Chinese room

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The Chinese room argument holds that a digital computer executing a program cannot be shown to have a "mind", "understanding" or "consciousness", regardless of how intelligently or human-like the program may make the computer behave. The argument was first presented by philosopher John Searle in his paper, "Minds, Brains, and Programs", published in Behavioral and Brain Sciences in 1980. It has been widely discussed in the years since. The centerpiece of the argument is a thought experiment known as the Chinese room.

The argument is directed against the philosophical positions of functionalism and computationalism, which hold that the mind may be viewed as an information-processing system operating on formal symbols, and that simulation of a given mental state is sufficient for its presence. Specifically, the argument is intended to refute a position Searle calls strong AI: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."

Although it was originally presented in reaction to the statements of artificial intelligence (AI) researchers, it is not an argument against the goals of mainstream AI research, because it does not show a limit in the amount of "intelligent" behavior a machine can display. The argument applies only to digital computers running programs and does not apply to machines in general.

Chinese room thought experiment

John Searle in December 2005

Searle's thought experiment begins with this hypothetical premise: suppose that artificial intelligence research has succeeded in constructing a computer that behaves as if it understands Chinese. It takes Chinese characters as input and, by following the instructions of a computer program, produces other Chinese characters, which it presents as output. Suppose, says Searle, that this computer performs its task so convincingly that it comfortably passes the Turing test: it convinces a human Chinese speaker that the program is itself a live Chinese speaker. To all of the questions that the person asks, it makes appropriate responses, such that any Chinese speaker would be convinced that they are talking to another Chinese-speaking human being.

The question Searle wants to answer is this: does the machine literally "understand" Chinese? Or is it merely simulating the ability to understand Chinese? Searle calls the first position "strong AI" and the latter "weak AI".

Searle then supposes that he is in a closed room and has a book with an English version of the computer program, along with sufficient papers, pencils, erasers, and filing cabinets. Searle could receive Chinese characters through a slot in the door, process them according to the program's instructions, and produce Chinese characters as output. If the computer had passed the Turing test this way, it follows, says Searle, that he would do so as well, simply by running the program manually.

Searle asserts that there is no essential difference between the roles of the computer and himself in the experiment. Each simply follows a program, step-by-step, producing a behavior which is then interpreted by the user as demonstrating intelligent conversation. However, Searle himself would not be able to understand the conversation. ("I don't speak a word of Chinese," he points out.) Therefore, he argues, it follows that the computer would not be able to understand the conversation either.

Searle argues that, without "understanding" (or "intentionality"), we cannot describe what the machine is doing as "thinking" and, since it does not think, it does not have a "mind" in anything like the normal sense of the word. Therefore, he concludes that the "strong AI" hypothesis is false.

History

Gottfried Leibniz made a similar argument in 1714 against mechanism (the position that the mind is a machine and nothing more). Leibniz used the thought experiment of expanding the brain until it was the size of a mill. Leibniz found it difficult to imagine that a "mind" capable of "perception" could be constructed using only mechanical processes. In the 1961 short story "The Game" by Anatoly Dneprov, a stadium of people act as switches and memory cells implementing a program to translate a sentence of Portuguese, a language that none of them knows. In 1974, Lawrence Davis imagined duplicating the brain using telephone lines and offices staffed by people, and in 1978 Ned Block envisioned the entire population of China involved in such a brain simulation. This thought experiment is called the China brain, also the "Chinese Nation" or the "Chinese Gym".

The Chinese Room Argument was introduced in Searle's 1980 paper "Minds, Brains, and Programs", published in Behavioral and Brain Sciences. It eventually became the journal's "most influential target article", generating an enormous number of commentaries and responses in the ensuing decades, and Searle has continued to defend and refine the argument in many papers, popular articles and books. David Cole writes that "the Chinese Room argument has probably been the most widely discussed philosophical argument in cognitive science to appear in the past 25 years".

Most of the discussion consists of attempts to refute it. "The overwhelming majority", notes BBS editor Stevan Harnad, "still think that the Chinese Room Argument is dead wrong". The sheer volume of the literature that has grown up around it inspired Pat Hayes to comment that the field of cognitive science ought to be redefined as "the ongoing research program of showing Searle's Chinese Room Argument to be false".

Searle's argument has become "something of a classic in cognitive science", according to Harnad. Varol Akman agrees, and has described the original paper as "an exemplar of philosophical clarity and purity".

Philosophy

Although the Chinese Room argument was originally presented in reaction to the statements of artificial intelligence researchers, philosophers have come to consider it as an important part of the philosophy of mind. It is a challenge to functionalism and the computational theory of mind, and is related to such questions as the mind–body problem, the problem of other minds, the symbol-grounding problem, and the hard problem of consciousness.

Strong AI

Searle identified a philosophical position he calls "strong AI":

The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds.

The definition depends on the distinction between simulating a mind and actually having a mind. Searle writes that "according to Strong AI, the correct simulation really is a mind. According to Weak AI, the correct simulation is a model of the mind."

