Journal of Philosophy, Inc.
What Might Cognition Be, If Not Computation?
Author(s): Tim Van Gelder
Source: The Journal of Philosophy, Vol. 92, No. 7 (Jul., 1995), pp. 345-381
Published by: Journal of Philosophy, Inc.
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THE JOURNAL OF PHILOSOPHY
VOLUME XCI, NO. 7,JULY1995
-----------+-•-+___________
WHAT MIGHT COGNITION BE, IF NOT COMPUTATION?*
What is cognition? Contemporary orthodoxy maintains that
it is computation: the mind is a special kind of computer,
and cognitive processes are the rule-governed manipulation
of internal symbolic representations. This broad idea has dominated
the philosophy and the rhetoric of cognitive science-and
even, to a large extent, its practice-ever since the field emerged
from the postwar cybernetic melee. It has provided the general
framework for much of the most well-developed and insightful research
into the nature of mental operation. Yet, over the last decade
or more, the computational vision has lost much of its lustre.
Although work within it continues apace, a variety of difficulties and
limitations have become increasingly apparent, and researchers
across cognitive science and related disciplines have been casting
around for other ways to understand cognitive processes. Partly as a
result, there are now many research programs which, one way or another,
stand opposed to the traditional computational approach;
these include connectionism, neurocomputational approaches, ecological
psychology, situated robotics, synergetics, and artificial life.
These approaches appear to offer a variety of differing and even
conflicting conceptions of the nature of cognition. It is therefore an
appropriate time to step back and reconsider the question: What
general arguments are there in favor of the idea that cognitive
processes must be specifically computational in nature? In order prop*
Criticism and advice from numerous people helped improve this paper, but
special acknowledgement is due to Robert Port, John Haugeland, and James
Townsend. Audiences at the University of Illinois/Chicago, the New Mexico State
University, Indiana University, the Australian National University, the University of
New South Wales, Princeton University, Lehigh University, and the University of
Skiivde were suitably and helpfully critical of earlier versions.
0022-362X/95/9207/345-81 © 1995 TheJournal of Philosophy, Inc.
345
346 TIIEJOURNAL OF PHILOSOPHY
erly to address this question, however, we must first address another:
What are the alternatives? What could cognition be, if it were not
computation of some form or other?
There are at least two reasons why this second question is important.
First, arguments in favor of some broad hypothesis are rarely, if
ever, completely general. They tend to be arguments not for A
alone, but rather in favor of A as opposed to B, and such arguments
often fail to support A as opposed to C. For ex(l.mple, one of the
most powerful early comiderations raised in favor of the computational
conception of cognition was the idea that intelligent behavior
requires sophisticated internal representations. While this clearly
supported the computational conception against a behaviorism
which eschewed such resources, however, it was no use against a connectionism
which helped itself to internal representations, though
rather different in kind than the standard symbolic variety.
The second reason we need to ask what alternatives there may be
is that one of the most influential arguments in favor of the computational
view is the claim that there is simply no alternative. This is
sometimes known as the "what else could it be?" argument.1 As Allen
Newell2 recently put it:
...although a small chance exists that we will see a new paradigm
emerge for mind, it seems unlikely to me. Basically, there do not seem
to be any viable alternatives. This position is not surprising. In lots of
sciences we end up where there are no major alternatives around to the
particular theories we have. Then, all the interesting kinds of scientific
action occur inside the major view. It seems to me that we are getting
rather close to that situation with respect to the computational theory
of mind (ibid., p. 56).
This paper describes a viable alternative. Rather than computers,
cognitive systems may be dynamical systems; rather than computation,
cognitive processes may be state-space evolution within these
very different kinds of systems. It thus disarms the "what else could it
be?" argument, and advances the broader project of evaluating competing
hypotheses concerning the nature of cognition. Note that
achieving these goals does not require decisively establishing that
the dynamical hypothesis is true. That would require considerably
more space than is available here, and to attempt it now would be
hopelessly premature anyway. All that must be done is to describe
1 This title may have been first used in print byJohn Haugeland in 'The Nature
and Plausibility ofCognitivism," Behavioral and Brain Sciences, I (1978): 215-26.
2 "Are There Alternatives?" in W. Sieg, ed., Acting and Reflecting (Boston: Kluwer,
1990).
WHAT MIGHT COGNITION BE, IF NOT COMPUTATION? 347
and motivate the dynamical conception sufficiently to show that it
does in fact amount to an alternative conception of cognition, and
one which is currently viable, as far as we can now tell.
A fruitful way to present the dynamical conception is to begin with
an unusual detour, via the early industrial revolution in England,
circa 1788.
