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The Mind as Neural Software? Understanding Functionalism, Computationalism, and Computational Functionalism



Defending or attacking either functionalism or computationalism requires clarity on what they amount to and what evidence counts for or against them. My goal here is not to evaluate their plausibility. My goal is to formulate them and their relationship clearly enough that we can determine which type of evidence is relevant to them. I aim to dispel some sources of confusion that surround functionalism and computationalism, recruit recent philosophical work on mechanisms and computation to shed light on them, and clarify how functionalism and computationalism may or may not legitimately come together.
The Mind as Neural Software?
Understanding Functionalism,
Computationalism, and
Computational Functionalism
gualtiero piccinini
University of Missouri, St. Louis
Defending or attacking either functionalism or computationalism requires clarity
on what they amount to and what evidence counts for or against them. My goal
here is not to evaluate their plausibility. My goal is to formulate them and their
relationship clearly enough that we can determine which type of evidence is rele-
vant to them. I aim to dispel some sources of confusion that surround functional-
ism and computationalism, recruit recent philosophical work on mechanisms and
computation to shed light on them, and clarify how functionalism and computa-
tionalism may or may not legitimately come together.
1. Introduction
Functionalism is forty years old, computationalism is over sixty, and
philosophers often conjoin them. Yet their relationship remains obscure.
With Jerry Fodor, I am struck by ‘‘the widespread failure to distinguish
the computational program in psychology from the functionalist pro-
gram in metaphysics’’ (Fodor 2000, 104). A recent paper by Paul
Churchland (2005) epitomizes such a failure. Churchland argues that
functionalism is false, because the brain is not a classical (i.e., more or
less Turing-machine-like) computing system but a connectionist one.
Thanks to those who commented on this paper at its various and multiple stages,
especially Ken Aizawa, David Chalmers, Robert Cummins, Carl Craver, Chris Elia-
smith, John Heil, Bill Lycan, Peter Machamer, Diego Marconi, Tom Polger, Oron
Shagrir, and Larry Shapiro. Ancestors of this paper were presented at the 2004
Pacific APA and the 2007 SSPP. I thank the audiences and commentators—Matth-
ias Scheutz and Charles Wallis at the APA, Whit Schonbein at the SSPP—for their
feedback. This work was supported in part by a 2006 NEH Summer Seminar at
Washington University in St. Louis and a University of Missouri Research Grant.
The views expressed here do not necessarily reflect those of these institutions.
Philosophy and Phenomenological Research
Vol. LXXXI No. 2, September 2010
2010 Philosophy and Phenomenological Research, LLC
Philosophy and
Phenomenological Research
His argument presupposes that functionalism entails classical computa-
tionalism. But functionalism—properly understood—does not entail
computationalism, whether classical or non-classical.
Assessing functionalism and computationalism requires clarity on
what they amount to and what evidence counts for or against them.
My goal here is not to evaluate their plausibility. My goal is to formu-
late them and their relationship clearly enough that we can determine
which type of evidence is relevant to them. I aim to dispel some sources
of confusion that surround functionalism and computationalism,
recruit recent philosophical work on mechanisms and computation to
shed light on them, and clarify how functionalism and computational-
ism may or may not legitimately come together.
I will frame the discussion in terms of functionalism, because func-
tionalism is the metaphysical doctrine most closely associated (and con-
flated) with computationalism. But one upshot of this paper is that
functionalism and computationalism need not go together. Functional-
ism may be combined with a non-computational theory of mind, and
computationalism may be combined with a non-functionalist metaphys-
ics. Once we understand how functionalism and computationalism
mesh, we can generalize our picture and see how metaphysical doc-
trines other than functionalism may be combined with computational-
ism as well as how theories other than computationalism may be
combined with functionalism.
To a first approximation, functionalism is the view that the mind is
the ‘‘functional organization’’ of the brain, or any other system that is
functionally equivalent to the brain (cf. Putnam 1960, 149; 1967a, 200;
1967b, 32). Another formulation of functionalism is that mental states
are ‘‘functional states’’ (Putnam 1967b, 30).
Putnam’s main example of
a description of functional organization is the machine table of a Tur-
ing machine. For him, a functional organization is a set of functional
states with their functional relations, where a functional state is defined
by its causal relations to inputs, outputs, and other functional states
(under normal conditions). Thus, under Putnam’s notion of functional
organization, our two formulations of functionalism are equivalent.
Under the broader notion of functional organization that I will
defend, there is more to functional organization than individual
In the cited references, Putnam writes ‘‘functional organization of organisms’’ or
‘‘functional organization of the human being’’ rather than ‘‘functional organization
of the brain,’’ as I wrote. Since much of the functional organization of human
beings—or organisms, for that matter—is irrelevant to the mind, I replaced ‘organ-
ism’ and ‘human being’ with ‘brain’. Even so, I doubt that the brain fixes the exact
boundaries of the mind’s realization. For present purposes, it will be convenient to
understand ‘brain’, whenever appropriate, as referring to whatever aspects of the
functional organization of organisms realize the mind according to functionalism.
functional states and their relations. There are also aggregates of states,
components bearing the states, functional properties of the compo-
nents, and relations between the components and their properties. Since
the first formulation of functionalism is more general than the second,
I prefer the first, but nothing in what follows hinges on the difference
between the two.
Stronger or weaker versions of functionalism may be formulated
depending on how much of the mind is taken to be functional—how
many mental states, or which aspects thereof, are functional. Are all
mental states functional, or only some? Are all aspects of mental states
functional, or only, say, their non-phenomenal aspects? How these
questions are answered makes no difference here, because I’m not con-
cerned with the plausibility and scope of functionalism. I’m concerned
with what functionalism amounts to. One question I will address is,
what is functional organization? In due course, I will examine different
notions of functional organization and search for the pertinent one.
Computationalism, for present purposes, is the view that the func-
tional organization of the brain (or any other functionally equivalent
system) is computational, or that neural states are computational
states. Again, stronger or weaker versions of computationalism may be
formulated depending on how much of the functional organization of
the brain is taken to be computational. But again, I will not assess the
plausibility and scope of computationalism here.
Functionalism plus computationalism equals computational function-
alism. In a well-known slogan, computational functionalism says that
the mind is the software of the brain (or any functionally equivalent
system; I will omit this qualification from now on). Taken at face
value, this slogan draws an analogy between the mind and the software
of ordinary, program-controlled computers. But the same slogan is
often understood to suggest, more modestly, that the mind is the com-
putational organization of the brain—or that mental states are compu-
tational states—without the implication that such a computational
organization is that of a program-controlled computer. As we shall see,
the ambiguity between the strong and the weak reading is one source
of confusion in this area.
Computational functionalism has been popular among functionalists
who are sympathetic to computationalist research programs in artificial
intelligence, psychology, and neuroscience. It has also encountered
ferocious resistance.
The present goal, however, is not to determine
The critical literature is vast. Representative examples include Block 1978, Putnam
1988, Searle 1992, and Lucas 1996.
whether the mind is the software of the brain. It is to understand what
this means and what counts as evidence for or against it.
An important caveat. The functionalism I am concerned with des-
cends from some early writings of Hilary Putnam and Jerry Fodor
(Putnam 1960, 1964, 1967a, 1967b; Fodor 1965, 1968a, 1968b). It is a
metaphysics of mind—the main alternatives being dualism, behavior-
ism, and the type-identity theory. It accounts for minds as they are
described by scientific theories and explanations. It is also known as
psychofunctionalism (Block 1980).
2. The Analogy between Minds and Computers
At the origin of computational functionalism are analogies between
some features of minds and some features of computers. Different anal-
ogies pull towards different versions of the view.
Putnam, the chief founder of computational functionalism, drew an
analogy between the individuation conditions of mental states and
those of Turing machine states (Putnam 1960, 1967a, 1967b).
noticed that the states of Turing machines are individuated in terms of
the way they affect and are affected by other Turing machine states,
inputs, and outputs. By the same token, he thought, mental states are
individuated by the way they affect and are affected by other mental
states, stimuli, and behavior. At first, Putnam did not conclude that
mental states are Turing machine states, because—he said—the mind
might not be a causally closed system (Putnam 1960). A bit later,
though, Putnam reckoned that mental states can be characterized
Thus, I am not directly concerned with functionalism about folk psychological theo-
ries (Lewis 1966, 1972, 1980; Armstrong 1970), analytical or conceptual truths about
the mental (Shoemaker 2003b), or the content of mental states (e.g., Sellars 1954;
Harman 1973, 1999; Block 1986).
I avoid formulating functionalism in terms of Ramsey sentences (Lewis 1972,
Block 2007) because such formulations obscure the issues addressed here (cf. Gillett
2007). I will not address several other topics related to functionalism: to what extent
functionalism is consistent with reductionism and the identity theory (e.g., Lewis
1969, Fodor 1975, 1997, Boyd 1980, Churchland and Churchland 1982, Lycan 1982,
Enc¸ 1983, Wilson 1984, 1993, Kim 1989, 1992, Pereboom and Kornblith 1991,
Bickle 1998, Shagrir 1998, Sober 1999, Bechtel and Mundale 1999, Keeley 2000,
Bechtel 2001, Prinz 2001, Pereboom 2002), whether functionalism is consistent with
mental causation (Block 2003, Kim 2003, Rupert 2006), whether functionalism
should be formulated in terms of roles or realizers, whether functionalism should be
formulated in terms of higher level properties, higher order properties, or similarities
between fundamental properties (Heil 2003, 2004), whether the mind extends into
the environment (Harman 1999, Shapiro 1994, Adams and Aizawa 2001, Wilson
2004, Rupert 2004), and the correct metaphysics of realization (Kim 1998, Shapiro
2000, Shoemaker 2001, 2003a, Gillett 2002, 2003, Polger 2004, 2007, Wilson 2004).
