The Chinese Room(leibniz.stanford.edu)

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The Chinese Room ArgumentFirst published Fri Mar 19, 2004; substantive revision Wed Oct 23, 2024

The argument and thought-experiment now generally known as the Chinese
Room Argument was first published in a 1980 article by American
philosopher John Searle (1932–2025). It has become one of the
best-known arguments in recent philosophy. Searle imagines himself
alone in a room following a computer program for responding to Chinese
characters slipped under the door. Searle understands nothing of
Chinese, and yet, by following the program for manipulating symbols
and numerals just as a computer does, he sends appropriate strings of
Chinese characters back out under the door, and this leads those
outside to mistakenly suppose there is a Chinese speaker in the room.

The narrow conclusion Searle draws from the argument is that
programming a digital computer may make it appear to understand
language but could not produce real understanding. Hence the
“Turing Test” is inadequate. Searle argues that the
thought experiment underscores the fact that computers merely use
syntactic rules to manipulate symbol strings, but have no
understanding of meaning or semantics. The broader conclusion of the
argument is that the theory that human minds are computer-like
computational or information processing systems is refuted. Instead
minds must result from biological processes; computers can at best
simulate these biological processes. Thus the argument has large
implications for semantics, philosophy of language and mind, theories
of consciousness, computer science, and cognitive science generally.
As a result, there have been many critical replies to the
argument.

1. Overview
2. Historical Background

2.1 Leibniz’ Mill
2.2 Turing’s Paper Machine
2.3 The Chinese Nation

3. The Chinese Room Argument
4. Replies to the Chinese Room Argument

4.1 The Systems Reply
4.2 The Robot Reply
4.3 The Brain Simulator Reply
4.4 The Other Minds Reply
4.5 The Intuition Reply
4.6 Advances in Artificial intelligence

5. The Larger Philosophical Issues

5.1 Syntax and Semantics
5.2 Intentionality
5.3 Mind and Body
5.4 Simulation, duplication and evolution

Conclusion
Bibliography
Academic Tools
Other Internet Resources
Related Entries

1. Overview

Work in Artificial Intelligence (AI) has produced computer programs
that can beat the world chess champion, control autonomous vehicles,
and defeat the best human players on the television quiz show
Jeopardy. By 2022 AI had evolved from personal digital
assistants (Alexa, Siri, Google Assistant) translating and answering
questions to using Large Language Models (LLMs) that could write
poems, college level essays, and computer programs, and could pass
exams designed to screen the entrants into graduate schools, the study
and practice of Law, and other “learned professions”. Our
experience shows that playing chess or Jeopardy, writing
essays, passing difficult exams, and carrying on a conversation, are
activities that require understanding and intelligence. Does computer
prowess at conversation, writing essays, and passing difficult
examinations then show that computers can understand language and be
intelligent? Will further development result in digital computers that
fully match or even exceed human intelligence?

Alan Turing
(1950), one of the pioneer theoreticians of computing, believed the
answer to these questions was “yes”. Turing proposed what
is now known as
‘The Turing Test’:
if a computer can pass for human in online chat, we should grant that
it is intelligent. By the late 1970s some AI researchers claimed that
computers already understood at least some natural language. In 1980
U.C. Berkeley philosopher John Searle introduced a short and
widely-discussed argument intended to show conclusively that it is
impossible for digital computers to understand language or think, now
or in the future

Searle argues that a good way to test a theory of mind, say a theory
that holds that understanding can be created by doing such and such,
is to imagine what it would be like to actually do what the theory
says will create understanding. Searle (1999) summarized his Chinese
Room Argument (hereinafter, CRA) concisely:

Imagine a native English speaker who knows no Chinese locked in a room
full of boxes of Chinese symbols (a data base) together with a book of
instructions for manipulating the symbols (the program). Imagine that
people outside the room send in other Chinese symbols which, unknown
to the person in the room, are questions in Chinese (the input). And
imagine that by following the instructions in the program the man in
the room is able to pass out Chinese symbols which are correct answers
to the questions (the output). The program enables the person in the
room to pass the Turing Test for understanding Chinese but he does not
understand a word of Chinese.