I. IBE GOVERNING PROBLEM
A central engineering challenge for the industrial revolution was to
find a source of power that was reliable, smooth, and uniform. In the
latter half of the eighteenth century, this had become the problem
of translating the oscillating action of the steam piston into the rotating
motion of a flywheel. In one of history's most significant technological
achievements, Scottish engineer James Watt designed and
patented a gearing system for a rotative engine. Steam power was no
longer limited to pumping; it could be applied to any machinery
that could be driven by a flywheel. The cotton industry was particularly
eager to replace its horses and water wheels with the new engines.
High-quality spinning and weaving required, however, that the
source of power be highly uniform, that is, there should be little or
no variation in the speed of revolution of the main driving flywheel.
This is a problem, since the speed of the flywheel is affected both by
the pressure of the steam from the boilers, and by the total workload
being placed on the engine, and these are constantly fluctuating.
It was clear enough how the speed of the flywheel had to be regulated.
In the pipe carrying steam from the boiler to the piston there
was a throttle valve. The pressure in the piston, and so the speed of
the wheel, could be adjusted by turning this valve. To keep engine
speed uniform, the throttle valve would have to be turned, atjust the
right time and by just the right amount, to cope with changes in
boiler pressure and workload. How was this to be done? The most
obvious solution was to employ a human mechanic to tum the valve
as necessary. This had a number of drawbacks, however: mechanics
required wages, and were often unable to react sufficiently swiftly
and accurately. The industrial revolution thus confronted a second
engineering challenge: design a device which can automatically adjust
the throttle valve so as to maintain uniform speed of the flywheel
despite changes in steam pressure or workload. Such a device is
known as a gavemor.
Difficult engineering problems are often best approached by
breaking the overall task down into simpler subtasks, continuing the
process of decomposition until one can see how to construct devices
that can directly implement the various component tasks. In the case
348 TIIEJOURNAL OF PHILOSOPHY
of the governing problem, the relevant decomposition seems clear.
A change need only be made to the throttle valve if the flywheel is
not currently running at the correct speed. Therefore, the first subtask
must be to measure the speed of the wheel, and the second subtask
must be to calculate whether there is any discrepancy between
the desired speed and the actual speed. If there is no discrepancy,
no change is needed, for the moment at least. If there is a discrepancy,
then the governor must determine by how much the throttle
valve should be adjusted to bring the speed of the wheel to the desired
level. This will depend, of course, on the current steam pressure,
and so the governor must measure the current steam pressure
and then on that basis calculate how much to adjust the valve.
Finally, of course, the valve must be adjusted. This overall sequence
of subtasks must be carried out as often as necessary to keep the
speed of the wheel sufficiently close to the desired speed.
A device that can solve the governing problem would have to carry
out these various subtasks repeatedly in the correct order, and so we
can think of it as obeying the following algorithm:
1. Measure the speed of the flywheel.
2. Compare the actual speed against the desired speed.
3. If there is no discrepancy, return to step 1. Otherwise,
a. measure the current steam pressure;
b. calculate the desired alteration in steam pressure;
c. calculate the necessary throttle valve adjustment.
4. Make the throttle valve adjustment.
Return to step 1.
There must be some physical device capable of actually carrying out
each of these subtasks, and so we can think of the governor as incorporating
a tachometer (for measuring the speed of the wheel); a device
for calculating the speed discrepancy; a steam pressure meter; a
device for calculating the throttle valve adjustment; a throttle valve
adjuster; and some kind of central executive to handle sequencing
of operations. This conceptual breakdown of the components of the
governor may even correspond to its actual breakdown; that is, each
of these components may be implemented by a distinct, dedicated
physical device. The engineering problem would then reduce to the
(presumably much simpler) problem of constructing the various
components and hooking them together so that the whole system
functions in a coherent fashion.
Now, as obvious as this approach now seems, it was not the way the
governing problem was actually solved. For one thing, it presupposes
devices that can swiftly perform some quite complex calculations,
WHAT MIGHT COGNITION BE, IF NOT COMPUTATION? 349
and although some simple calculating devices had been invented in
the seventeenth century, there was certainly nothing available in the
late eighteenth century that could have met the demands of a practical
governor.
The real solution, adapted by Watt from existing windmill technology,
was much more direct and elegant. It consisted of a vertical
spindle geared into the main flywheel so that it rotated at a speed directly
dependent upon that of the flywheel itself (see figure 1).
Attached to the spindle by hinges were two arms, and on the end of
each arm was a metal ball. As the spindle turned, centrifugal force
drove the balls outward and hence upward. By a clever arrangement,
this arm motion was linked directly to the throttle valve. The result
was that as the speed of the main wheel increased, the arms raised,
closing the valve and restricting the flow of steam; as the speed decreased,
the arms fell, opening the valve and allowing more steam to
flow. The engine adopted a constant speed, maintained with extraordinary
swiftness and smoothness in the presence of large fluctuations
in pressure and load.