For a more detailed reconstruction and discussion of Putnam’s functionalism and
computational functionalism, see Piccinini 2004b and Shagrir 2005.
functionally, like those of Turing machines, though he added that the
mind might be something ‘‘quite different and more complicated’’ than
a Turing machine (Putnam 1967a). Finally, Putnam went all the way to
computational functionalism: mental states are (probabilistic) Turing
machine states (Putnam 1967b).
As Putnam’s trajectory illustrates, the analogy between the individu-
ation of mental states and that of Turing machine states does not entail
computational functionalism. The latter conclusion was reached by
Putnam some time after drawing his analogy, on independent grounds.
What grounds?
It’s hard to know for sure. The relevant papers by Putnam contain
references to the plausibility and success of computational models of
mental phenomena, including Warren McCulloch and Walter Pitts’s
theory of the brain. In 1943, McCulloch and Pitts proposed a mathe-
matical theory of neurons and their signals, which theory was widely
interpreted to claim that, in essence, the brain is a Turing machine
(without tape).
A few years later, John von Neumann interpreted
McCulloch and Pitts’s work as proof that ‘‘anything that can be
exhaustively and unambiguously described, anything that can be
exhaustively and unambiguously put into words, is ipso facto realizable
by a suitable finite neural network’’ of the McCulloch and Pitts type
(von Neumann 1951, 23).
It is now clear that von Neumann’s statement isn’t true, at least
under its most natural interpretation. A fortiori, McCulloch and Pitts
proved nothing of the sort. For one thing, the nervous system
described by their theory is a simplified and idealized version of the
real thing. More importantly, von Neumann’s statement implicitly
abuses the Church-Turing thesis. The Church-Turing thesis says that
anything that is computable in an informal sense, which is intuitively
familiar to mathematicians, is computable by Turing machines. From
this, it doesn’t follow that anything that can be exhaustively and unam-
biguously described is computable by Turing machines. Nor does it fol-
low, as many would soon conclude, that everything can be simulated
by Turing machines or that everything is computational. Alas, neither
von Neumann nor his early readers were especially careful about
these matters. After von Neumann, fallacious arguments from the
As McCulloch put it, ‘‘What we thought we were doing (and I think we succeeded
pretty well) was treating the brain as a Turing machine’’ (quoted in von Neumann
1951, 33). For a detailed study of McCulloch and Pitts’s theory, see Piccinini 2004a.
Church-Turing thesis—sometimes in conjunction with McCulloch and
Pitts’s actual or purported results—to the conclusion that the mind is
computational began to proliferate.
Since McCulloch and Pitts’s networks can be simulated by digital
computers, von Neumann’s (unwarranted) statement entails that any-
thing that can be exhaustively and unambiguously described can be
simulated by a digital computer. If you add to this a dose of faith in
scientists’ ability to describe phenomena—’’exhaustively and unambigu-
ously’’—you obtain pancomputationalism: at a suitable level of descrip-
tion, everything is computational. Thus, pancomputationalism made its
way into the literature. As Putnam put it, ‘‘everything is a Probabilistic
Automaton [i.e., a kind of Turing machine] under some Description’’
(Putnam 1967b, 31). Together with Putnam’s analogy between mental
states and Turing machine states and the alleged plausibility of compu-
tational psychology, pancomputationalism is the most likely ground for
Putnam’s endorsement of computational functionalism.
For present purposes, the most important thing to notice is that the
resulting version of computational functionalism is quite a weak thesis.
This remains true if computational functionalism is disengaged from
Putnam’s appeal to Turing machine states in favor of the thesis that
mental states are, more generally, computational states (Block and
Fodor 1972). If mental states are computational simply because at
some level, everything is computational, then computational function-
alism tells us nothing specific about the mind. It is a trivial conse-
quence of the purported general applicability of computational
descriptions to the natural world. This version of computational func-
tionalism does not tell us how the mind works or what is special about
it. Such a weak thesis stands in sharp contrast with others, which
Examples of such arguments may be found in Dennett 1978, Webb 1980, Nelson
1987, Chalmers 1996b, Simon 1996, and Baum 2004. For their refutation, see Cope-
land 2000 and Piccinini 2007c.
derive from different analogies between features of minds and features
of computers.
Both minds and many kinds of computing systems manipulate
complex combinatorial structures. Minds produce natural language
sentences and other complex sequences of actions. Computing systems
manipulate complex strings of digits.
The structures and processes in
question are complex in the sense that in the interesting cases, there
are recursive rules describing the structure of the inputs and outputs
as well as recursive rules describing the relationship between inputs
and outputs. Furthermore, for any (universal) formalism for specify-
ing computations (e.g., Turing machines) and any recursive function,
there is a program describing a way to compute that function within
that formalism. Finally, whether a program computes a function cor-
rectly (e.g., whether it computes square roots correctly) is an open
question—it’s ‘‘open to rational criticism’’ (Putnam 1960, 149), as it
David Chalmers has pointed out to me that the weak thesis may be strengthened by
arguing that while computation is insufficient for the instantiation of most proper-
ties, computation is sufficient for the instantiation of mental properties. Unlike most
properties, mental properties might be such that they are instantiated ‘‘in virtue of
the implementation of computations’’ (Chalmers, personal correspondence). This is
a fair point, but it makes a difference only insofar as we have good evidence that
computation is sufficient for mentation.
Chalmers defends a thesis of computational sufficiency along these lines as
follows. He defines a notion of abstract causal organization, which involves ‘‘the
patterns of interaction among the parts of the system, abstracted away from the
make-up of individual parts and from the way the causal connections are imple-
mented,’’ and yet includes ‘‘a level fine enough to determine the causation of behav-
ior’’ (Chalmers unpublished). He then argues that unlike most non-mental
properties, all there is to mental properties is abstract causal organization, and
abstract causal organization can be fully and explanatorily captured computation-
ally. If Chalmers is right, then (the right kind of) computation is sufficient for men-
tation while being insufficient for most other properties.
Lacking space for a detailed discussion of Chalmers’s argument, let me make the
following brief comment. I don’t see that Chalmers’s argument establishes computa-
tional sufficiency for mental properties in a way that makes a difference for present
purposes. Chalmers faces a dilemma. If abstract causal organization is truly fine
grained enough to determine the causation of a system’s behavior, then—contrary
to Chalmers’s intent—abstract causal organization will capture (the causal aspects
of) any property whatsoever (including digestion, combustion, etc.). If, instead,
abstract causal organization excludes enough information about a system to rule
out at least certain properties (such as digestion and combustion), then—again,
contrary to Chalmers’s intent—there is no reason to accept that abstract causal
organization will capture every aspect of mental properties. Either way, the specific
connection between mentation and computation is not strengthened. Thus,
Chalmers’s argument does not affect our main discussion.
I am using ‘digit’ to refer to the physical entities or states manipulated by comput-
ers, regardless of whether they represent numbers.
Thanks in part to the complexity of their structure and the sensitiv-
ity of computing systems to such a structure, strings of digits may be
systematically interpreted. So computers’ activities are usually charac-
terized by semantic descriptions. For example, we say that computers
do arithmetic calculations, which is an activity individuated in terms of
operations on numbers, which are possible referents of digits. This
interpretability of the digits manipulated by computers has often been
seen as part of the analogy between mental states and computational
states, because mental states are also typically seen as endowed with
semantic content. This, in turn, has contributed to the attractiveness of
computational theories of mind.
But matters of mental content are
controversial, and without agreement on mental content, the putative
semantic analogy between mental states and computational states gives
us no firm ground on which to explicate computational functionalism.
In the next section, I will argue that the semantic properties of comput-
ing mechanisms make no difference for our purposes. Setting semantic
properties aside, let’s go back to the complexity of the structures being
The analogy between the structures manipulated by minds and by
computing mechanisms is stronger than the previous one: prima facie,
most things cannot manipulate combinatorial structures of arbitrary
recursive complexity according to arbitrary recursive rules. This stron-
ger analogy is a likely source of some versions of computationalism,
and derivatively, of computational functionalism. But this analogy is
still insufficient to underwrite the slogan ‘‘the mind is the software of
the brain.’’ The reason is that the analogy holds between minds and
computing mechanisms in general, and many computing mechanisms
(e.g., ordinary Turing machines) don’t possess any software in the rele-
vant sense. For the relevant notion of software, we need yet another
analogy, which holds between certain mental capacities and certain
capacities of program-controlled computers.
Program-controlled computers (‘‘computers’’ from now on), unlike
other computing systems, have an endless versatility in manipulat-
ing strings of digits. If they are universal, they embody in a single
entity the universality of whole programming systems. Computers are
According to Smith, ‘‘The only compelling reason to suppose that we (or minds or
intelligence) might be computers stems from the fact that we, too, deal with repre-
sentations, symbols, meanings, and the like’’ (1996, 11). Smith is exaggerating in
calling this the only reason. But the semantic analogy between minds and comput-
ers does have a long and influential history. For a more detailed discussion, see
Piccinini 2004c.
versatile because they can store, manipulate, and execute programs.
