital computer,
because any object whatever could have syntactical ascriptions made to
it.
You could describe anything in terms of 0's and 1's.
- Worse yet, syntax is not intrinsic to physics.
The ascription of syntactical properties is always relative to
an agent or observer who treats certain physical phenomena as
syntactical.
Now why exactly would these consequences be disastrous?
Well, we wanted to know how the brain works, specifically how
it produces mental phenomena.
And it would not answer that question to be told that the
brain is a digital computer in the sense in which stomach , liver,
heart, solar system , and the state of Kansas are all digital computers.
The model we had was that we might discover some fact about the
operation of the brain which would show that it is a computer.
We wanted to know if there
was not some sense in which brains were
intrinsically digital computers in a way that green leaves
intrinsically perform photosynthesis
or hearts intrinsically pump blood. It is not a matter of us
arbitrarily or "conventionally" assigning the word "pump" to hearts
or "photosynthesis"
to leaves. There is an actual fact of the matter. And what we
were asking is, "Is there in that
way a fact of the matter about brains that would
make them digital computers?"
It does not answer that question to be told, yes, brains are
digital computers because everything is a digital computer.
On the standard textbook definition of computation,
- For any object there is some description of that object
such that under that description the object is a digital computer.
- For any program there is some sufficiently complex object such
that there is some description of the object under which it is
implementing the program. Thus for example the wall behind my back
is right now implementing the Wordstar program, because there is
some pattern of molecule movements which is isomorphic with the
formal structure of Wordstar. But if the wall is implementing
Wordstar then if it is a big enough wall it is implementing any
program, including any program implemented in the brain.
I think the main reason
that the proponents do not see that multiple or universal
realizability is a problem is that they do not see it as a consequence
of a much deeper point, namely that the "syntax" is not
the name of a physical feature, like mass or gravity.
On the contrary they talk of "syntactical engines" and
even "semantic engines" as if such talk were like that of
gasoline engines or diesel engines, as if it could be just a plain
matter of fact that the brain or anything else is a syntactical engine.
I think it is probably possible to block the result of universal
realizability by tightening up our definition of computation.
Certainly we ought to respect the fact that programmers and
engineers regard it as a quirk of Turing's original definitions and
not as a real feature of computation. Unpublished works by Brian
Smith , Vinod Goel, and John Batali all suggest that a more realistic
definition of computation will emphasize such features as
the causal relations among
program states, programmability and controllability of the mechanism,
and situatedness in the real world.
But these further restrictions on the definition of computation
are no help in the present discussion because the really
deep problem is that syntax is essentially an observer relative notion.
The multiple realizability of computationally equivalent processes
in different physical media was not just a sign that the
processes were abstract, but that they were not
intrinsic to the system at all. They depended on an interpretation
from outside.
We were looking for some facts of the matter which would make brain
processes computational; but given the way we have defined computation, there never could be any such facts of the matter.
We can't, on the one hand,
say that anything is a digital computer if we can
assign a syntax to it and then suppose there is a factual question
intrinsic to its physical operation whether or not a natural system
such as the brain is a digital
computer.
And if the word "syntax" seems puzzling, the same point can be stated
without it.
That is, someone might claim that the notion of "syntax" and "symbols"
are just a manner of speaking and that what we are really interested
in is the existence of systems with discrete physical phenomena and
state transitions between them. On this view we don't really need
O's and 1's; they are just a convenient shorthand.
But, I believe, this move is no help.
A physical state of a system is a computational state only relative
to the assignment to that state of some computational role, function,
or interpretation.
The same problem arises without 0's and 1's
because notions such as computation, algorithm and program do
not name intrinsic physical features of systems.
Computational states are not
discovered within the physics, they are
assigned to the physics.
This is a different argument from the
Chinese Room Argument and I should have seen it ten years
ago but I did not. The Chinese Room Argument showed
that semantics is not intrinsic to syntax. I am now making the
separate and different point that syntax is not intrinsic to physics.
For the purposes of the original
argument I was simply assuming that
the syntactical characterization
of the computer was unproblematic.
But that is a mistake.
There
is no way you could discover that something is intrinsically
a digital computer because the characterization of it as a digital
computer is
always relative to an observer who assigns a
syntactical interpretation
to the purely physical features of the system.