It is worth emphasizing how remarkably well the centrifugal governor
actually performed its task. This device was not just an engineerFigure
1'
'The Watt centrifugal governor for controlling the speed of a steam enginefrom
J. Farey, A Treatise on the Steam Engine: Historical, Practical, and Descriptive
(London: Longman, Rees, Orme, Brown, and Green, 1827).
350 TIIEJOURNAL OF PHILOSOPHY
ing hack employed because computer technology was unavailable.
Scientific American claimed in 1858 that an American variant of the
basic centrifugal governor, "if not absolutely perfect in its action,
is so nearly so, as to leave in our opinion nothing further to be
desired."
But why should any of this be of any interest in the philosophy of
cognitive science? The answer may become apparent as we examine
a little more closely some of the differences between the two
governors.
II. TWO KINDS OF GOVERNORS
The two governors described in the previous section are patently different
in construction, yet they both solve the same control problem,
and we can assume (for purposes of this discussion) that they both
solve it sufficiently well. Does it follow that, deep down, they are really
the same kind of device, despite superficial differences in construction?
Or are they deeply different, despite their similarity in
overt performance?
It is natural to think of the first governor as a computational device;
one which, as part of its operation computes some result,
namely, the desired change in throttle valve angle. Closer attention
reveals that there is in fact a complex group of properties here, a
group whose elements are worth teasing apart.
Perhaps the most central of the computational governor's distinctive
properties is its dependence on representation. Every aspect of
its operation, as outlined above, deals with representations in some
manner or other. The very first thing it does is measure its environment
(the engine) to obtain a symbolic representation of current engine
speed. It then performs a series of operations on this and other
representations, resulting in an output representation, a symbolic
specification of the alteration to be made in the throttle valve; this
representation then causes the valve adjusting mechanism to make
the corresponding change. This is why it is appropriately described
as computational (now in a somewhat narrower sense): it literally
computes the desired change in throttle valve by manipulating symbols
according to a schedule of rules. Those symbols, in the context
of the device and its situation, have meaning, and the success of the
governor in its task is owed to its symbol manipulations being in systematic
accord with those meanings. The manipulations are discrete
operations which necessarily occur in a determinate sequence; for
example, the appropriate change in the throttle valve can only be
calculated after the discrepancy between current and desired speeds
has been calculated. At the highest level, the whole device operates
WHAT MIGHT COGNITION BE, IF NOT COMPUTATION? 351
in a cyclic fashion: it first measures (or "perceives") its environment;
it then internally computes an appropriate change in throttle valve;
it then effects this change ("acts" on its environment). After the
change has been made and given time to affect engine speed, the
governor runs through whole the cycle again...and again.... Finally,
notice that the governor is homuncular in construction.
Homuncularity is a special kind of breakdown of a system into parts
or components, each of which is responsible for a particular subtask.
Homuncular components are ones that, like departments or committees
within bureaucracies, interact by communication (that is, by
passing meaningful messages). Obviously, the representational and
computational nature of the governor is essential to its homuncular
construction: if the system as a whole did not operate by manipulating
representations, it would not be possible for its components to
interact by communication.
These properties-representation, computation, sequential and
cyclic operation, and homuncularity-form a mutually interdependent
cluster; a device with any one of them will standardly possess
others. Now, the Watt centrifugal governor does not exhibit this cluster
of properties as a whole, nor any one of them individually. As obvious
as this may seem, it deserves a little detailed discussion and
argument, since it often meets resistance, and some useful insights
can be gained along the way.
Since manipulable representations lie at the heart of the computational
picture, the nonrepresentational nature of the centrifugal governor
is a good place to start. There is a common and initially quite
attractive intuition to the effect that the angle at which the arms are
swinging is a representation of the current speed of the engine, and
it is because the arms are related in this way to engine speed that the
governor is able to control that speed. This intuition is misleading,
however; arm angle and engine speed are of course intimately related,
but the relationship is not representational. There are a number
of powerful arguments favoring this conclusion. They are not
based on any unduly restrictive definition of the notion of representation;
they go through on pretty much any reasonable characterization,
based around a core idea of some state of a system which, by
virtue of some general representational scheme, stands in for some
further state of affairs, thereby enabling the system to behave appropriately
with respect to that state of affairs.4
• This broad characterization is adapted from Haugeland, "Representational
Genera," in W. Ramsey, S.P. Stich, D.E. Rumelhart, eds., Philosophy and
Connectionist Theory (Hillsdale, NJ: Erlbaum, 1991), pp. 61-89.