Searle goes on to say, “The point of the argument is this: if
the man in the room does not understand Chinese on the basis of
implementing the appropriate program for understanding Chinese then
neither does any other digital computer solely on that basis because
no computer, qua computer, has anything the man does not
have.”

Thirty years after introducing the CRA Searle 2010 describes the
conclusion in terms of consciousness and
intentionality:

I demonstrated years ago with the so-called Chinese Room Argument that
the implementation of the computer program is not by itself sufficient
for consciousness or intentionality (Searle 1980). Computation is
defined purely formally or syntactically, whereas minds have actual
mental or semantic contents, and we cannot get from syntactical to the
semantic just by having the syntactical operations and nothing else.
To put this point slightly more technically, the notion “same
implemented program” defines an equivalence class that is
specified independently of any specific physical realization. But such
a specification necessarily leaves out the biologically specific
powers of the brain to cause cognitive processes. A system, me, for
example, would not acquire an understanding of Chinese just by going
through the steps of a computer program that simulated the behavior of
a Chinese speaker (p.17).

“Intentionality” is a technical term for a feature of
mental and certain other things, namely being about something. Thus a
desire for a piece of chocolate as well as thoughts about real-world
Manhattan or fictional Harry Potter all display intentionality, as
will be discussed in more detail in section 5.2 below.

Searle’s shift from machine understanding to consciousness and
intentionality is not directly supported by the original 1980
argument. However the re-description of the conclusion indicates the
close connection between understanding and consciousness in
Searle’s later accounts of meaning and intentionality. Those who
don’t accept Searle’s linking of understanding and
consciousness might hold that running a program can create
understanding without necessarily creating consciousness, and
conversely a fancy robot might have dog level consciousness, desires,
and beliefs, without necessarily understanding natural language.

In moving to discussion of intentionality Searle seeks to develop the
broader implications of his argument. It aims to refute the
functionalist
approach to understanding minds, that is, the approach that holds
that mental states are defined by their causal roles, not by the stuff
(neurons, transistors) that plays those roles. The argument counts
especially against that form of functionalism known as
the Computational Theory of Mind
that treats minds as information processing systems. As a result of
its scope, as well as Searle’s clear and forceful writing style,
the Chinese Room argument has probably been the most widely discussed
philosophical argument in cognitive science to appear since the Turing
Test. By 1991 computer scientist Pat Hayes had defined Cognitive
Science as the ongoing research project of refuting Searle’s
argument. Cognitive psychologist Steven Pinker (1997) pointed out that
by the mid-1990s well over 100 articles had been published on
Searle’s thought experiment – and that discussion of it
was so pervasive on the Internet that Pinker found it a compelling
reason to remove his name from all Internet discussion lists.

This interest has not subsided, and the range of connections with the
argument has broadened. A search on Google Scholar for “Chinese
Room Argument” produces thousands of results, including papers
making connections between the argument and topics ranging from
embodied cognition to theater to talk psychotherapy to postmodern
views of truth and “our post-human future” – as well
as discussions of group or collective minds, and discussions of the
role of intuitions in philosophy. In 2007 a UK game company took the
name “The Chinese Room” in joking honor of
“...Searle’s critique of AI – that you could create
a system that gave the impression of intelligence without any actual
internal smarts.” This wide-range of discussion and implications
is a tribute to the argument’s simple clarity and centrality.

2. Historical Background

2.1 Leibniz’ Mill

Searle’s argument has four important antecedents. The first of
these is an argument set out by the philosopher and mathematician
Gottfried Leibniz (1646–1716). This argument, often known as
“Leibniz’ Mill”, appears as section 17 of
Leibniz’ Monadology. Like Searle’s argument,
Leibniz’ argument takes the form of a thought experiment.
Leibniz asks us to imagine a physical system, a machine, that behaves
in such a way that it supposedly thinks and has experiences
(“perception”).