As applied to
the Language of Thought hypothesis,
this has the consequence that the thesis is incoherent.
There is no
way you could discover that there are, intrinsically,
unknown
sentences in your head
because something is a sentence only relative to some agent or
user who uses it as a sentence.
As applied to the computational model generally,
the characterization of a process as computational is
a characterization of a physical system from
outside; and the identification of the process
as computational does not identify
an intrinsic feature of the physics, it is
essentially an observer relative characterization.
This point has to be understood precisely.
I am not saying there are
a priori
limits on the patterns we could
discover in nature.
We could no doubt discover a pattern of events in my brain that was
isomorphic to the implementation of the vi program on this
computer. But to say that something is
functioning as
a computational process is to say something more than that
a pattern of physical events is occuring. It requires the assignment
of a computational interpretation by some agent. Analogously,
we might discover in nature objects which had the same sort of shape
as chairs and which could therefore be used as chairs; but we could not
discover objects in nature which were functioning as chairs,
except relative to some agents who regarded them
or used them as chairs.
V. Second Difficulty: The Homunculus Fallacy is Endemic to Cognitivism.
So far, we seem to have arrived at a problem. Syntax is not part
of physics. This has the consequence that if computation is defined
syntactically then nothing is
intrinsically a digital computer solely in
virtue of its physical properties.
Is there any way out of this problem? Yes, there is, and
it is a way standardly taken in cognitive science, but
it is out of the frying pan and into the fire. Most
of the works I have seen in the computational theory of
the mind commit some variation on the homunculus fallacy.
The idea always is to treat the brain as if there were some
agent inside it using it to compute with. A typical case
is David Marr(1982) who describes the task of vision as proceeding
from a two-dimensional visual array on the retina to a
three-dimensional description of the
external world as output of the visual system.
The difficulty is: Who is reading the description?
Indeed, it looks throughout Marr's book, and in other standard
works on the subject, as if we have to invoke a homunculus
inside the system in order to treat its operations as
genuinely computational.
Many writers feel that the homunculus fallacy
is not really a problem, because, with Dennett (1978), they feel
that the homunculus can be "discharged". The idea is
this: Since the computational operations of the computer
can be analyzed into progressively simpler units, until
eventually we reach simple flip-flop, "yes-no",
"1-0" patterns, it seems that the higher-level
homunculi can be discharged with progressively
stupider homunculi, until finally we reach the
bottom level of a simple flip-flop that involves
no real homunculus at all. The idea, in short, is
that recursive decomposition will eliminate the homunculi.
It took me a long time to figure out what these
people were driving at, so in case someone else is
similarly puzzled I will explain an example in detail:
Suppose that we have a computer that multiplies six times
eight to get forty-eight. Now we ask "How does it do it?"
Well, the answer might be that it adds six to itself
seven times.\**
But if you ask "How does it add six to itself
seven times?", the answer might be that, first, it converts
all of the numerals into binary notation, and second,
it applies a simple algorithm for operating on
binary notation until finally we reach the bottom level
at which the only instructions are of the form,
"Print a zero, erase a one."
So, for example, at the top level our intelligent
homunculus says "I know how to multiply six times eight
to get forty-eight". But at the next lower-level he is replaced
by a stupider homunculus who says "I do not actually
know how to do multiplication, but I can do addition."
Below him are some stupider ones who say "We do not actually know
how to do addition or multiplication, but we know how to convert decimal to
binary." Below these are stupider ones who say "We do not
know anything about any of this stuff, but we know how
to operate on binary symbols."
At the bottom level are a whole bunch of a
homunculi who just say "Zero one, zero one".
All of the higher levels reduce to this bottom level.
Only the bottom level really exists; the top levels are all just
as-if.
Various authors (e.g. Haugeland (1981), Block (1990)) describe this
feature when they say that the system is a syntactical
engine driving a semantic engine. But we still must face
the question we
had before: What facts intrinsic
to the system
make it syntactical? What facts about the bottom level
or any other level make these operations into zeros and
ones?