352 1HEJOURNAL OF PHILOSOPHY
A useful criterion of representation-a reliable way of telling
whether a system contains them or not-is to ask whether there is
any explanatory utility in describing the system in representational
terms. If you really can make substantially more sense of how a system
works by concretely describing various identifiable parts or aspects
of it as representations in the above sense, that is the best
evidence you could have that the system really does contain representations.
Conversely, if describing the system as representational
lets you explain nothing over and above what you could explain before,
why on earth suppose it to be so? Note that very often representational
descriptions do yield substantial explanatory benefits. This is
certainly true for pocket calculators, and mainstream cognitive science
is premised on the idea that humans and animals are like that
as well. A noteworthy fact about standard explanations of how the
centrifugal governor works is, however, that they never talk about
representations. This was true for the informal description given
above, which apparently suffices for most readers; more importantly,
it has been true of the much more detailed descriptions offered by
those who have actually been in the business of constructing centrifugal
governors or analyzing their behavior. Thus, for example, a
mechanics manual for construction of governors from the middle of
last century, Maxwell's original dynamical analysis (see below), and
contemporary mathematical treatments all describe the arm angle
and its role in the operation of the governor in nonrepresentational
terms. The reason, one might reasonably conclude, is that the governor
contains no representations.
The temptation to treat the arm angle as a representation comes
from the informal observation that there is some kind of correlation
between arm angle and engine speed; when the engine rotates at a
certain speed, the arms will swing at a given angle. Now, supposing for
the moment that this is an appropriate way to describe their relationship,
it would not follow that the arm angle is a representation. One of
the few points of general agreement in the philosophy of cognitive science
is that mere correlation does not make something a representation.
Virtually everything is correlated, fortuitously or otherwise, with
something else; to describe every correlation as representation is to
trivialize representation. For the arm angle to count, in the context of
the governing system alone, as a representation, we would have to be
told what else about itjustifies the claim that it is a representation.
But to talk of some kind of correlation between arm angle and engine
speed is grossly inadequate, and once this is properly understood,
there is simply no incentive to search for this extra ingredient.
WHAT MIGHT COGNITION BE, IF NOT COMPUTATION? 353
For a start, notice that the correlation at issue only obtains when the
total system has reached its stable equilibrium point, and is immediately
disturbed whenever there is some sudden change in, for example,
the workload on the engine. At such times, the speed of the
engine quickly drops for a short period, while the angle of the arms
adjusts only at the relatively slow pace dictated by gravitational acceleration.
Yet, even as the arms are falling, more steam is entering the
piston, and hence the device is already working; indeed, these are
exactly the times when it is most crucial that the governor work effectively.
Consequently, no simple correlation between arm angle
and engine speed can be the basis of the operation of the governor.
The fourth and deepest reason for supposing that the centrifugal
governor is not representational is that, when we fully understand
the relationship between engine speed and arm angle, we see that
the notion of representation isjust the wrong sort of conceptual tool
to apply. There is no doubt that at all times the arm angle is in some
interesting way related to the speed of the engine. This is the insight
which leads people to suppose that the arm angle is a representation.
Yet appropriately close examination of this dependence shows
exactly why the relationship cannot be one of representation. For
notice that, because the arms are directly linked to the throttle valve,
the angle of the arms is at all times determining the amount of
steam entering the piston, and hence at all times the speed of the
engine depends in some interesting way on the angle of the arms.
Thus, arm angle and engine speed are at all times both determined
by, and determining, each other's behavior. As we shall see below,
there is nothing mysterious about this relationship; it is quite
amenable to mathematical description. Yet it is much more subtle
and complex than the standard concept of representation can handle,
even when construed as broadly as is done here. In order to describe
the relationship between arm angle and engine speed, we
need a more powerful conceptual framework than mere talk of representations.
That framework is the mathematical language of dynamics,
and in that language, the two quantities are said to be
coupled. The real problem with describing the governor as a representational
device, then, is that the relation of representing-something
standing in for some other state of affairs-is too simple to
capture the actual interaction between the governor and the engine.
If the centrifugal governor is not representational, then it cannot
be computational, at least in the specific sense that its processing
cannot be a matter of the rule-governed manipulation of symbolic
representations. Its noncomputational nature can also be established
354 1HEJOURNAL OF PHILOSOPHY
another way. Not only are there no representations to be manipulated,
there are no distinct manipulatings that might count as computational
operations. There are no discrete, identifiable steps in
which one representation gets transformed into another. Rather, the
system's entire operation is smooth and continuous; there is no possibility
of nonarbitrarily dividing its changes over time into distinct
manipulatings, and no point in trying to do so. From this, it follows
that the centrifugal governor is not sequential and not cyclic in its
operation in anything like the manner of the computational governor.