To a first approximation, a program is a list of instructions for execut-
ing a task defined over strings of digits. An instruction is also a string
of digits, which affects a computer in a special way. Most computers
can execute many different (appropriately written) programs—typically,
within certain limits of time and memory, they can execute any pro-
gram. Because of this, computers can acquire any number of new
capacities simply by acquiring and switching between programs. They
can also refine their capacities by altering their programs. Just as minds
can learn to execute a seemingly endless number of tasks, computers
can execute any task, defined over strings of digits, for which they are
given an appropriate program.
This special property of computers—their capacity to store and exe-
cute programs—gives rise to the special form of explanation that we
employ for their behavior. How is my desktop computer letting me
write this paper? By executing a word-processing program. How does it
allow me to search the web? By executing an Internet browsing pro-
gram. And so on for the myriad capacities of my computer. These are
explanations by program execution:
An explanation by program execution of a capacity Cpossessed
by a system Sis a program Pfor Csuch that Spossesses C
because Sexecutes P.
Explanation by program execution applies only to systems, such as
computers, that have the capacity to execute programs. Other relevant
systems include certain automatic looms. Even though computers are
not the only class of systems subject to explanation by program execu-
tion, computers have other interesting properties that they do not share
with other program-controlled mechanisms. The main difference is that
the processes generated by computer programs depend on the precise
configuration of the input data (viz., the input strings of digits) for
their application: to each string of input data there corresponds a dif-
ferent computation. Furthermore, typical computers have an internal
memory in which they can store and manipulate their own data and
programs, and they can compute any recursive function for as long as
they have time and memory space.
These remarkable capacities of computers—to manipulate strings of
digits and to store and execute programs—suggest a bold hypothesis.
Perhaps brains are computers, and perhaps minds are nothing but the
For a detailed and systematic discussion of computers and their properties, see
Piccinini 2008a.
programs running on neural computers. If so, then we can explain the
multiple capacities minds exhibit by postulating specific programs for
those capacities. The versatility of minds would then be explained by
assuming that brains have the same special power that computers have:
the power to compute by storing and executing programs.
This is the
true source of the computational functionalist slogan: the mind is the
software of the brain.
Compare this version of computational functionalism to the first
one. Here we have a putative explanation of human behavior, based on
an analogy with what explains computers’ behavior. This version tells
us how the mind works and what’s special about it: the brain has the
capacity of storing and executing different programs, and the brain’s
switching between programs explains its versatility. It is a strong thesis:
of all the things we observe, only brains and computers exhibit such
seemingly endless ability to switch between tasks and acquire new
skills. Presumably, there are few if any other systems whose behavior is
explained in terms of (this type of) program execution.
If we take this formulation of computational functionalism seriously,
we ought to find an adequate explication of program execution. We
ought to make explicit what differentiates systems that compute by exe-
cuting programs from other kinds of system. For if minds are to be
interestingly analogous to some aspect of computers, there must be
something that minds and computers share and other systems
lack—something that accounts for the versatility of minds and comput-
ers as well as the explanation of this versatility by program execution.
Unfortunately, the received view of software implementation, which is
behind the standard view of program execution, does not satisfy this
condition of adequacy.
3. Troubles with Program Execution
Ever since Putnam (1967b) formulated computational functionalism,
the received view of software implementation has been as follows. If
there are two descriptions of a system, a physical description and a
computational description, and if the computational description
maps onto the physical description, then the system is a physical
An argument to this effect is in Fodor 1968b, which is one of the founding docu-
ments of computational functionalism and which Fodor 2000 singles out as conflat-
ing functionalism and computationalism. An argument along similar lines is in
Newell 1990, 113ff. Other influential authors offered similar considerations. For the
role played by program execution in Alan Turing’s thinking about intelligence, see
Turing 1950 and Piccinini 2003a. For the role played by program execution in von
Neumann’s thinking about brains, see von Neumann 1958 and Piccinini 2003b.
implementation of the computational description and the computa-
tional description is the system’s software.
The problem with this view is that it turns everything into a com-
puter. As was mentioned in the previous section, everything can be
given computational descriptions. For instance, some cosmologists
study the evolution of galaxies using cellular automata. According to
the received view of software implementation, this turns galaxies into
hardware running the relevant cellular automata programs. If satisfying
computational descriptions is sufficient for implementing them in the
sense in which ordinary computers execute their programs, then every-
thing is a computer executing its computational descriptions. This is
not only counterintuitive—it also trivializes the notion of computer as
well as the analogy at the origin of computational functionalism. If the
mind is the software of the brain in the sense in which certain cellular
automata are the software of galaxies, then the analogy between minds
and computers becomes an analogy between minds and everything else.
As a consequence, the strong version of computational functionalism
collapses into something very much like the weak one.
To make matters worse, the same system satisfies many computa-
tional descriptions. An indefinite number of cellular automata—using
Here is a case in point:
[A] programming language can be thought of as establishing a mapping
of the physical states of a machine onto sentences of English such that
the English sentence assigned to a given state expresses the instruction
the machine is said to be executing when it is in that state (Fodor 1968b,
Beginning in the 1970s, some authors attempted to go beyond the mapping view by
imposing further constraints on implementation. Most prominently, Bill Lycan
(1987) imposed a teleological constraint. Although this was a step in the right
direction (more on this later), Lycan and others used ‘software hardware’ and
‘role realizer’ interchangeably. They offered no account specific to software imple-
mentation as opposed to role realization, so the conflation between functionalism
and computationalism remained unaffected. When talking specifically about com-
putation, philosophers continued to appeal to versions of the mapping view:
[A] physical system is a computational system just in case there is an
appropriate (revealing) mapping between the system’s physical states and
the elements of the function computed (Churchland and Sejnowski 1992,
p. 62; emphasis added).
[C]omputational theories construe cognitive processes as formal opera-
tions defined over symbol structuresSymbols are just functionally
characterized objects whose individuation conditions are specified by a
realization function f
which maps equivalence classes of physical features
of a system to what we might call ‘‘symbolic’’ features. Formal operations
are just those physical operations that are differentially sensitive to the
aspects of symbolic expressions that under the realization function f
specified as symbolic features. The mapping f
allows a causal sequence
of physical state transitions to be interpreted as a computation (Egan
1992, p. 446; first emphasis added).
different state transition rules, different time steps, or cells that repre-
sent regions of different sizes—map onto the same physical dynamics.
Furthermore, an indefinite number of formalisms different from cellu-
lar automata, such as Turing machines or C++ programs, can be
used to compute the same functions computed by cellular automata.
Given the received view of software implementation, it follows that gal-
axies are running all these programs at once.
By the same token, brains implement all their indefinitely many com-
putational descriptions. If the mind is the software of the brain, as
computational functionalism maintains, then given the standard view
of software implementation, we obtain either indeterminacy as to what
the mind is, or that the mind is a collection of indefinitely many pieces
of software. This is not a promising metaphysics of mind, nor is it a
way of explaining mental capacities in terms of program execution.
The problem under discussion should not be confused with a superfi-
cially similar problem described by Putnam (1988) and Searle (1992).
They argue that any physical system implements a large number of
computations, or perhaps every computation, because a large number
of (or perhaps all) state transitions between computational states can
be freely mapped onto the state transitions between the physical states
of a system. For example, I can take the state transitions my web
browser is going through and map them onto the state transitions my
desk is going through. As a result, my desk implements my web brow-
ser. I can establish the same mapping relation between a large number
of (or perhaps all) computations and physical systems. From this, Put-
nam and Searle conclude that the notion of computation is observer-
relative in a way that makes it useless to the philosophy of mind. Their
Matthias Scheutz has suggested an amendment to the standard explication of soft-
ware implementation, according to which for a computational description to be
considered relevant to software implementation, all its states and all its computa-
tional steps must map onto the system that is being described (Scheutz 2004). This
proposal rules out many computational descriptions, such as computational models
that employ C++ programs, as irrelevant to what software is being implemented
by a system, and hence it improves on the standard view. But this proposal still
leaves in place indefinitely many computational descriptions of any given system,
so it doesn’t solve the present problem.
The thesis that everything has computational descriptions is more problematic than
it may appear, in a way that adds a further difficulty for the standard view of soft-
ware implementation. For ordinary computational descriptions are only approxi-
mate descriptions rather than exact ones, and hence a further undesirable
consequence of the standard view is that programs can only approximate the
behavior of the systems that are supposed to be implementing them. A full treat-
ment of this further difficulty would take up more space than is available in this
paper, so I will set it aside. For a detailed discussion of the relevance of approxi-
mation to pancomputationalism and the way pancomputationalism trivializes com-
putationalism, see Piccinini 2007a.
argument is based on the received view of software implementation,
and we might avoid its conclusion by abandoning the received view.
But even under the received view of software implementation, Put-
nam and Searle’s problem is not very serious. As many authors have
noted (e.g., Chrisley 1995, Copeland 1996, Chalmers 1996a, Bontly
1998, Scheutz 2001), the computational descriptions employed by Put-
nam and Searle are anomalous. In the case of kosher computational
descriptions—the kind normally used in scientific modeling
—the work
of generating successive descriptions of a system’s behavior is done by
a computer running an appropriate program (e.g., a weather forecast-
ing program), not by the mapping relation. In the sort of descriptions
employed in Putnam and Searle’s argument, instead, the descriptive
work is done by the mapping relation.
In our example, my web browser does not generate successive
descriptions of the state of my desk. If I want a genuine computational
description of my desk, I have to identify states and state transitions of
the desk, represent them by a computational description (thereby fixing
the mapping relation between the computational description and the
desk), and then use a computer to generate subsequent representations
of the state of the desk, while the mapping relation stays fixed. So, Put-
nam and Searle’s alleged problem is irrelevant to genuine computa-
tional descriptions. Still, the problem under discussion remains:
everything can be given an indefinite number of bona fide computa-
tional descriptions.