Without a homunculus that stands outside the recursive decomposition,
we do not even have a syntax to operate with
The attempt to eliminate the homunculus fallacy through recursive
decomposition fails, because the only way to get the syntax intrinsic
to the physics is to put a homunculus in the physics.
There is a fascinating feature to all of this. Cognitivists
cheerfully concede that the higher levels of computation , e.g.
"multiply 6 times 8" are observer relative; there is nothing
really there that corresponds
directly to multiplication;
it is all in the eye of the homunculus/beholder.
But they want to stop this concession at the the lower levels.
The electronic circuit , they admit,
does not really multiply 6X8 as such,
but it really does manipulate 0's and 1's and these manipulations,
so to speak, add up to multiplication.
But to concede that
the higher levels of computation
are not intrinsic to the physics is already to concede
that the lower levels are not intrinsic either.
So the homunculus fallacy is still with us.
For real computers of the kind you buy in the store, there
is no homunculus problem, each user is the homunculus
in question.
But if we are to suppose that the brain is a digital computer,
we are still faced with the question "And who is the user?"
Typical homunculus questions in cognitive science are such as
the following: "How does the visual system compute shape from
shading; how does it compute
object distance from size of retinal image?" A
parallel question would be, "How do nails compute the distance they
are to travel in the board from the impact of the hammer and the
density of the wood?"
And the answer is the same in both sorts of case:
If we are talking about how the system works intrinsically
neither nails nor visual systems compute anything.
We as outside homunculi might describe them computationally, and it
is often useful to do so. But you do not understand
hammering by supposing that nails are somehow intrinsically
implementing
hammering algorithms and you do not understand vision
by supposing the system is implementing, e.g, the shape from
shading alogorithm.
VI. Third Difficulty: Syntax Has No Causal Powers.
Certain sorts of explanations in the
natural sciences
specify mechanisms which function causally in the production
of the phenomena to be explained.
This is especially common in the biological sciences.
Think of the germ theory of disease, the account of photosynthesis,
the DNA theory of inherited traits, and even the Darwinian theory of
natural selection.
In each case a causal mechanism is specified, and
in each case the specification gives an explanation of the
output of the mechanism.
Now if you go back and look at the Primal Story it seems clear
that this is the sort of explanation promised by Cognitivism.
The mechanisms by which brain processes produce
cognition are supposed to be computational,
and by specifying the programs
we will have specified the causes of cognition.
One beauty of this research program, often remarked, is that we do
not need to know the details of brain functioning in order to
explain cognition. Brain processes provide only the hardware
implementation of the cognitive programs, but the program level
is where the real cognitive explanations are given.
On the standard account, as stated by Newell for
example, there are three levels of explanation,
hardware, program, and intentionality( Newell calls this last level,
the knowledge level), and the special contribution of cognitive
science is made at the program level.
But if what I have said so far is correct, then there is something
fishy about this whole project.
I used to believe that as a causal account the
cognitivist's theory was at least false; but I now
am having difficulty formulating a version of it that is coherent
even to the point where it could be an empirical thesis at all.
The thesis is that there are a whole lot of symbols being manipulated
in the brain, 0's and 1's flashing through the brain at
lightning speed and invisible not only to the naked eye but even
to the most powerful electron microscope, and it is these which cause
cognition.
But the difficulty is that
the 0's and 1's as such have no causal powers at all
because they do not even exist except in the eyes of the beholder.
The implemented program has no causal powers other
than those of the implementing medium because the program has no
real existence, no ontology, beyond that of the implementing medium.
Physically speaking there is no such thing as a separate "program
level".
You can see this if you go back to the
Primal Story and remind yourself of the difference between
the mechanical computer and
Turing's human computer.
In Turing's human computer there really is
a program level intrinsic to the system and
it is functioning causally at that level to
convert input to output. This is because
the human is consciously following the
rules for doing a certain computation, and this causally explains
his performance. But when we program the mechanical
computer to peform the same computation, the assignment
of a computational interpretation is now relative to us, the
outside homunculi. And there is no longer a level of intentional
causation intrinsic to the system.
The human computer is consciously following
rules, and this fact explains his behavior, but the mechanical computer
is not literally following any rules at all. It is designed
to behave exactly as if it were following rules, and so for
practical, commercial purposes it does not matter.