Since there are no distinct processing steps, there can be no sequence
in which those steps occur. There is never any one operation
that must occur before another one can take place. Consequently,
there is nothing cyclical about its operation. The device has, to be
sure, an "input" end (where the spindle is driven by the engine) and
an "output" end (the connection to the throttle valve). But the centrifugal
governor does not follow a cycle where it first takes a measurement,
then computes a throttle valve change, then makes that
adjustment, then takes a measurement, and so on. Rather, input, internal
activity, and output are all happening continuously and at the
very same time, much as a radio is producing music at the very same
time as its antenna is receiving signals.
The fact that the centrifugal governor is not sequential or cyclic in
any respect points to yet another deep difference between the two
kinds of governor. There is an important sense in which time does not
matter in the operation of the computational governor. There is, of
course, the minimal constraint that the device must control the engine
speed adequately, and so individual operations within the device must
be sufficiently fast. There is also the constraint that internal operations
must happen in the right sequence. Beyond these, however, there is
nothing that dictates when each internal operation takes place, how
long it takes to carry it out, and how long elapses between each operation.
There are only pragmatic implementation considerations: which
algorithms to use, what kind of hardware to use to run the algorithms,
and so forth. The timing of the internal operations is thus essentially
arbitrary relative to that of any wider course of events. It is as if the
wheel said to the governing system: "Go away and figure out how much
to change the valve to keep me spinning at 100 rpm. I don't care how
you do it, how many steps you take, or how long you take over each
step, as long as you report back within (say) 10 milliseconds."
In the centrifugal governor, by contrast, there is simply nothing
that is temporally unconstrained in this way. There are no occurrences
whose timing is arbitrary relative to the operation of the en-
WHAT MIGIIT COGNITION BE, IF NOT COMPUTATION? 355
gine. All behavior in the centrifugal governor happens in the very
same real time frame as change in the speed of the fl)Wheel. We can
sum up the point this way: the two kinds of governor differ fundamentally
in their temporality, and the temporality of the centrifugal
governor is essentially that of the engine itself.
Finally, it need hardly be labored that the centrifugal governor is
not a homuncular system. It has parts, to be sure, and its overall behavior
is the direct result of the organized interaction of those parts.
The difference is that those parts are not modules interacting by communication;
they are not like little bureaucratic agents passing representations
among themselves as the system achieves the overall task.
III. CONCEPTUAL FRAMEWORKS
In the previous section, I argued that the differences in nature between
the two governors run much more deeply than the obvious dif
ferences in mechanical construction. Not surprisingly, these
differences in nature are reflected in the kind of conceptual tools that
we must bring to bear ifwe wish to understand the operation of these
devices. That is, the two different governors require very different conceptual
frameworks in order to understand how it is that they function
as governors, that is, how they manage to control their environment.
In the case of the computational governor, the behavior is captured
in all relevant detail by an algorithm, and the general conceptual
framework we are bringing to bear is that of mainstream computer science.
Computer scientists are typically concerned with what you can
achieve by stringing together, in an appropriate order, some set of
basic operations: either how best to string them together to achieve
some particular goal (programming, theory of algorithms), or what is
achievable in principle in this manner (computation theory). So we
understand the computational governor as a device capable of carrying
out some set of basic operations (measurings, subtractings, etc.),
and whose sophisticated overall behavior results from nothing more
than the complex sequencing of these basic operations. Note that
there is a direct correspondence between elements of the governor
(the basic processing steps it goes through) and elements of the algorithm
which describes its operation (the basic instructions).
The Watt centrifugal governor, by contrast, cannot be understood
this way at all. There is nothing in that device for any algorithm to
lock onto. Very different conceptual tools have always been applied
to this device. The terms in which it was described above, and indeed
by Watt and his peers, were straightforwardly mechanical: rotations,
spindles, levers, displacements, forces. Last century, more precise
and powerful descriptions became available, but these also have
356 TIIEJOURNAL OF PHILOSOPHY
nothing to do with computer science. In 1868, the physicistJames
Clerk Maxwell5 made a pioneering extension of the mathematical
tools of dynamics to regulating and governing devices. The general
approach he established has been standard ever since. Though familiar
to physicists and control engineers, it is less so to most cognitive
scientists and philosophers of mind, and hence is worth
describing in a little detail.