To solve this problem, we must conclude that being described com-
putationally is insufficient for implementing software, which is to say,
we must go beyond the received view of software implementation.
The same point is supported by independent considerations. The
word ‘software’ was coined to characterize specific systems called
‘computers’. Computers perform different activities from those per-
formed by other systems such as drills or valves—let alone galaxies.
We consider the invention of computers in the 1940s a major intellec-
tual breakthrough—the discovery of something new. We have specific
disciplines—computer science and computer engineering—that study
the peculiar activities and characteristics of computers and only com-
puters. For all these reasons, a good account of software implementa-
tion must draw a principled distinction between computers and other
Philosophers have largely ignored this problem, and the charitable
reader may legitimately wonder why. A first part of the answer is that
For a systematic treatment of computational modeling in science, see Humphreys
philosophers interested in computationalism have devoted most of their
attention to explaining mental phenomena, leaving computation per se
largely unanalyzed.
A second part of the answer is that computationalist philosophers
typically endorse the semantic view of computation, according to which
computational states are individuated, at least in part, by their content
(for a recent example, see Shagrir 2001, 2006). The semantic view
appears to offer protection to the received view of software implemen-
tation, because independently of the semantic view, it is plausible that
only some things, such as mental states, are individuated by their con-
tent. If computational states are individuated by their content and con-
tent is present only in few things, then explanation by program
execution will apply at most to things that have content, and the trivi-
alization of the notion of software is thereby avoided. Unfortunately,
the protection offered by the semantic view is illusory. Here, there is no
room for the in-depth treatment the semantic view of computation
deserves. I have given such a treatment elsewhere (Piccinini 2004c,
2008b); I will only reiterate its results.
First, even the conjunction of the received view of software imple-
mentation and the semantic view of computation does not capture the
notion of program execution that applies to ordinary computers. Com-
puters (and some automatic looms, for that matter) execute programs
whether or not the digits they manipulate have content, and there are
mechanisms that perform computations defined over interpreted
strings of digits just like those manipulated by computers but do so
without executing programs (e.g., many calculators). Second, there are
computationalists who maintain that content plays no explanatory or
individuative role in a computational theory of mind (Stich 1983,
Egan 1992, 2003). Conjoining computationalism with the semantic
view of computation begs the question of whether computational
states are individuated by their content. Finally, and most seriously,
the semantic view of computational states is incorrect, because com-
putability theorists and computer designers—i.e., those to whom we
should defer in individuating computational states—individuate com-
putational states without appealing to their semantic properties. For
these reasons, the semantic view of computation needs to be rejected,
and cannot restore to health the received view of software implemen-
To a first approximation, the distinction between computers and
other systems can be drawn in terms of program execution, where pro-
gram execution is understood informally as a special kind of activity
pertaining to special mechanisms (cf. Fodor 1968b, 1975; Pylyshyn
1984). Computers are among the few systems whose behavior we
normally explain by invoking the programs they execute.
When we do
so, we explain each activity of a computer by appealing to the unique
program being executed. A program may be described in many differ-
ent ways: instructions, subroutines, whole program in machine lan-
guage, assembly language, or higher level programming language. But
modulo the compositional and functional relations between programs
and their components at different levels of description, a computer runs
one and only one program at any given time. An expert can actually
retrieve the unique program run by a computer and write it down,
instruction by instruction.
True, modern computers can run more than one program ‘‘at once,’’
but this has nothing to do with applying different computational
descriptions to them at the same time. It has to do with computers’
capacity to devote some time to running one program, quickly switch
to another program, quickly switch back, and so forth, creating the
impression that they are running several programs at the same time.
(Some so-called supercomputers can execute many programs in parallel.
This is because they have many different processors, i.e., program-
executing components. Each processor executes one and only one
program at any given time.)
All of this is in need of non-circular
explication. A good account of software implementation must say why
computers execute programs while most other systems don’t, and hence
what minds need to be like in order to be the putative software of
brains. To prepare for that, it’s time to clarify the relationship between
functionalism and computationalism.
4. Mechanistic Functionalism
According to functionalism, the mind is the functional organization of
the brain. According to computationalism, the functional organization
of the brain is computational. These theses are prima facie logically
independent—it should be possible to accept one while rejecting the
other. But according to one construal, functional organizations are
specified by computational descriptions connecting a system’s inputs,
internal states, and outputs (Putnam 1967b, Block and Fodor 1972).
Under this construal, functional organizations are ipso facto com-
putational, and hence functionalism entails computationalism. This
In the relevant sense of ‘‘program execution’’. Another notion is that of develop-
mental ‘‘program,’’ which is employed in biology. That is an independent notion of
program, which would require a separate account.
It’s also true that indefinitely many programs can produce the same behavior. But
in any given computer processor, at any given time, there is a fact of the matter as
to which program is generating the behavior in question: it’s the one that the pro-
cessor is executing.
consequence makes it impossible to reject computationalism without
also rejecting functionalism, which may explain why attempts at refut-
ing functionalism often address explicitly only its computational variety
(e.g., Block 1978, Churchland 2005). The same consequence has led to
Fodor’s recent admission that he and others conflated functionalism
and computationalism (2000, 104).
To avoid conflating functionalism and computationalism, we need a
notion of functional organization that doesn’t beg the question of com-
putationalism. The broadest notion of functional organization is the
purely causal one, according to which functional organization includes
all causal relations between a system’s internal states, inputs, and out-
puts. Given this notion, functionalism amounts to the thesis that the
mind is the causal organization of the brain, or that mental states are
individuated by their causal properties. Indeed, this is how functional-
ism is often formulated. The good news is, this version of functionalism
is not obviously committed to computationalism, because prima facie,
causal properties are not ipso facto computational. The bad news is,
this version of functionalism is too weak to underwrite a theory of
The causal notion of functional organization applies to all systems
with inputs, outputs, and internal states. A liberal notion of input and
output generates an especially broad causal notion of functional orga-
nization, which applies to all physical systems. For instance, every
physical system may be said to take its state at time t
as input, go
through a series of internal states between t
and t
, and yield its state
at t
as output. A more restrictive notion of input and output generates
more interesting notions of functional organization. For instance, opa-
que bodies may be said to take light of all wavelengths as input and
yield light of only some wavelengths plus thermal radiation as output.
Still, the purely causal notion of functional organization is too vague
and broad to do useful work in the philosophy of mind (and computa-
tion, for that matter). How should the notions of input and output be
applied? Which of the many causal properties of a system are relevant
to explaining its capacities? Does this purely causal version of function-
alism entail computationalism? To answer these questions, we need to
restrict our attention to the causal properties of organisms and artifacts
that are relevant to explaining their specific capacities.
To fulfill this purpose, we turn to the notion of functional analysis.
Functional analysis was introduced in modern philosophy of mind
by Fodor (1965, 1968a). He used examples like the camshaft, whose
function is to lift an engine’s valve so as to let fuel into the piston.
The camshaft has many causal properties, but only some of them, such
as its capacity to lift valves, are functionally relevant—relevant to
explaining an engine’s capacity to generate motive power. Fodor argued
that psychological theories are functional analyses, like our analysis of
the engine’s capacity in terms of the functions of its components.
When Fodor defined psychological functional analysis in general,
however, he departed from his examples and assimilated psychological
functional analyses to computational descriptions.
Several other
authors developed a similar notion of functional analysis, retaining
Fodor’s assimilation of functional analyses to computational descrip-
tions (Cummins 1975, 1983, 2000, Dennett 1978, Haugeland 1978,
Block 1995). If functional organizations are specified by functional
analyses and functional analyses are computational descriptions, then
functional organizations are ipso facto computational. The mongrel of
functional analysis and computational description is another source of
the conflation between functionalism and computationalism.
To avoid this conflation, we need a notion of functional organiza-
tion that has the relevant explanatory power—like Fodor et al.’s—but
does not commit us to the view that every functionally organized sys-
tem is computational. The recent revival of mechanisms in the philoso-
phy of science offers us what we need. Not only do mechanisms satisfy
our need; a formulation of functionalism in terms of mechanisms is
also independently motivated. For an important lesson of recent philos-
ophy of science is that (the relevant kind of) explanation in the special
sciences, such as psychology and neuroscience, takes a mechanistic
Amechanism M with capacities Cis a set of spatiotemporal compo-
nents A
, their functions F, and F’s relevant causal and spatio-
temporal relations R, such that Mpossesses Cbecause (i) Mcontains
, (ii) A
have functions Forganized in way R, and
(iii) F, when organized in way R, constitute C.
A mechanism in the present sense exhibits its capacities thanks to its
components, their functions, and their organization. Biologists ascribe
functions to types of biological traits (e.g., the digestive function of
stomachs) and engineers ascribe them to types of artifacts and their
components (e.g., the cooling function of refrigerators). The functions
ascribed to traits and artifacts are distinct from their accidental effects
Cf.: ‘‘the paradigmatic psychological theory is a list of instructions for producing
behavior’’ (Fodor 1968b, 630). For a more extended discussion, see Piccinini
E.g., see Bechtel and Richardson 1993, Machamer, Darden and Craver 2000, Bech-
tel 2001, 2006, 2007, Craver 2001, 2005, 2006, 2007, Glennan 2002, 2005, Thagard
2003, Machamer 2004, Tabery 2004, Bogen 2005, Bechtel and Abrahamsen 2005,
Darden 2006.