Now Cognitivism tells us that the brain functions like the commercial
computer and this causes cognition.
But without a homunculus, both commercial computer and brain have
only patterns and the patterns have no causal powers in addition
to those of the implementing media. So it seems there is
no way Cognitivism
could
give a causal account of cognition.
However there is a puzzle for my view.
Anyone who works with computers even casually knows
that we often do in fact give causal explanations that appeal to
the program.
For example,
we can say that
when I hit this key I got such and such results because the
machine is implementing the vi program and not the emacs
program; and this
looks like an ordinary causal explanation.
So the puzzle is, how do we
reconcile the fact that syntax, as such, has no causal powers with
the fact that we do give causal explanations that appeal to programs?
And, more pressingly, would these sorts of explanations provide
an appropriate model for Cognitivism, will they rescue Cognitivism?
Could we for example rescue the analogy with thermostats by pointing out
that just as the notion "thermostat" figures in causal explanations
independently of any reference to the physics of its implementation,
so the notion "program", might be explanatory while
equally independent of the physics.
To explore this puzzle let us try to make the case for
Cognitivism by extending the Primal Story to show how
the Cognitivist investigative procedures work
in actual research practice. The idea,
typically, is to program a commercial computer so that it
simulates some cognitive capacity, such as vision or language.
Then, if we
get a good simulation, one that gives us at least
Turing equivalence, we hypothesize that the brain
computer is running the same program as the commercial
computer, and to test the hypothesis we
look for indirect psychological evidence, such as reaction
times. So it seems that
we can causally explain the behavior of the brain computer by
citing the program in exactly the same sense in which
we can explain the behavior of the commerical computer. Now what
is wrong with that? Doesn't it sound like a perfectly legitimate
scientific research program?
We know that the commercial computer's conversion of input to
output is explained by a program, and in the brain we discover
the same program, hence we have a causal explanation.
Two things ought to worry us immediately about this
project. First, we would
never accept this mode of explanation for any function of the
brain where we actually understood how it worked at the
neurobiological level.
Second we would not accept it
for other sorts of system that we can simulate computationally.
To illustrate the first point, consider for example
the famous account of "What the Frog's Eye
Tells the Frogs Brain" (Lettvin, et al. 1959 in McCulloch, 1965)
The account is given entirely in terms of the anatomy and
physiology of the frog's nervous system. A typical passage,
chosen at random goes like this:
"1. Sustained Contrast Detectors.
An unmyelinated axon of this group does not respond
when the general illumination is turned on or off. If the sharp
edge of an object either lighter or darker than the background
moves into its field and stops, it discharges promptly and continues
discharging, no matter what the shape of the edge or whether
the object is smaller or larger than the receptive field."(p. 239).
I have never heard anyone say that all this is just
the hardware implementation, and that they
should have figured out which program the frog was implementing.
I do not doubt that you could do a computer simulation of the
frog's "bug detectors". Perhaps someone has done it. But we
all know that once you understand how the frog's visual system
actually works, the "computational level" is just irrelevant.
To illustrate the second point,
consider simulations of other sorts of systems.
I am for example typing these words
on a machine that simulates the behavior of an
old fashioned mechanical typewriter.\**
As simulations go, the word processing program simulates a typewriter
better
than any AI program I know of simulates the brain.
But no sane person thinks: "At long last we understand how
typewriters work, they are implementations of word processing
programs."
It is simply not the case in general that computational
simulations provide causal explanations of the
phenomena simulated.
So what is going on?
We do not in
general suppose that computational simulations of brain processes
give us any explanations
in place of or in addition to neurobiological accounts
of how the brain actually works.
And we do not in general take "X is a computational
simulation of Y" to name a symmetrical relation. That is, we do
not suppose that because the computer simulates a typewriter that
therefore the typewriter simulates a computer.
We do not suppose that
because a weather program simulates a hurricane, that the causal
explanation of the behavior of the hurricane is provided by the
program.
So why should we make an exception to these
principles where unknown brain processes are concerned? Are there
any good grounds for making the exception?
And what kind of a causal explanation is an explanation that
cites a formal program?