The key feature of the governor's behavior is the angle at which the
arms are hanging, for this angle determines how much the throttle
valve is opened or closed. Therefore, in order to understand the b~
havior of the governor, we need to understand the basic principles
governing how arm angle changes over time. Obviously, the arm angle
depends on the speed of the engine; hence we need to understand
change in arm angle as a function of engine speed. Ifwe suppose for
the moment that the link between the governor and the throttle valve
is disconnected, then this change is given by the differential equation:
d
2
(J = (nw) 2 cos(} sin(} - Ksin(} - r d(J
dt2 l dt
where (} is the angle of arms, n is a gearing constant, w is the speed
of engine, gis a constant for gravity, l is the length of the arms, and r
is a constant of friction at hinges.6 This nonlinear, second-order differential
equation tells us the instantaneous acceleration in arm
angle, as a function of what the current arm angle happens to be
(designated by the state variable 8), how fast arm angle is currently
changing (the derivative of(} with respect to time, d(J/dt) and the
current engine speed (w). In other words, the equation tells us how
change in arm angle is changing, depending on the current arm
angle, the way it is changing already, and the engine speed. Note
that in the system defined by this equation, change over time occurs
only in arm angle(} (and its derivatives). The other quantities (w, n,
g, l and r) are assumed to stay fixed, and are called parameters. The
particular values at which the parameters are fixed determine the
precise shape of the change in 8. For this reason, the parameter settings
are said to fix the dynamics of the system.
This differential equation is perfectly general and highly succinct:
it is a way of describing how the governor behaves for any arm angle
and engine speed. This generality and succinctness comes at a price,
however. lfwe happen to know what the current arm angle is, how
fast it is changing, and what the engine speed is, then from this
5 "On Governors," Proceedings ofthe Royal Society, XVI (1868): 270-83.
• Edward Beltrami, MathematicsfarDynamir.alModeling (Boston: Academic, 1987), p. 163.
WHAT MIGIIT COGNITION BE, IF NOT COMPUTATION? 357
equation all we can figure out is the current instantaneous acceleration.
If we want to know at what angle the arms will be in a half-second,
for example, we need to find a solution to the general
equation-that is, an equation that tells us what values (} takes as a
function of time, which satisfies the differential equation. There are
any number of such solutions, corresponding to all the different behavioral
trajectories that the governor might exhibit, but these solutions
often have important general properties in common; thus, as
long as the parameters stay within certain bounds, the arms will always
eventually settle into a particular angle of equilibrium for that
engine speed; that angle is known as a point attrador.
Thus far I have been discussing the governor without taking into
account its effect on the engine, and thereby indirectly on itself.
Here, the situation gets a little more complicated, but the same mathematical
tools apply. Suppose we think of the steam engine itself as a
dynamical system governed by a set of differential equations, one of
which gives us some derivative of engine speed as a function of current
engine speed and a number of other variables and parameters:
dnw
- = F(w, ... , T, ...)
dtn
One of these parameters is the current setting of the throttle valve,
T, which depends directly on the governor arm angle 0. We can thus
think of(} as a parameter of the engine system, just as engine speed
w is a parameter of the governor system. (Alternatively, we can think
of the governor and steam engine as comprising a single dynamical
system in which both arm angle and engine speed are state variables.)
This relationship, known as coupling, is particularly interesting
and subtle. Changing a parameter of a dynamical system changes
its total dynamics (that is, the way its state variables change their values
depending on their current values, across the full range ofvalues
they may take). Thus, any change in engine speed, no matter how
small, changes not the state of the governor directly, but rather the
way the state of the governor changes, and any change in arm angle
changes the way the state of the engine changes. Again, however, the
overall system (coupled engine and governor) settles quickly into a
point attractor, that is, engine speed and arm angle remain constant.
Indeed, the remarkable thing about this coupled system is that
under a wide variety of conditions it always settles swiftly into states at
which the engine is running at a particular speed. This is of course
exactly what is wanted: coupling the governor to the engine results
in the engine running at a constant speed.
358 THEJOURNAL OF PHILOSOPHY
In this discussion, two very broad, closely related sets of conceptual
resources have (in a modest way) been brought into play. The
first is dynamical modeling, that branch of applied mathematics
which attempts to describe change in real-world systems by describing
the states of the system numerically and then writing
equations that capture how these numerical states change over
time. The second set of resources is dynamical systems theory, the
general study of dynamical systems considered as abstract mathematical
structures. Roughly speaking, dynamical modeling attempts
to understand natural phenomena as the behavior of
real-world realizations of abstract dynamical systems, whereas dynamical
systems theory studies the abstract systems themselves.
There is no sharp distinction between these two sets of resources,
and for our purposes they can be lumped together under the general
heading of dynamics.
IV. MORALS
This discussion of the governing task suggests a number of closely related
lessons for cognitive science:
(1) Various different kinds of systems, fundamentally different in nature
and requiring very different conceptual tools for their understanding,
can subsexve sophisticated tasks-including interacting
with a changing environment-which may initially appear to demand
that the system have knowledge of, and reason about, its environment.