(e.g., making noise or breaking under pressure), and hence are only a
subset of their causal powers. As a consequence, tokens of organs and
artifacts that do not perform their functions may be said to malfunc-
tion or be defective.
As I will use the term, a mechanism’s functional organization
includes the states and activities of components, the spatial relations
between components, the temporal relations between the components’
activities, and the specific ways the components’ activities affect one
another. For example, the heart pumps blood into the arteries. This
simple mechanistic description can begin to be unpacked as follows: (i)
the mechanism includes a heart (component), arteries (components),
and blood (input output), (ii) the heart pumps the blood (activity of
the heart), (iii) the heart is attached to the arteries in a certain way
(spatial relation), (iv) the blood enters the arteries after it leaves the
heart (temporal relation), (v) the heart’s pumping causes the blood to
enter the arteries (active relation). The relevant spatial relations
between components may continue to hold even when the mechanism
is not functioning. Not so for most temporal and active relations.
Much more could be said about functional organization. The impor-
tant point is that the functional organization of a mechanism is a nec-
essary condition for the mechanism to do what it does.
Different notions of mechanism may be generated by employing dif-
ferent notions of function.
Drawing from William Wimsatt’s helpful
taxonomy, we find three especially pertinent notions (Wimsatt 1972,
4–5). Perspectival functions are causal powers that are relevant accord-
ing to a view or perspective of what the system is doing. Evaluative
functions are causal powers that contribute to a system’s proper
functioning. Teleological functions are causal powers that contribute to
fulfilling the goal(s) of the system or its users.
These three notions are related. Fulfilling goals is one way of func-
tioning properly, especially if proper functioning is defined as fulfilling
one’s goals, though in a more general sense, something may function
properly without fulfilling its goals. Functioning properly may be all
that is needed to fulfill one’s goals, especially if one’s goal is to func-
tion properly, though something may fulfill its goals without function-
ing properly. So evaluative and teleological functions may or may not
go together. Furthermore, goals and standards of proper functioning
define perspectives that we may take towards a system. Thus, as
Wimsatt points out, evaluative and teleological functions are special
Polger 2004 follows a similar strategy in distinguishing different versions of func-
tionalism based on which notion of function they employ. Unfortunately, in doing
so he draws a false contrast between functions in the present sense and mathemati-
cal functions (cf. also Polger 2007, p. 252, for the same false contrast).
cases of perspectival functions. But the notion of perspectival function
is broader than the others: there are perspectives towards a system that
have nothing to do with proper functioning or goals.
The above notions of function (as well as goals, proper functioning,
and perspectives) need naturalistic explications. There is no shortage of
literature devoted to that, and I cannot hope to resolve the debate on
functions here.
For present purposes, I’ll limit myself to the following
First, different authors offer slightly different accounts of mecha-
nisms, and not all of them employ the word ‘function’. But those dif-
ferences are irrelevant here. All accounts of mechanisms may be
subsumed under the above template by using the broad notion of per-
spectival function.
Second, there may be several legitimate notions of mechanisms,
corresponding to different notions of function. Any notion of func-
tion that aspires to be relevant here, however, must be naturalistic.
Which of the more precise notions of mechanism is most adequate to
account for the explanatory practices that are relevant to the science
and metaphysics of mind is a question that need not be resolved
Third, any further explication of the notion of function or mecha-
nism cannot rely on the notion of computation in such a way as to
turn all mechanisms into computing mechanisms, on pain of begging
the question of computationalism again. Fortunately, computation
plays no such role in current explications of these notions. As a
result, appealing to mechanisms does not beg the question of
This shows that we need not fasten together functions and computa-
tions the way Fodor and his followers did. When we appeal to the
function of camshafts to explain the capacities of engines, our function
ascription is part of a mechanistic explanation of the engine’s capacities
in terms of its components, their functions, and their organization. We
do not appeal to programs executed by engines, nor do we attribute
any computation to engines. In fact, most people would consider
engines good examples of systems that do not work by executing pro-
grams (or more generally, by performing computations). The same
point applies to the vast majority of mechanisms, with the notable
The main competing accounts of functions in biology and engineering may be
found in Allen, Bekoff, and Lauder 1998; Preston 1998; Schlosser, 1998b; Buller
1999; Ariew, Cummins, and Perlman 2002, Christensen and Bickhard 2002. Other
contributions include Perlman 2004, Hourkes and Vermaas 2004, Cameron 2004,
Johansson 2004, Schroeder 2004, Vermaas and Houkes 2006, Scheele 2006, Frans-
sen 2006, Vermaas 2006, Houkes 2006, Houkes and Meijers 2006, Kroes 2006.
exception of computers and other computing mechanisms (including,
perhaps, brains).
Fourth and finally, we should not confuse the teleological notion of
function with the etiological account of teleology. The etiological
account of teleology in terms of evolutionary history is perhaps the
most popular one, but it might not suit our present purposes.
matters here is that teleological functions ground a robust notion of
functional organization without relying on the notion of computation.
The question of how teleological functions ought to be explicated is
surely important, but I can remain neutral about it.
The systems studied by most special sciences—including neurosci-
ence, psychology, and computer science—are mechanisms. Investigators
in these disciplines analyze systems (e.g., trees) by breaking them down
into component parts (e.g., roots) and discovering (or in engineering,
designing) the functions of those parts (e.g., supporting the tree and
absorbing water from the soil). Neuroscientists and psychologists elab-
orate their theories in the same way: they partition the brain or mind
into components (e.g., the suprachiasmatic nuclei or episodic memory)
and they ascribe them functions (respectively, regulating circadian
rhythms and storing records of events). Mutatis mutandis, computer
scientists do the same thing: they partition a computer into components
(e.g., the memory and the processor) and ascribe them functions
(respectively, storing data as well as instructions and executing instruc-
tions on the data).
Since mechanisms give us the notion of functional organization that
is relevant to understanding theories in psychology, neuroscience, and
computer science, we should adopt this notion of functional organiza-
tion in our formulation of functionalism. We can now give a novel and
improved formulation of functionalism, which does justice to the origi-
nal motivations of functionalism without begging the question of com-
putationalism. Functionalism about a system Sshould be construed as
Why not? First, the evolutionary account of functions does not immediately apply
to artifacts, such as computers, which are not the product of evolution by natural
selection. Because of this, it’s unclear whether and how computational functional-
ism, which is based on an analogy between features of minds and features of com-
puters, can be formulated in terms of a notion of function that relies on evolution.
(This is not to say that there can’t be a broader notion of selection that applies to
both organisms and artifacts; cf. Wright 1973, Preston 2003.) Second, and more
importantly, the evolutionary account of functions grounds any resulting theory of
mind on the notions and practices of evolutionary biology rather than the empirical
disciplines relevant to explaining the capacities of minds and computers—namely,
psychology, neuroscience, and perhaps computer science.
For example, non-etiological accounts of teleology are given by Schlosser 1998,
Boorse 2002, and Wimsatt 2002.
the thesis that Sis the functional organization of the mechanism that
exhibits S’s capacities. We can now explicate the claim that the mind is
the functional organization of the brain: the brain is the relevant mech-
anism, and the mind is its functional organization. This mechanistic
functionalism preserves functionalism’s insight while doing justice to the
relevant scientific practices.
Under this mechanistic version of functionalism, a system is individ-
uated by its component parts, their functions, and their relevant causal
and spatiotemporal relations. The functional states of the system are
individuated by their role within the mechanistic explanation of the sys-
tem. The states of the system are not only individuated by their rele-
vant causal relations to other states, inputs, and outputs, but also by
the component to which they belong and the function performed by
that component when it is in that state. This applies to all mechanisms,
including computing mechanisms. For example, pace Putnam, ordinary
Turing machine states are individuated not only as having the function
of generating certain outputs and other internal states on the basis of
certain inputs and states, but also as being states of the active device
(as opposed to the tape), which is a component of the Turing machine
and has the functions of moving along the tape, reading the tape, and
writing on it.
Mechanistic functionalism has a further great advantage, which is
especially relevant to the concerns of this paper: it is based on a notion
of mechanism that offers us the materials for explicating the notion of
program execution, and more generally, computation.
Carl Gillett (2007) has independently developed a proposal similar to what I’m call-
ing ‘mechanistic functionalism’, with which he addresses other aspects of function-
When mechanistic functionalism is further specified by employing teleological
functions, the resulting doctrine is a close relative of teleological functionalism
(Lycan 1981, 1987, Wilkes 1982, Millikan 1984, Sober 1990, Shapiro 1994, Rupert
2006). According to teleological functionalism, the mind is the teleological organi-
zation of the brain, or mental states are individuated by their teleological function.
A teleological version of mechanistic functionalism adds to traditional teleological
functionalism a mechanistic framework within which to specify the functional orga-
nization of the brain. Furthermore, the following two caveats apply. First, teleolog-
ical functionalism is often offered as an alternative to computational functionalism
(e.g., Lycan 1981, Millikan 1984, Sober 1990). But I will soon argue that mecha-
nisms are the most adequate framework within which to explicate computation. As
a consequence, computational functionalism turns out to be a special version of
mechanistic functionalism. Second, many supporters of teleological functionalism
endorse an etiological account of teleology. But as I already pointed out, this may
not be the most suitable account of teleology for present purposes.