Here, I believe, is the solution to our puzzle. Once you remove the
homunculus from the system, you are left only with a pattern of
events to which someone from outside could attach a computational
interpretation. Now the only sense in which the specification of
the pattern by itself provides a causal explanation is that if you know that
a certain pattern exists in a system you know that some cause or
other is responsible for the pattern. So you can, for example,
predict later
stages from earlier stages.
Furthermore, if you already know that the system
has been programmed by an outside homunculus you can give
explanations that make reference to the intentionality of the
homunculus. You can say, e.g. this machine behaves the way it
does because it is running vi.
That is like explaining that this book begins with a bit
about happy families and does not contain any long passages about
a bunch of brothers, because it is Tolstoy's
Anna Karenina
and not
Dostoevsky's
The Brothers Karamozov.
But you cannot explain a physical system such as a typewriter or
a brain
by identifying a pattern which it shares with its
computational simulation, because the existence of the pattern does
not explain how the system actually works
as a physical system.
In the case
of cognition the pattern is at much too high
a level of abstraction to explain such concrete
mental (and therefore physical) events as the
occurrence of a visual perception or the understanding
of a sentence.
Now, I think it is obvious that we cannot
explain how typewriters and hurricanes
work by pointing to formal patterns they
share with their computational simulations. Why is it not obvious
in the case of the brain?
Here we come to the second part of our solution to the puzzle.
In making the case for Cognitivism we were tacitly supposing that
the brain might be implementing algorithms for cognition,
in the same sense that Turing's human computer and his mechanical
computer implement algorithms.
But it is precisely that assumption which we have seen to be
mistaken.
To see this ask yourself what happens when a system implements an
algorithm. In the human computer the system consciously goes through
the steps of the algorithm, so the process is both causal and
logical; logical, because the algorithm provides a set of rules for
deriving the output symbols from the input symbols; causal, because
the agent is making a conscious effort to go through the steps.
Similarly in the case of the mechanical computer the whole system
includes an outside homunculus, and with the homunculus the system
is both causal and logical; logical because the homunculus provides
an interpretation to the the processes of the machine; and causal
because the hardware of the machine causes it to go through
the processes. But these conditions cannot be met by the brute,
blind, nonconscious neurophysiological operations of the brain.
In the brain computer
there is no conscious intentional implementation of
the algorithm as there is in the human computer, but there can't
be any nonconscious implementation as there is in the mechanical
computer either, because that requires an outside homunculus
to attach a computational interpretation to the physical events.
The most we could find in the brain
is a pattern of events which is formally
similar to the implemented program in the mechanical computer,
but that pattern, as such, has no causal powers to call its own and
hence explains nothing.
In sum, the fact that the attribution of
syntax identifies no further causal powers is fatal to
the claim that programs provide causal explanations of
cognition.
To explore the consequences of this let us remind ourselves
of what Cognitivist explanations actually look like. Explanations
such as Chomsky's account of the syntax of natural
languages or Marr's account of vision
proceed by stating a set of rules
according to which a symbolic
input is converted into a symbolic output. In
Chomsky's case, for example, a single input symbol, S, is converted
into any one of a potentially infinite number of sentences by the
repeated application of a set of syntactical rules. In Marr's case,
representations of a two dimensional visual array are converted
into three dimensional "descriptions" of the world in accordance
with certain algorithms. Marr's tripartite distinction between
the computational task, the algorithmic solution of the task and the
hardware implementation of the algorithm, has (like Newell's
distinctions) become famous as
a statement of the general pattern of the explanation.
If you take these explanations naively, as I do, it is best to
think of them as saying that it is just as if a man alone in a
room were going through a set of steps of following rules to
generate English sentences or 3D descriptions, as the case might be.
But now, let us ask what facts in the real world are supposed to correspond
to these explanations as applied to the brain.
In Chomsky's case, for example we are not supposed
to think that the agent consciously goes through a set of
repeated applications of rules;
nor are we supposed to think that he is unconsciouly thinking his
way through the set of rules. Rather the rules are "computational"
and the brain is carrying out the computations. But what does that
mean? Well, we are supposed to think that it is just like a
commercial computer. The sort of thing that corresponds to the
ascription of the same set of rules to a commercial computer is
supposed to correspond to the ascription of those rules to the brain.