The governing problem is one simple example of such a
task; it can be solved either by a computational system or by a noncomputational
dynamical system, the Watt centrifugal governor.
(2) In any given case, our sense that a specific cognitive task must be
subserved by a (generically) computational system may be due to
deceptively compelling preconceptions about how systems solving
complex tasks must work. Many people are oblivious to the possibility
of a noncomputational, dynamical solution to the governing
problem, and so all-too-readily assume that it must be solved in a
computational manner. Likewise, it may be that the basically computational
shape of most mainstream models of cognition results
not so much from the nature of cognition itself as it does from the
shape of the conceptual equipment that cognitive scientists typically
bring to bear in studying cognition.
(3) Cognitive systems may in fact be dynamical systems, and cognition
the behavior of some (noncomputational) dynamical system.
Perhaps, that is, cognitive systems are more relevantly similar to the
centrifugal governor than they are similar either to the computational
governor, or to that more famous exemplar of the broad category
of computational systems, the Turing machine.
WHAT MIGHT COGNITION BE, IF NOT COMPUTATION? 359
In what follows, the first and third of these points will be elaborated
in just enough detail to substantiate the basic claim of this
paper, that there is in fact a currently viable alternative to the
computational conception of cognition. As a first step toward
doing that, however, I shall briefly describe an example of dynamical
research in cognitive science, in order to provide what might
seem to be no more than rank speculation with a little healthy
flesh.
V. AN EXAMPLE OF DYNAMICAL RESEARCH
Consider the process of coming to make a decision between a variety
of options, each of which has attractions and drawbacks.
This is surely a high-level cognitive task, if anything is.
Psychologists have done endless experimental studies determining
how people choose, and produced many mathematical models
attempting to describe and explain their choice behavior. The
dominant approach in modeling stems from the classic expectedutility
theory and statistical decision theory as originally developed
by John von Neumann and Oskar Morgenstern. The basic
idea here is that an agent makes a decision by selecting the option
that has the highest expected utility, which is calculated in
turn by combining some formal measure of the utility of any
given possible outcome with the probability that it will eventuate
if the option is chosen. Much of the work within the classical
framework is mathematically elegant and provides a useful description
of optimal reasoning strategies. As an account of the actual
decisions people reach, however, classical utility theory is
seriously flawed; human subjects typically deviate from its recommendations
in a variety of ways. As a result, many theories incorporating
variations on the classical core have been developed,
typically relaxing certain of its standard assumptions, with varying
degrees of success in matching actual human choice behavior.
Nevertheless, virtually all such theories remain subject to some
further drawbacks:
(1) They do not incorporate any account of the underlying motivations
that give rise to the utility that an object or outcome holds at a given
time.
(2) They conceive of the utilities themselves as static values, and
can offer no good account of how and why they might change
over time, and why preferences are often inconsistent and in-
constant.
(3) They offer no serious account of the deliberation process, with its
attendant vacillations, inconsistencies, and distress; and they have
360 THEJOURNAL OF PHILOSOPHY
nothing to say about the relationships that have been uncovered between
time spent deliberating and the choices eventually made.
Curiously, these drawbacks appear to have a common theme; they all
concern, one way or another, temporal aspects of decision making. It
is worth asking whether they arise because of some deep structural
feature inherent in the whole framework which conceptualizes decision-making
behavior in terms of calculating expected utilities.
Notice that utility-theory based accounts of human decision making
("utility theories") are deeply akin to the computational solution
to the governing task. That is, ifwe take such accounts as notjust describing
the outcome of decision-making behavior, but also as a
guide to the structures and processes that underlie such behavior,7
then there are basic structural similarities to die computational governor.
Thus, utility theories are straightforwardly computational;
they are based on static representations of options, utilities, probabilities,
and so on, and processing is the algorithmically specifiable internal
manipulation of these representations to obtain a final
representation of the choice to be made. Consequently, utility theories
are strictly sequential; they presuppose some initial temporal
stage at which the relevant information about options, likelihoods,
and so on, is acquired; a second stage in which expected utilities are
calculated; and a third stage at which the choice is effected in actual
behavior. And, like the computational governor, they are essentially
atemporal; there are no inherent constraints on the timing of the
various internal operations with respect to each other or change in
the environment.
What we have, in other words, is a model of human cognition
which, on one hand, instantiates the same deep structure as the
computational governor, and on the other, seems structurally incapable
of accounting for certain essentially temporal dimensions of
decision-making behavior. At this stage, we might ask: What kind of
model of decision-making behavior we would get if, rather, we took
the centrifugal governor as a prototype? It would be a model with a
relatively small number of continuous variables influencing each
other in real time. It would be governed by nonlinear differential
equations. And it would be a model in which the agent and the
choice environment, like the governor and the engine, are tightly in-
terlocked.