5. Mechanisms, Computation, and Program Execution
A mechanism may or may not perform computations, and a mecha-
nism that performs computations—a computing mechanism—may or
may not do so by executing programs. To illustrate the latter distinc-
tion, consider Turing machines. Turing machines are made out of a
tape of unbounded length, an active device that can take a finite num-
ber of states, letters from a finite alphabet, and functional relations
(specified by a machine table) between tape, active device, states, and
letters. Of course, Turing machines are usually thought of as abstract,
in the same sense in which mathematically-defined triangles and circles
are abstract. Like triangles and circles, Turing machines can be physi-
cally implemented. Physically implemented Turing machines and other
concrete computing mechanisms operate on concrete counterparts of
strings of letters, which I call ‘strings of digits’. Whether abstract or
concrete, Turing machines are mechanisms, subject to mechanistic
explanation no more and no less than other mechanisms.
Some Turing machines compute only one function. Other Turing
machines, called ‘universal’, can compute any computable functions.
The distinction between the abstract and the concrete easily leads to confusion on
these matters. Here I have no room for a full treatment of all the relevant issues,
so the following brief points will have to suffice.
Thomas Polger argues that abstract computations, being abstract, are not caus-
ally individuated (2007, 239–244). (He also expresses some second thoughts; cf. fn.
18.) Polger slides between talk of abstract functions, computations, machines,
states, algorithms, and programs, as if the same considerations applied to all. I take
issue with that. Abstract functions are not realized in the same sense in which
machines are. Functions are computed by machines—they are relations holding
between the inputs and outputs of machines. Machines compute functions by fol-
lowing algorithms or programs. As we shall see, some special machines not only
follow, but execute programs. Algorithms may be seen as sequences of statements
or as relations holding between machine states and actions; programs may be seen
as sequences of strings, sequences of statements, or relations holding between
machine states and actions. Abstract functions are not causally individuated, but
machines as well as their states and computations are—at least insofar as they are
physically realizable. Algorithms and programs may or may not be, depending on
how they are conceived of.
On a different note, it is common to see references to different levels of descrip-
tion of computing mechanisms, some of which are said to be more abstract than
others (e.g., Newell 1980, Marr 1982). In this sense, a description is more or less
abstract depending on whether it includes less or more details about a system. In
this paper, I am not questioning the distinction between more abstract and more
concrete levels of description and I am not focusing on the ‘‘implementation’’ or
‘‘physical’’ level at the expense of more ‘‘abstract’’ computational levels. Rather, I
am offering a new way of understanding computational levels, in light of the fact
that insofar as levels of description are relevant to the explanation of a system’s
capacities, they are all describing aspects of a mechanism—they are all part of a
complete mechanistic explanation of the system, regardless of how abstract they
are. For a detailed account of mechanistic levels, see Craver 2007.
This difference in computation power has a mechanistic explanation.
Universal Turing machines, unlike non-universal ones, treat some digits
on their tape as programs; they manipulate their data by appropriately
responding to the programs. Because of this, universal Turing machines
—unlike non-universal ones—may be said to execute the programs
written on their tape. The behavior of all Turing machines is explained
by the computations they perform on their data, but only the behavior of
universal Turing machines is explained by the execution of programs.
Besides Turing machines, many other mechanisms compute: finite state
automata, pushdown automata, RAM machines, etc. Of these, some
compute by executing programs and some don’t. Of those that do, some
are universal and some aren’t.
Like Turing machines, the capacities of most biological systems and
artifacts are mechanistically explained in terms of their components
and functions (Bechtel and Richardson 1993, Craver and Darden
2001). Unlike Turing machines, the capacities of most biological sys-
tems are not explained by appealing to putative computations they per-
form, let alone programs that they execute (except, of course, in the
case of brains and other putative computing mechanisms).
So, explaining a capacity by program execution is not the same as
explaining it computationally, which is not the same as explaining it
mechanistically. Rather, explaining a (computational) capacity by pro-
gram execution is a special kind of computational explanation, which is
a special kind of mechanistic explanation. Computing mechanisms are
subject to explanation by program execution because of their peculiar
mechanistic properties. More specifically, computers are subject to
computational explanation because of their peculiar mechanistic prop-
erties, some of which (though not all) they share with other computing
The rest of this section is devoted to explicating the above distinc-
tions. First, I will identify the subclass of mechanisms that perform
computations and whose (relevant) capacities are explained by the com-
putations they perform.
Second, I will identify the subclass of com-
puting mechanisms that execute programs and whose (relevant)
activities are explained by the programs they execute. Once we have an
account of these distinctions, we will have the resources to explicate
computational functionalism.
Most mechanisms are partially individuated by their capacities. For
instance, stomachs are things whose function is to digest food, and
refrigerators are things whose function is to lower the temperature of
certain regions of space. Capacities, in turn, may be analyzed in terms
For a more detailed formulation and defense of this account, see Piccinini 2007d.
of inputs received from the environment and outputs delivered to the
environment. Stomachs take undigested food as input and yield
digested food as output; refrigerators take their inside at a certain tem-
perature as input and deliver the same region at a lower temperature as
output. Inputs and outputs may be taxonomized in many ways, which
are relevant to the capacities to be explained. In our examples, foods
and temperatures are taxonomized, respectively, in terms of whether
and how they can be processed by stomachs and refrigerators in the
relevant ways. Being a specific kind of mechanism, computing mecha-
nisms are individuated by inputs and outputs of a specific kind and by
a specific way of processing those inputs and outputs.
The inputs and outputs that are relevant to computing mechanisms
are what computability theorists call strings of letters, or symbols.
string of digits, as I’m using the term, is a concrete counterpart of a
string of letters. What does it take for a concrete entity to be a string
of digits? I will now sketch an answer in mechanistic terms. A digit is a
state of a particular that belongs to one and only one of a finite num-
ber of relevant types. The digits’ types are unambiguously distinguish-
able (and hence individuated) by the effects they have on the
mechanism that manipulates them. That is, every digit of the same type
affects a mechanism in the same way relative to generating the mecha-
nism’s output, and each type of digit affects the mechanism in a differ-
ent way relative to generating the mechanism’s output.
In other words, ceteris paribus, if Digit
and Digit
are of the same
type, then substituting Digit
for Digit
results in the exact same com-
putation (type) with the same output string (type), whereas if Digit
and Digit
are of different types, then substituting Digit
for Digit
results in a different computation, which may generate a different out-
put string.
This property of digits differentiates them from many other
classes of particulars, such as temperatures and bites of food, which
belong to indefinitely many relevant types. (There is no well-defined
functional classification of temperatures or foods such that every tem-
perature or bite of food belongs to one among a finite number of rele-
vant types).
A string is a list of permutable digits individuated by the digits’
types, their number, and their order within the string. Every finite
string has a first and a last digit member, and each digit that belongs
in a string (except for the last member) has a unique successor. A digit
For the mathematical theory of strings, see Corcoran, Frank, and Maloney 1974.
It is possible for two different computations to generate the same output from the
same input. This simply shows that computations are individuated more finely than
input-output mappings.
within a string can be substituted by another digit without affecting the
other digits’ types, number, or position within the string. In particular,
when an input string is processed by a mechanism, ceteris paribus, the
digits’ types, their number, and their order within the string make a dif-
ference to what output string is generated.
The fact that digits are organized into strings further differentiates
strings of digits from the inputs and outputs of other functionally ana-
lyzable systems. Neither temperatures nor bites of food are organized
into strings in the relevant sense. The comparison is unfair, because
neither bites of food nor temperatures are digits to begin with. But let
us suppose, for the sake of the argument, that we could find a way to
unambiguously taxonomize bites of food into finitely many (function-
ally relevant) types. For instance, we could taxonomize bites of food
into protein bites, fat bites, etc. If such a taxonomy were viable, it
would turn bites of food into digits. Still, sequences of bites of food
would not constitute strings of digits, because digestion—unlike compu-
tation—is not precisely sensitive to the order in which an organism
bites its food.
Among systems that manipulate strings of digits, some do so in a
special way: under normal conditions, they produce output strings of
digits from input strings of digits in accordance with a general rule,
which applies to all relevant strings and depends on the inputs (and
perhaps the internal states) for its application.
The rule in question
specifies the function computed by the system. Some systems manipu-
late strings without performing computations over them. For instance,
a genuine random number generator yields strings of digits as outputs,
but not on the basis of a general rule defined over strings. (If it did, its
output would not be genuinely random.) Systems that manipulate
strings of digits in accordance with the relevant kind of rule deserve to
be called computing mechanisms.
The activities of computing mechanisms are explained by the com-
putations they perform. For example, if you press the buttons marked
‘21’, ‘:’, ‘7’, and ‘=’, of a (well-functioning) calculator, after a short
delay it will display ‘3’. The explanation of this behavior includes the
facts that 3 is 21 divided by 7, ‘21’ represents 21, ‘:’ represents division,
‘7’ represents 7, ‘=‘ represents equality, and ‘3’ represents 3. But most
crucially, the explanation involves the fact that under those conditions,
the calculator performs a specific calculation: to divide its first input
Which strings are relevant? All the strings from the relevant alphabet. For each
computing mechanism, there is a relevant finite alphabet. Notice that the rule need
not define an output for all input strings (and perhaps internal states) from the rel-
evant alphabet. If some outputs are left undefined, then under those conditions the
mechanism should produce no output strings of the relevant type.
datum by the second. The capacity to calculate is explained, in turn,
by an appropriate mechanistic explanation. Calculators have input
devices, processing units, and output devices. The function of the input
devices is to deliver input data and commands from the environment
to the processing units, the function of the processing units is to
perform the relevant operations on the data, and the function of
the output devices is to deliver the results to the environment. By
iterating this explanatory strategy, we can explain the capacities of a
calculator’s components in terms of its components’ functions and their
Until now, I’ve sketched an account of computing mechanisms in
general: computing mechanisms are mechanisms whose function is
manipulating strings of digits according to appropriate rules; their
behaviors are explained by the computations they perform. There
remains to explicate the more interesting notion of a (program-
controlled) computer—a mechanism that computes by executing
Computers have special processing units, usually called processors.