But we have seen that in the commercial computer the ascription is
always observer relative, the ascription is made relative to a
homunculus who assigns computational interpretations to the
hardware states. Without the homunculus there is no computation,
just an electronic circuit. So how do we get computation into the
brain without a homunculus? As far as I know neither Chomsky nor
Marr ever addressed the question or even thought there was such
a question.
But without a homunculus there is no explanatory power
to the postulation of the program states.
There is just a physical mechanism, the brain, with its various
real physical and physical/mental causal levels of description.
VII. Fourth Difficulty: The Brain Does Not Do Information Processing.
In this section I turn finally to what I think is, in some ways, the central issue
in all of this, the issue of information processing.
Many people in the "cognitive science"
scientific paradigm will feel that much of
my discussion is simply irrelevant and they will argue against it as
follows:
"There is a difference between the brain and all of these other systems you have been
describing and this difference explains why a computational
simulation in the case of the other systems is a mere simulation
whereas in the case of the brain a computational simulation
is actually duplicating and not merely modeling the functional
properties of the brain. The reason is that
the brain, unlike these other systems, is an
information processing
system. And this fact about the brain is, in your words,
"intrinsic". It is just a fact about
biology that the brain functions to process information,
and since we can also process the same information computationally,
computational models of brain processes have
a different role altogether from
computational models of, for example, the weather.
So there is a well defined research question: "Are
the computational
procedures by which the brain processes information the same
as the procedures by which computers process the same information?"
What I just imagined an opponent saying
embodies one of the worst mistakes in cognitive science. The mistake is
to suppose that in the sense in which computers are used to
process information, brains also process information.
To see that that is a mistake contrast what goes on in the computer with
what goes on in the brain.
In the case of the computer, an outside agent encodes some information
in a form that can be processed by the circuitry of the computer. That
is, he or she provides a syntactical realization of the information
that the computer can implement in, for example, different voltage
levels.
The computer then goes through a series of electrical stages
that the outside agent can interpret both syntactically and semantically
even though, of course, the hardware
has no intrinsic syntax or semantics:
It is all in the eye of the beholder. And the physics does not matter
provided only that you can get it to implement the algorithm.
Finally, an output is produced in the form of physical
phenomena which an observer can interpret as
symbols with a syntax and a semantics.
But now contrast that with the brain.
In the case of the brain, none of the relevant neurobiological
processes are observer
relative (though of course, like anything they can be described
from an observer relative point of view) and the specificity of
the neurophysiology matters desperately.
To make this difference
clear, let us go through an example. Suppose
I see a car coming toward me. A standard computational model of vision
will take in information about the visual array on my retina and
eventually print out the
sentence, "There is a car coming toward me". But that is not what
happens in the actual biology. In the biology
a concrete and specific series of electro-chemical reactions
are set up by the assault of the photons on the photo receptor cells
of my retina, and this entire process eventually results in a concrete
visual experience. The biological reality is not that of a bunch
of words or symbols
being produced by the visual system, rather it is a matter of
a concrete specific conscious visual event; this very visual experience.
Now that concrete visual event is as specific and as concrete as
a hurricane or the digestion of a meal. We can, with the computer, do an
information processing model of that event or of its production,
as we can do
an information model of the weather, digestion or any other
phenomenon, but the phenomena themselves are not thereby
information processing systems.
In short, the sense of information processing
that is used in cognitive science,
is at much too high a level of abstraction to capture the concrete
biological reality of intrinsic intentionality.
The "information" in the brain is always specific to some modality
or other. It is specific to thought, or vision, or hearing, or touch,
for example. The level of information processing which is described
in the cognitive science computational models of cognition , on the
other hand, is simply a matter of getting a set of symbols as output
in response to a set of symbols as input.
We are blinded to this difference by the fact that the same sentence,
"I see a car coming toward me", can be used to record both the
visual intentionality and the output of the computational model of
vision. But this should not obscure from us the fact that the
visual experience is a concrete event and is produced in the brain
by specific electro-chemical biological processes.
To confuse these events and processes
with formal symbol manipulation is to confuse the reality with the
model.
The upshot of this part of the discussion is that in the sense
of "information" used in cognitive science
it is simply false to say
that the brain is an information processing device.