17. Moreover, it must be confessed that perception and that which
depends upon it are inexplicable on mechanical grounds, that is to
say, by means of figures and motions. And supposing there were a
machine, so constructed as to think, feel, and have perception, it
might be conceived as increased in size, while keeping the same
proportions, so that one might go into it as into a mill. That being
so, we should, on examining its interior, find only parts which work
one upon another, and never anything by which to explain a perception.
Thus it is in a simple substance, and not in a compound or in a
machine, that perception must be sought for. [Robert Latta
translation]

Notice that Leibniz’s strategy here is to contrast the overt
behavior of the machine, which might appear to be the product of
conscious thought, with the way the machine operates internally. He
points out that these internal mechanical operations are just parts
moving from point to point, hence there is nothing that is conscious
or that can explain thinking, feeling or perceiving. For Leibniz
physical states are not sufficient for, nor constitutive of, mental
states.

To this day the mystery of consciousness remains; one can still follow
Leibniz’ suggestion and imagine a brain made so huge that one could
walk between the neurons, and all one would see is, at best, squirts
of neurotransmitters, and nothing to explain conscious experience,
including the experience of understanding language. Leibniz’ argument,
that no matter what a physical system does, there would be no
consciousness (and so materialism is refuted), is parallel to
Searle’s claim that no matter what syntactic processing there
is, there would be no understanding of meaning (and so strong AI
claims are refuted).

2.2 Turing’s Paper Machine

A second antecedent to the Chinese Room argument is the idea of a
paper machine, a computer implemented by a human. This idea is found
in the work of Alan Turing, for example in “Intelligent
Machinery” (1948). Turing writes there that he wrote a program
for a “paper machine” to play chess. A paper machine is a
kind of program, a series of simple steps like a computer program, but
written in natural language (e.g., English), and implemented by a
human. The human operator of the paper chess-playing machine need not
(otherwise) know how to play chess. All the operator does is follow
the instructions for generating moves on the chess board. In fact, the
operator need not even know that he or she is involved in playing
chess – the input and output strings, such as
“N–QB7” need mean nothing to the operator of the
paper machine.

As part of the WWII project to decipher German military encryption,
Turing had written English-language programs for human
“computers”, as these specialized workers were then known,
and these human computers did not need to know what the programs that
they implemented were doing.

One reason the idea of a human-plus-paper machine is important is that
it already raises questions about agency and understanding similar to
those in the CRA. Suppose I am alone in a closed room and follow an
instruction book for manipulating strings of symbols. I thereby
implement a paper machine that generates symbol strings such as
“N-KB3” that I write on pieces of paper and slip under the
door to someone ouside the room. Suppose further that prior to going
into the room I don’t know how to play chess, or even that there
is such a game. However, unbeknownst to me, in the room I am running
Turing’s chess program and the symbol strings I generate are
chess notation and are taken as chess moves by those outside the room.
They reply by sliding the symbols for their own moves back under the
door into the room. If all you see is the resulting sequence of moves
displayed on a chess board outside the room, you might think that
someone in the room knows how to play chess very well. Do I now know
how to play chess? Or is it the system (consisting of me, the manuals,
and the paper on which I manipulate strings of symbols) that is
playing chess? If I memorize the program and do the symbol
manipulations inside my head, do I then know how to play chess, albeit
with an odd phenomenology? Do someone’s conscious states matter
for whether or not they know how to play chess? If a digital computer
implements the same program, does the computer (or program or computer
plus program) then play chess, or merely simulate this?

By mid-century Turing was optimistic that the newly developed
electronic computers themselves would soon be able to exhibit
apparently intelligent behavior, answering questions posed in English
and carrying on conversations. Turing (1950) proposed what is now
known as the Turing Test: if a computer could pass for human in
on-line chat, it should be counted as intelligent.