Processors are capable of performing a finite number of primitive oper-
ations on input strings (of fixed length) called ‘data’. Which operation
a processor performs on its data is determined by further strings of dig-
its, called ‘instructions’. Different instructions cause different opera-
tions to be performed by a processor. The performance of the relevant
operation in response to an instruction is what constitutes the execu-
tion of that instruction. A list of instructions constitutes a program.
The execution of a program’s instructions in the relevant order consti-
tutes the execution of the program. So, by executing a program’s
instructions in the relevant order, a computer processor executes the
program. This is a brief mechanistic explanation of (program-
controlled) computers and their capacity to execute programs in terms
of their components, functions, and organization. The capacity of a
processor to execute instructions can be further explained by a mecha-
nistic explanation of the processor in terms of its components, their
functions, and their organization.
Only computing mechanisms of a specific kind, namely computers,
have processors capable of executing programs (and memories for stor-
ing programs, data, and results). This is why only the capacities of
computers, as opposed to the capacities of other computing mecha-
nisms—let alone mechanisms that do not perform computations—are
explained by program execution. Computational explanation by
Cf. any standard textbook on computer organization and design, such as Patterson
and Hennessy 1998.
program execution says that there are strings of digits whose function
is to determine a sequence of operations to be performed by a proces-
sor on its data.
In other words, program execution requires that some states of some
components of the computer function as a program; in an explanation
by program execution, ‘program’ is used as a function term. The way a
program determines what the computer does is cashed out in terms of
the computer’s mechanistic properties. A program-controlled computer
is a very special kind of computing mechanism, which has the capacity
to execute programs. This is why the appeal to program execution is
explanatory for computers—because it postulates programs and proces-
sors inside computers.
As a consequence, when the behavior of ordinary computers is
explained by program execution, the program is not just a description.
The program is also a (stable state of a) physical component of the com-
puter, whose function is to generate the relevant capacity of the com-
puter. Programs are physically present within computers, where they
have a function to perform. Somehow, this simple and straightforward
point seems to have been almost entirely missed in the philosophical
For an exception, see Moor 1978, 215. Robert Cummins, one of the few people to
discuss this issue explicitly, maintained that ‘‘programs aren’t causes but abstract
objects or play-by-play accounts’’ (Cummins 1983, p. 34; see also Cummins 1977).
Cummins’s weaker notion of program execution is quite popular among philoso-
phers, and is yet another side of the fuzziness surrounding functionalism and com-
putationalism. This is because the weaker notion is not the one used in computer
science and does not underwrite the strong analogy between mental capacities and
computers’ capacities that is behind the slogan ‘‘the mind is the software of the
brain,’’ and yet the weaker notion is often used in explicating computationalism or
even functionalism. The main reason for Cummins’s view of program execution
seems to be the way he mixes functional analysis and computational description.
Roughly, Cummins thinks that explaining a capacity by program execution is the
same as giving a functional analysis of it, and therefore the program is not a part
of the computer but a description of it (see Section 4 above). This leads Cummins
and his followers to the paradoxical conclusion that connectionist networks com-
pute by executing algorithms or programs (Cummins and Schwarz 1991, Roth
2005). But it should be obvious that connectionist networks do not store and exe-
cute programs in the sense I explicated in the main text, which is why their behav-
ior is not as flexible as that of digital computers. For a detailed account of
connectionist computationalism, including a distinction between connectionist sys-
tems that compute and those that don’t, see Piccinini 2008c. I should point out that
Cummins has recently agreed that ‘‘stored programs are certainly causes’’ (personal
6. Computational Functionalism
We now have the means to explicate computational functionalism:
Computational functionalism: the mind is the software of the
In the broadest sense, this may be interpreted to say that the mind is
(an aspect of) the computational organization of the brain, where com-
putational organization is the functional organization of a computing
mechanism and the brain is assumed to be a computing mechanism. In
other words, systems that realize minds are mechanisms that manipu-
late strings of digits according to general rules; the mind is the collec-
tion of functional states and properties such that the mechanism
manipulates those strings in accordance with those rules.
This broad interpretation cashes out the general analogy between
minds and many computing mechanisms, according to which both
minds and computing mechanisms manipulate complex combinatorial
structures in accordance with appropriate rules. This version of compu-
tational functionalism is compatible with any nontrivial version of
computationalism, including connectionist computationalism. But as I
pointed out in Section 2, this analogy is neither as strong nor as
explanatory as the analogy between minds and (program-controlled)
computers. The more general analogy is not based on the notion of
software used in computer science, which is the notion that explains
the capacities of (program-controlled) computers and inspires the slo-
gan ‘‘the mind is the software of the brain.’’ Accordingly, in explicating
computational functionalism we should give precedence to the literal
notion of software. (I’ll come back to this more general formulation
In its strong and literal form, computational functionalism says that
(i) the brain contributes to the production of behavior by storing and
executing programs, in the sense sketched in the previous section, and
(ii) the mind is constituted by the programs stored and executed by the
brain, plus, perhaps, the states and processes generated by executing
those programs. As in the broader version of computational functional-
ism, the mind is an aspect of the computational organization of a com-
puting mechanism; in addition, the computing mechanism is a
program-controlled computer and the mind is its programs (plus, per-
haps, the states and processes generated by executing the programs).
This doctrine has some interesting consequences for the study of minds
and brains.
Computational functionalism licenses explanations of mental capaci-
ties by program execution. This is a kind of mechanistic explanation,
which explains mental capacities by postulating a specific kind of mech-
anism with specific functional properties. Briefly, the postulated mecha-
nism includes memory components, which store programs, and at least
one processor, which manipulates and executes them. Together, the
interaction between memories and processors determines how the sys-
tem processes its data and produces its outputs. The capacities of the
system are explained as the result of the processing of data performed
by the processor(s) in response to the program(s).
Computational functionalism entails that minds are multiply realiz-
able, in the sense in which different tokens of the same type of com-
puter program can run on different kinds of hardware. So if
computational functionalism is correct, then—pace Bechtel and Mun-
dale 1999, Shapiro 2000, Churchland 2005 and other foes of multiple
realizability—mental programs can also be specified and studied inde-
pendently of how they are implemented in the brain, in the same way
in which one can investigate what programs are (or should be) run by
digital computers without worrying about how they are physically
implemented. Under the computational functionalist hypothesis, this is
the task of psychological theorizing. Psychologists may speculate on
which programs are executed by brains when exhibiting certain mental
capacities. The programs thus postulated are part of a mechanistic
explanation for those capacities.
The biggest surprise is that when interpreted literally, computational
functionalism entails that the mind is a (stable state of) a component
of the brain, in the same sense in which computer program tokens are
(stable states of) components of computers. As a consequence, even a
brain that is not processing any data—analogously to an idle com-
puter, or even a computer that is turned off—might still have a mind,
provided that its programs are still physically present. This conse-
quence seems to offend some people’s intuitions about what it means
to have a mind, but it’s independently plausible. It corresponds to the
sense in which people who are asleep or otherwise unconscious still
have minds. Their minds are ‘‘causally quiescent,’’ as David Armstrong
puts it (1981).
Computational functionalism describes the mind as a program,
which means that the function of the mind is to determine which
sequences of operations the brain has to perform. This presupposes a
certain mechanistic explanation of the brain as a program-controlled
computer, i.e., a mechanism with certain components that have certain
functions and a certain organization. Whether a system is a particular
kind of mechanism is an empirical question. In this important respect,
computational functionalism turns out to incorporate a strong empiri-
cal hypothesis.
Philosophers of mind have usually recognized that computationalism
is an empirical hypothesis in two respects. On the one hand, there is
the empirical question of whether a computer can be programmed to
exhibit all of the capacities that are peculiar to minds. This is one tradi-
tional domain of artificial intelligence. On the other hand, there is the
empirical question of whether all mental capacities can be explained by
program execution. This is one traditional domain of cognitive psychol-
ogy. As to neuroscience, computationalists have traditionally consid-
ered it irrelevant to testing their hypothesis, on the grounds that the
same software can be implemented by different kinds of hardware. This
attitude is unsatisfactory in two respects.
First, as we have seen, at least two important construals of function-
alism are such that they entail computationalism. But if computational-
ism is a logical consequence of the metaphysical doctrine of
functionalism, then the empirical status of computationalism is tied to
that of functionalism: if functionalism is a priori true (as some philoso-
phers believe), then computationalism should need no empirical
testing; conversely, any empirical disconfirmation of computationalism
should disconfirm functionalism too. An important advantage of my
proposed reformulation of functionalism is that it does not entail com-
putationalism. This leaves computationalism free to be an empirical
hypothesis about the specific functional organization of the brain,
which—when conjoined with functionalism—gives rise to computa-
tional functionalism.
But second, if computationalism is an empirical hypothesis to the
effect that mental capacities are the result of program execution, it isn’t
enough to test it by programming computers and attempting to explain
mental capacities by program execution. Indeed, to assume that this is
enough for testing it begs the question of whether the brain is the sort
of mechanism that could run mental programs at all—whether it is a
(program-controlled) computer. Assuming that the mind is the software
of the brain presupposes that the brain has components of the relevant
kinds, with the relevant functional and organizational properties.