VI. Summary of the Argument.
This brief argument has a simple
logical structure and I will lay it out:
- On the standard textbook definition, computation is defined
syntactically in terms of symbol manipulation.
- But syntax and symbols are not defined in terms of physics.
Though symbol tokens are always physical tokens, "symbol"
and "same symbol" are not defined in terms of physical features.
Syntax, in short,
is not intrinsic to physics.
- This has the consequence that
computation is not discovered in the physics, it is assigned to it.
Certain physical phenomena are assigned or used or
programmed or interpreted syntactically.
Syntax and symbols
are observer relative.
- It follows that you could not
discover
that the brain or anything else
was intrinsically a digital computer, although
you could assign a computational interpretation to it
as you could to anything else.
The point is not that the claim "The brain is a digital computer"
is false. Rather it does not get up to the level of falsehood. It
does not have a clear sense.
You will have misunderstood my account if you think
that I am arguing that it is simply false that the brain is a digital
computer. The question "Is the brain a digital computer?"
is as ill defined as the questions "Is it an abacus?",
"Is it a book?", or "Is it a set of symbols?",
"Is it a set of mathematical formulae?"
- Some physical systems facilitate the
computational use much better than
others. That is why we build, program, and use them.
In such cases we are the homunculus in the system
interpreting the physics in both syntactical and semantic terms.
- But the causal explanations we then give do not cite
causal properties different from the physics of the implementation
and the intentionality of the homunculus.
- The standard, though tacit, way out of this is to commit
the homunculus fallacy.
The humunculus fallacy is endemic to computational models of
cognition and cannot be removed by the standard recursive
decomposition arguments. They are addressed to a different question.
- We cannot avoid the foregoing results
by supposing that the brain is
doing "information processing". The brain, as far as its intrinsic
operations are concerned, does no information processing.
It is a specific biological organ and its specific neurobiological
processes cause specific forms of intentionality. In the brain,
intrinsically, there are neurobiological processes and sometimes
they cause consciousness. But that is the end of the story.\**
References
Block, Ned (1990, forthcoming).
"The Computer Model of the Mind".
Boolos, George
S. and Jeffrey, Richard C. (1989).
Computability and Logic. Cambridge University Press,
Cambridge Mass.
Dennett, Daniel C. (1978).
Brainstorms: Philosophical Essays on Mind and Psychology.
MIT Press, Cambridge, Mass.
Feigenbaum, E.A. and Feldman, J. ed. (1963).
Computers and Thought.
McGraw-Hill Company, New York and San Francisco.
Fodor, J. (1975)
The Language of Thought. Thomas Y. Crowell,
New York.
Haugeland, John ed. (1981).
Mind Design.
MIT Press, Cambridge, Mass.
Johnson-Laird, P. N. (1988).
The Computer and the Mind. Harvard University Press,
Cambridge Mass.
Kuffler, Stephen W. and Nicholls, John G. (1976).
From Neuron to Brain. Sinauer Associates, Sunderland, Mass.
Lettvin, J.Y., Maturana, H.R., McCulloch, W.S., and Pitts, W.H. (1959).
What the Frog's Eye Tells the Frog's Brain.
Proceedings of the Institute of Radio Engineers, 47, 1940-1951,
reprinted in McCulloch, 1965, pp.230-255.
Marr, David (1982).
Vision.
W.H. Freeman and Company, San Francisco.
McCulloch, Warren S. (1965).
The Embodiments of Mind.
MIT Press, Cambridge, Mass.
Pylyshyn (1985).
Computation and Cognition.
MIT Press, Cambridge, Mass.
Searle, John R. (1980). "Minds, Brains and Programs",
The Behavioral and Brain Sciences.3, pp. 417-424.
Searle, John R. (1984).
Minds, Brains and Science.
Harvard University Press, Cambridge, Mass.
Sharples, M., Hogg, D., Hutchinson, C., Torrance, S., and Young, D. (1988).
Computers and Thought. MIT Press, Cambridge, Mass. and London.
Shepherd, Gordon M. (1983).
Neurobiology.
Oxford University Press, New York and Oxford.
Turing, Alan (1950). "Computing Machinery and Intelligence."
Mind, 59,
433-460.