A third antecedent of Searle’s argument was the work of
Searle’s colleague at Berkeley, Hubert Dreyfus. Dreyfus was an
early critic of the optimistic claims made by AI researchers. In 1965,
when Dreyfus was at MIT, he published a circa hundred page report
titled “Alchemy and Artificial Intelligence”. Dreyfus
argued that key features of human mental life could not be captured by
formal rules for manipulating symbols. Dreyfus moved to Berkeley in
1968 and in 1972 published his extended critique, “What
Computers Can’t Do”. Dreyfus’ primary research
interests were in Continental philosophy, with its focus on
consciousness, intentionality, and the role of intuition and the
inarticulated background in shaping our understandings. Dreyfus
identified several problematic assumptions in AI, including the view
that brains are like digital computers, and, again, the assumption
that understanding can be codified as explicit rules.

However by the late 1970s, as computers became faster and less
expensive, some in the burgeoning AI community started to claim that
their programs could understand English sentences, using a database of
background information. The work of one of these, Yale researcher
Roger Schank (Schank & Abelson 1977) came to Searle’s
attention. Schank’s team developed a technique called
“conceptual representation” that used
“scripts” to represent conceptual relations (related to
Conceptual Role Semantics). Searle’s argument was originally
presented in 1980 specifically as a response to the claim that AI
programs such as Schank’s literally understand the sentences
that they respond to.

2.3 The Chinese Nation

A fourth antecedent to the Chinese Room argument are thought
experiments involving myriad humans acting as a computer. In 1961
Anatoly Mickevich (pseudonym A. Dneprov) published “The
Game”, a story in which a stadium full of 1400 math students are
arranged to function as a digital computer (see Dneprov 1961 and the
English translation listed at Mickevich 1961, Other Internet
Resources). For 4 hours each student repeatedly does a bit of
calculation on binary numbers received from someone near them, then
passes the binary result onto someone nearby. They learn the next day
that they collectively translated a sentence from Portuguese into
their native Russian. Mickevich’s protagonist concludes
“We’ve proven that even the most perfect simulation of
machine thinking is not the thinking process itself, which is a higher
form of motion of living matter.”

Apparently independently, a similar consideration emerged in early
discussion of functionalist theories of minds and cognition (see
further discussion in section 5.3 below). Functionalists hold that
mental states are defined by the causal role they play in a system
(just as being a door stop is defined by what it does, not by what it
is made out of). Critics of functionalism were quick to turn its
proclaimed virtue of multiple realizability against it.

By emphasizing causal or information processing roles as the essence
of mental states, functionalism allowed us to understand creatures
with different physiology, for example extraterrestrials, to have the
same types of mental states as humans – pains, for example. But
it was pointed out that if extraterrestrial aliens, with some other
complex system in place of brains, could realize the functional
properties that constituted mental states, then, presumably so could
systems even less like human brains. The computational form of
functionalism, which holds that the defining role of each mental state
is its role in information processing or computation, is particularly
vulnerable to this maneuver, since a wide variety of systems with
simple components are computationally equivalent (see e.g., Maudlin
1989 for discussion of a computer built from buckets of water).
Critics asked if it was really plausible that these inorganic systems
could have mental states or feel pain.

Daniel Dennett (1978) reports that in 1974 Lawrence Davis gave a
colloquium at MIT in which he presented one such unorthodox
implementation. Dennett summarizes Davis’ thought experiment as
follows:

Let a functionalist theory of pain (whatever its details) be
instantiated by a system the subassemblies of which are not such
things as C-fibers and reticular systems but telephone lines and
offices staffed by people. Perhaps it is a giant robot controlled by
an army of human beings that inhabit it. When the theory’s
functionally characterized conditions for pain are now met we must
say, if the theory is true, that the robot is in pain. That is, real
pain, as real as our own, would exist in virtue of the perhaps
disinterested and businesslike activities of these bureaucratic teams,
executing their proper functions.