Whether the brain is a kind of program-controlled computer is itself
an empirical question, and if the brain were not functionally organized
in the right way, computational functionalism about the mind would
turn out to be false. This shows computational functionalism to incor-
porate an empirical hypothesis that can be effectively tested only by
neuroscience. Whether brains are one kind of mechanism or another
can only be determined by studying brains.
This sense in which computational functionalism embodies an
empirical hypothesis is more fundamental than the other two. If the
brain is a (program-controlled) computer, then both classical artificial
intelligence and classical cognitive psychology have a fair chance of
succeeding. But if the brain is not a computer, then classical artificial
intelligence and cognitive psychology may or may not succeed in ways
that depend on the extent to which it is possible to reproduce the
capacities of systems that are not computers by executing programs. It
may be possible to reproduce all or many mental capacities by compu-
tational means even though the brain is not a computer, the mind is
something other than the programs running on the brain, or both. The
extent to which this is possible is a difficult question, which there is no
room to address here.
I have formulated and discussed computational functionalism pri-
marily using the notion of program execution, because the analogy
between minds and program-controlled computers is the motivation
behind the strong version of computational functionalism. There is no
question that many of those who felt the pull of the analogy between
some features of minds and some features of computers—such as Alan
Turing, John von Neumann, Jerry Fodor, Allen Newell, and Herbert
Simon—did so in part because of the explanatory power of program
But as I noticed at the beginning of this essay, computational func-
tionalism is ambiguous between a strong and a weak reading. It is
equally obvious that many other authors, who are (or were at one
point) sympathetic to the analogy between some features of minds and
some features of computers, such as Hilary Putnam, Robert Cummins,
Paul and Patricia Churchland, Michael Devitt and Kim Sterelny
(1999), and even Warren McCulloch and Walter Pitts (at least in 1943),
would resist the conclusion that the brain stores and executes pro-
grams. Is there a way to cash out their view without falling into the
trivial conclusion that the mind can be described computationally in
the sense in which anything else can? Indeed there is. Their view is cap-
tured by the more general formulation of computational functionalism
mentioned at the beginning of this section.
The account of computational explanation I sketched in Section 5
applies to all computing mechanisms, regardless of whether they are
controlled by programs. In fact, computation by program execution is
explicated in terms of the more general notion of computation tout
court. Roughly, computation is the manipulation of data and (possibly)
internal states according to an appropriate rule. (Computation by pro-
gram execution, then, is computation performed in response to instruc-
tions that encode the relevant rule.) The digital computers we use every
day compute by executing programs, but non-universal Turing
machines, finite state automata, and many connectionist networks per-
form computations without executing programs.
To cover theories that don’t appeal to program execution, all we
need to do is interpret computational functionalism in terms of compu-
tation tout court, without appealing to program execution. According
to this generalized computational functionalism, the mind is (some
aspect of) the computational organization of a (computing) mechanism,
regardless of whether that mechanism is a program-controlled com-
puter, a connectionist computing mechanism, or any other kind of
computing mechanism (e.g., a finite state automaton). Given the gener-
alized formulation, psychological explanations need not invoke the exe-
cution of programs—they can invoke either program execution or
some other kind of computation (connectionist or otherwise) that is
presumed to generate the behavior to be explained. This kind of expla-
nation is still a mechanistic explanation that appeals to the manipula-
tion of strings of digits in accordance with an appropriate rule by
appropriate components with appropriate functions. Hence, this gener-
alized formulation of computational functionalism still presupposes
that the brain has the relevant mechanistic properties, which can be
studied empirically by neuroscience. Given this generalization, compu-
tational functionalism is compatible with any computational theory of
mind, including connectionist computationalism.
Abandoning the strong analogy between minds and computers
(based on program execution), as the generalized version of computa-
tional functionalism does, produces a loss of explanatory power. The
generalized version of computational functionalism still appeals to
computation in explaining mental capacities, but it can no longer
appeal to the flexibility that comes with the ability to acquire, store,
modify, and execute different programs. Which computing mechanisms
are powerful enough to explain mental capacities? We do not have
room here to enter this complex debate (Macdonald and Macdonald
1995, Aizawa 2003). But by drawing attention to all functional and
organizational aspects of computing mechanisms at all relevant levels,
the account here proposed promises to push this debate forward.
The present account sheds light on some other old disputes too.
Two mental capacities that are especially contentious are intentionality
and consciousness. Several thought experiments have been proposed to
show that either intentionality or consciousness cannot be explained
computationally (e.g., Block 1978, Searle 1980, Maudlin 1989). The
putative failure of computational explanation is then assumed to affect
functionalism, presumably due to the assumption that functionalism
entails computationalism. But now we have seen that properly con-
strued, functionalism does not entail computationalism.
The only legitimate conclusion that may be drawn from these
thought experiments is that computationalism is insufficient to explain
intentionality or consciousness. This does not entail that computation-
alism explains no mental capacities, nor does it entail that intentional-
ity and consciousness cannot be explained functionally by some process
other than computation. In other words, if the intuitions behind those
thought experiments are accepted, then computationalism might still
explain many mental capacities, and functionalism might still be true of
the whole mind. Of course, the intuitions behind the thought experi-
ments are themselves in dispute. They remain an uncertain basis for
reaching consensus on these matters.
7. Functionalism, Computationalism, and Computational Functionalism
I have discussed three theses:
Functionalism: The mind is the functional organization of the
Computationalism: The functional organization of the brain is
Computational Functionalism (generalized): The mind is the com-
putational organization of the brain.
Computational functionalism is the conjunction of functionalism and
computationalism. I have offered a mechanistic framework within
which to make sense of these doctrines and exhibit some of their
mutual relations.
Functionalism does not entail computationalism, and by now it
should be easy to see why. There are plenty of functional organizations
that do not involve program execution or any other computational
process. That the mind is functionally constituted is consistent with
any non-computational mechanistic explanation applying to the mind.
Thus, it is a fallacy to attack functionalism by impugning some compu-
tationalist hypothesis or another (as done, e.g., by Churchland 2005).
Computationalism does not entail functionalism either. Computation-
alism is compatible with the mind being the computational organization
of the brain, but also with the mind being some non-computational but
still functional property of the brain, or even some non-functional prop-
erty of the brain, such as its physical composition, the speed of its action,
its color, or more plausibly, the intentional content or phenomenal quali-
ties of its states. In short, one may be a computationalist while opposing
or being neutral about functionalism, at least with respect to some
aspects of the mind.
Computationalism is an empirical, mechanistic hypothesis about the
brain. Even if the brain is a computing mechanism, the mind may or
may not be the brain’s computational organization—perhaps there are
aspects of the mind that have to do with other properties, e.g., the phe-
nomenal qualities of mental states. But if brains turn out not to be
computing mechanisms, then computationalism (and hence computa-
tional functionalism) is false. So, regardless of whether one agrees with
computational functionalism, one can still focus on whether the brain
is a computing mechanism and investigate computationalism. This, of
course, cannot be done by examining intuitions about imaginary sce-
narios (Block 1978, Searle 1980, Maudlin 1989)—it can only be done
by studying the functional organization of the brain empirically.
The standard formulations of computational functionalism in phi-
losophy of mind have made it difficult to discuss computationalism as
productively as it can be. They have convinced many philosophers
that computationalism is an a priori thesis, to be discussed by philo-
sophical arguments and thought experiments and assessed by the
extent to which it solves philosophical problems such as the mind-
body problem. This has led philosophers to ignore the fact that, in so
far as it has empirical content, computationalism embodies an empiri-
cal scientific hypothesis about the functional organization of the
brain, which comes in several varieties that ought to be assessed by
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... A cognitive system is an AI system that can be considered to be on par with humans in that it is similarly able to perform at least one type of task that corresponds to the exercise of a human cognitive capacity such as goal reasoning, perceiving and responding to di®erent types of stimuli from the environment, or analogy-making. Furthermore, we take a functionalist stance [Piccinini, 2010] in that cognitive systems do not have to con¯ne themselves to methods that are strictly biologically plausible but can employ any technologically realizable means of (re) creating human-level cognitive capacities. ...
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Many themes in the papers collected here are negative: there is no a priori knowledge or analytic truth; logic is not a theory of reasoning; a theory of truth conditions is not a theory of meaning; a purely objective account of meaning or mind cannot say what words mean or what it is like to see things in colour. Other themes are positive: theoretical reasoning has important practical aspects; meaning depends on how words are used to think with i.e. on how concepts function in reasoning, perception and action; the relevant uses or functions relate concepts to aspects of the environment and other things in the world; translation plays a central role in any adequate account of mind or meaning.
This book addresses a part of a problem. The problem is to determine the architecture of cognition, that is, the basic structures and mechanisms underlying cognitive processing. This is a multidimensional problem insofar as there appear to be many distinct types of mechanisms that interact in diverse ways during cognitive processing. Thus, we have memory, attention, learning, sensation, perception, and who knows what else, interacting to produce behavior. As a case in point, consider a bit of linguistic behavior. To tell a friend that I think Greg won a stunning victory, I must evidently rely on various bits of information stored in my memory, including who my friends are, who Greg is, what he won, and what natural languages I share with my friend. I must sense and perceive that my friend is within hearing distance, how loud I need to speak, how loud I am speaking, and whether my friend is paying attention. I must avail myself of what I know about the language I share with my friend, along with innumerable principles about human "folk psychology. " This book does not address the full range of contemporary theorizing about cognitive architecture, but only a part. It addresses theories of cognitive architecture that hypothesize that there exist cognitive representations, then begins to explore the possible structure of these representations. One of the leading hypotheses concerning the structure of cognitive representations is that it is akin to that found in symbolic logic.