7 See, for example, J.W. Payne, J.R. Bettman, and E.J. Johnson, "Adaptive
Strategy Selection in Decision Making," Journal offacperimental Psychology: Learning,
Memory, Cognition, XIV (1988): 534-52.
WHAT MIGID COGNITION BE, IF NOT COMPUTATION? 361
It would, in short, be rather like the motivational oscillatory theory
(MOT) modeling framework described by mathematical psychologist
James Townsend.8 MOT enables modeling ofvarious qualitative properties
of the kind of cyclical behaviors that occur when circumstances
offer the possibility of satiation of desires arising from more or less
permanent motivations; an obvious example is regular eating in response
to recurrent natural hunger. It is built around the idea that in
such situations, your underlying motivation, transitory desires with regard
to the object, distance from the object, and consumption of it are
continuously evolving and affecting each other in real time; for example,
if your desire for food is high and you are far from it, you will
move toward it (that is, z changes), which influences your satiation
and so your desire. The framework thus includes variables for the current
state of motivation, satiation, preference, and action (movement),
and a set of differential equations describe how these variables
change over time as a function of the current state of the system.9
•See ''.A Neuroconnectionistic Formulation of Dynamic Decision Field Theory,"
in D. Vickers and P.L. Smith, eds., Human Information Processing: Measures,
Mechanisms, and Models (Amsterdam: North Holland, 1988); and "Don't Be Fazed
by PHASER: Beginning Exploration of a Cyclical Motivational System," Behavior
Research Methods, Instruments and Computers, XXIV (1992): 219-27.
•The equations, with rough and partial translations into English, are:
dx
-=M-m-c
dt
(The change in motivation depends on how the current levels of motivation and of
consumption compare with some standard level of motivation, M.)
- = + 1 ·mdx [ 1 Jdt zi + z~ + a
(The change in one's preference for the goal will depend on current motivation and
one's distance from the object of preference.)
de [ b J- = (x + C- c) •
dt zi + ~ + r
(The change in consumption will depend on the level of preference, the level of
consumption, and the distance from the object of preference.)
dz1
- = -x·z
dt 1
(How one moves toward or away from the object depends on one's current level of
prefe1·ence for the object.) See "Don't Be Fazed by PHASER" for an accessible and
graphic introduction to the behaviors defined by these equations.
362 1HEJOURNAL OF PHILOSOPHY
MOT stands to utility theories in much the same relation as the
centrifugal governor does to the computational governor. In MOT,
cognition is not the manipulation of symbols, but rather state-space
evolution in a dynamical system. MOT models produce behavior
which, if one squints while looking at it, seems like decision making-after
all, the agent will make the move which offers the most reward,
which in this case means moving toward food if sufficiently
hungry. But this is decision making without decisions, so to speak,
for there never are in the model any discrete internal occurrences
that one could characterize as decisions. In this approach, decision
making is better thought of as the behavior of an agent under the influence
of the pushes and pulls that emanate from desirable outcomes,
undesirable outcomes, and internal desires and motivations;
in a quasi-gravitational way, these forces act on the agent with
strength varying as a function of distance.
The MOT modeling framework is a special case of a more general
(and rather more complex) dynamical framework which Townsend
and Jerome Busemeyer10 call "decision field theory." That framework
allows faithful modeling of a wide range of behaviors more easily recognizable
as decision making as studied within the traditional research
paradigm; indeed, their claim is that decision field theory
"covers a broader range of phenomena in greater detail" than classical
utility theories, and even goes beyond them by explaining in a
natural way several important paradoxes of decision making, such as
the so-called "common consequence effect" and "common ratio effect."
The important point for immediate purposes, however, is that
the general decision field theory works on the same fundamental dynamical
principles as MOT. There is thus no question that at least
certain aspects of human high-level cognitive functioning can be
modeled effectively using dynamical systems of the kind that can be
highlighted by reference to the centrifugal governor.
Thus far, all I have done is to use the governing problem as a
means of exploring some of the deep differences between computational
and noncomputational solutions to complex tasks, drawn out
some suggestive implications for cognitive science, and used the
Busemeyer and Townsend work to illustrate the claim that highlevel
cognitive processes can in fact be modeled using noncomputa-
10 "Decision Field Theory: A Dynamic-Cognitive Approach to Decision Making in
an Uncertain Environment," Psychological Review, c (1993): 432-59; an accessible
overview is given in "Dynamic Representation of Decision Making," in R Port and
myself, eds., Mind as Motion: Explorations in the Dynamics of Cognition (Cambridge:
MIT, 1995).