In “Troubles with Functionalism”, also published in 1978,
Ned Block envisions the entire population of China implementing the
functions of neurons in the brain. This scenario has subsequently been
called “The Chinese Nation” or “The Chinese
Gym”. We can suppose that every Chinese citizen would be given a
call-list of phone numbers, and at a preset time on implementation
day, designated “input” citizens would initiate the
process by calling those on their call-list. When any citizen’s
phone rang, he or she would then phone those on his or her list, who
would in turn contact yet others. No phone message need be exchanged;
all that is required is the pattern of calling. The call-lists would
be constructed in such a way that the patterns of calls implemented
the same patterns of activation that occur between neurons in
someone’s brain when that person is in a mental state –
pain, for example. The phone calls play the same functional role as
neurons causing one another to fire. Block was primarily interested in
qualia, and in particular, whether it is plausible to hold that the
population of China might collectively be in pain, while no individual
member of the population experienced any pain, but the thought
experiment applies to any mental states and operations, including
understanding language.

Thus Block’s thought experiment, as with those of Davis and
Dennett, is a system of many humans rather than one. The focus is on
consciousness, but to the extent that Searle’s argument also
involves consciousness, the thought experiment is closely related to
Searle’s. Cole (1984) tries to pump intuitions in the reverse
direction by setting out a thought experiment in which each of his
neurons is itself conscious, and fully aware of its actions including
being doused with neurotransmitters, undergoing action potentials, and
squirting neurotransmitters at its neighbors. Cole argues that his
conscious neurons would find it implausible that their collective
activity produced a consciousness and other cognitive competences,
including understanding English, that the neurons lack. That is, the
mental states achieved by the activity of my neurons are my mental
states, not those of any of my neurons – so if my neurons
thought in Chinese (only), that would not show that they don’t
collectively produce someone –me– who understands English
but not Chinese.) Cole suggests that the intuitions of implementing
systems are not to be trusted.

3. The Chinese Room Argument

In 1980 John Searle published “Minds, Brains and Programs”
in the journal The Behavioral and Brain Sciences. In this
article, Searle sets out the argument, and then replies to the
half-dozen main objections that had been raised during his earlier
presentations at various university campuses (see next section). In
addition, Searle’s article in BBS was published along
with comments and criticisms by 27 cognitive science researchers.
These 27 comments were followed by Searle’s replies to his
critics.

In the decades following its publication, the Chinese Room argument
was the subject of very many discussions. By 1984, Searle presented
the Chinese Room argument in a book, Minds, Brains and
Science. In January 1990, the popular periodical Scientific
American took the debate to a general scientific audience. Searle
included the Chinese Room Argument in his contribution, “Is the
Brain’s Mind a Computer Program?”, and Searle’s
piece was followed by a responding article, “Could a Machine
Think?”, written by philosophers Paul and Patricia Churchland.
Soon thereafter Searle had a published exchange about the Chinese Room
with another leading philosopher, Jerry Fodor (in Rosenthal (ed.)
1991).

The heart of the argument is Searle imagining himself following a
symbol-processing program written in English (which is what Turing
called “a paper machine”). The English speaker (Searle)
sitting in the room follows English instructions for manipulating
Chinese symbols, whereas a computer “follows” (in some
sense) a program written in a computing language. The human produces
the appearance of understanding Chinese by following the symbol
manipulating instructions, but does not thereby come to understand
Chinese. Since a computer just does what the human does –
manipulate symbols on the basis of their syntax alone – no
computer, merely by following a program, comes to genuinely understand
Chinese.

This narrow argument, based closely on the Chinese Room scenario, is
specifically directed at a position Searle calls “Strong
AI”. Strong AI is the view that suitably programmed computers
(or the programs themselves) can understand natural language and
actually have other mental capabilities similar to the humans whose
behavior they mimic. According to Strong AI, these computers really
play chess intelligently, make clever moves, or understand language.
By contrast, “weak AI” is the much more modest claim that
computers are merely useful in psychology, linguistics, and other
areas, in part because they can simulate mental abilities. But weak AI
makes no claim that computers actually understand or are intelligent.
The Chinese Room argument is not directed at weak AI, nor does it
purport to show that no machine can think – Searle says that
brains are machines, and brains think. The argument is directed at the
view that formal computations on symbols can produce thought.

We might summarize the narrow argument as a reductio ad
absurdum against Strong AI as follows. Let L be a natural
language, and let us say that a “program for L” is a
program for conversing fluently in L. A computing system is any
system, human or otherwise, that can run a program.

If Strong AI is true, then there is a program for
Chinese, C, such that if any computing system
runs C, that system thereby comes to understand Chinese.

I could run C without thereby coming to understand
Chinese.

Therefore Strong AI is false.

The first premise elucidates the claim of Strong AI. The second
premise is supported by the Chinese Room thought experiment. The
conclusion of this narrow argument is that running a program cannot
endow the system with language understanding. (There are other ways of
understanding the structure of the argument. It may be relevant to
understand some of the claims as counterfactual: e.g. “there is
a program” in premise 1 as meaning there could be a program,
etc. On this construal the argument involves modal logic, the logic of
possibility and necessity (see Damper 2006 for the CRA reconstructed
as a modal 5 step reductio and Shaffer 2009 in response)).

It is also worth noting that the claim made by Strong AI in the first
premise above attributes understanding to “the system”.
Exactly what Strong-AI supposes will acquire understanding when the
program runs is crucial to the success or failure of the CRA. Schank
1978 has a title that claims their group’s computer, a physical
device, understands, but in the body of the paper he claims that the
program [“SAM”] is doing the understanding: SAM, Schank
says “...understands stories about domains about which it has
knowledge” (p. 133). As we will see in the next section (4),
these issues about the identity of the understander (the cpu? the
program? the system? something else?) quickly came to the fore for
critics of the CRA. Searle’s wider argument includes the claim
that the thought experiment shows more generally that one cannot get
semantics (meaning) from syntax (formal symbol manipulation). That
larger claim and related issues are discussed in section 5: The Larger
Philosophical Issues.

4. Replies to the Chinese Room Argument

Criticisms of the narrow Chinese Room argument against Strong AI have
often followed three main lines, which can be distinguished by how
much they concede:

(1) Some critics concede that the man in the room doesn’t
understand Chinese, but hold that nevertheless running the program may
create comprehension of Chinese by something other than the room
operator. These critics object to the inference from the claim that
the man in the room does not understand Chinese to the
conclusion that no understanding has been created. There might
be understanding by a larger, smaller, or different, entity than the
man rustling papers in the room. This is the strategy of The Systems
Reply and the Virtual Mind Reply. These replies hold that the output
of the room might reflect real understanding of Chinese, but the
understanding would not be that of the room operator. Thus
Searle’s claim that he doesn’t understand Chinese while
running the room is conceded, but his claim that there is no
understanding of the questions in Chinese, and that computationalism
is false, is denied.

(2) Other critics concede Searle’s claim that just running a
natural language processing program as described in the CR scenario
does not create any understanding, whether by a human or a computer
system. But these critics hold that a variation on the
computer system could understand. The variant might be a computer
embedded in a robotic body, having interaction with the physical world
via sensors and motors (“The Robot Reply”), or it might be
a system that simulated the detailed operation of an entire human
brain, neuron by neuron (“the Brain Simulator Reply”).

(3) Finally, some critics do not concede even the narrow point against
AI. These critics hold that the man in the original Chinese Room
scenario might understand Chinese, despite Searle’s denials, or
that the scenario is impossible. For example, critics have argued that
our intuitions in such cases are unreliable. Other critics have held
that it all depends on what one means by “understand”
– points discussed in the section on The Intuition Reply. Others
(e.g. Sprevak 2007) object to the assumption that any system (e.g.
Searle in the room) can run any computer program. And finally some
have argued that if it is not reasonable to attribute understanding on
the basis of the behavior exhibited by the Chinese Room, then it would
not be reasonable to attribute understanding to humans on the basis of
similar behavioral evidence (Searle calls this last the “Other
Minds Reply”). This objection to the CRA is that we should be
willing to attribute understanding in the Chinese Room on the basis of
the overt behavior, just as we do with other humans (and some
animals), and as we w