The claim is implicit in some of the statements of early AI researchers and analysts. For example, in 1955, AI founder Herbert A. Simon declared that "there are now in the world machines that think, that learn and create". Simon, together with Allen Newell and Cliff Shaw, after having completed the first "AI" program, the Logic Theorist, claimed that they had "solved the venerable mind–body problem, explaining how a system composed of matter can have the properties of mind." John Haugeland wrote that "AI wants only the genuine article: machines with minds, in the full and literal sense. This is not science fiction, but real science, based on a theoretical conception as deep as it is daring: namely, we are, at root, computers ourselves."

Searle also ascribes the following claims to advocates of strong AI:

• AI systems can be used to explain the mind;
• The study of the brain is irrelevant to the study of the mind; and
• The Turing test is adequate for establishing the existence of mental states.

Strong AI as computationalism or functionalism

In more recent presentations of the Chinese room argument, Searle has identified "strong AI" as "computer functionalism" (a term he attributes to Daniel Dennett). Functionalism is a position in modern philosophy of mind that holds that we can define mental phenomena (such as beliefs, desires, and perceptions) by describing their functions in relation to each other and to the outside world. Because a computer program can accurately represent functional relationships as relationships between symbols, a computer can have mental phenomena if it runs the right program, according to functionalism.

Stevan Harnad argues that Searle's depictions of strong AI can be reformulated as "recognizable tenets of computationalism, a position (unlike "strong AI") that is actually held by many thinkers, and hence one worth refuting." Computationalism is the position in the philosophy of mind which argues that the mind can be accurately described as an information-processing system.

Each of the following, according to Harnad, is a "tenet" of computationalism:

• Mental states are computational states (which is why computers can have mental states and help to explain the mind);
• Computational states are implementation-independent—in other words, it is the software that determines the computational state, not the hardware (which is why the brain, being hardware, is irrelevant); and that
• Since implementation is unimportant, the only empirical data that matters is how the system functions; hence the Turing test is definitive.

Strong AI vs. biological naturalism

Searle holds a philosophical position he calls "biological naturalism": that consciousness and understanding require specific biological machinery that are found in brains. He writes "brains cause minds" and that "actual human mental phenomena [are] dependent on actual physical–chemical properties of actual human brains". Searle argues that this machinery (known to neuroscience as the "neural correlates of consciousness") must have some causal powers that permit the human experience of consciousness. Searle's belief in the existence of these powers has been criticized.

Searle does not disagree with the notion that machines can have consciousness and understanding, because, as he writes, "we are precisely such machines". Searle holds that the brain is, in fact, a machine, but that the brain gives rise to consciousness and understanding using machinery that is non-computational. If neuroscience is able to isolate the mechanical process that gives rise to consciousness, then Searle grants that it may be possible to create machines that have consciousness and understanding. However, without the specific machinery required, Searle does not believe that consciousness can occur.

Biological naturalism implies that one cannot determine if the experience of consciousness is occurring merely by examining how a system functions, because the specific machinery of the brain is essential. Thus, biological naturalism is directly opposed to both behaviorism and functionalism (including "computer functionalism" or "strong AI"). Biological naturalism is similar to identity theory (the position that mental states are "identical to" or "composed of" neurological events); however, Searle has specific technical objections to identity theory. Searle's biological naturalism and strong AI are both opposed to Cartesian dualism, the classical idea that the brain and mind are made of different "substances". Indeed, Searle accuses strong AI of dualism, writing that "strong AI only makes sense given the dualistic assumption that, where the mind is concerned, the brain doesn't matter."

Consciousness

Searle's original presentation emphasized "understanding"—that is, mental states with what philosophers call "intentionality"—and did not directly address other closely related ideas such as "consciousness". However, in more recent presentations Searle has included consciousness as the real target of the argument.

Computational models of consciousness are not sufficient by themselves for consciousness. The computational model for consciousness stands to consciousness in the same way the computational model of anything stands to the domain being modelled. Nobody supposes that the computational model of rainstorms in London will leave us all wet. But they make the mistake of supposing that the computational model of consciousness is somehow conscious. It is the same mistake in both cases.

— John R. Searle, Consciousness and Language, p. 16

David Chalmers writes "it is fairly clear that consciousness is at the root of the matter" of the Chinese room.

Colin McGinn argues that the Chinese room provides strong evidence that the hard problem of consciousness is fundamentally insoluble. The argument, to be clear, is not about whether a machine can be conscious, but about whether it (or anything else for that matter) can be shown to be conscious. It is plain that any other method of probing the occupant of a Chinese room has the same difficulties in principle as exchanging questions and answers in Chinese. It is simply not possible to divine whether a conscious agency or some clever simulation inhabits the room.

Searle argues that this is only true for an observer outside of the room. The whole point of the thought experiment is to put someone inside the room, where they can directly observe the operations of consciousness. Searle claims that from his vantage point within the room there is nothing he can see that could imaginably give rise to consciousness, other than himself, and clearly he does not have a mind that can speak Chinese.

Applied ethics

Sitting in the combat information center aboard a warship – proposed as a real-life analog to the Chinese Room

Patrick Hew used the Chinese Room argument to deduce requirements from military command and control systems if they are to preserve a commander's moral agency. He drew an analogy between a commander in their command center and the person in the Chinese Room, and analyzed it under a reading of Aristotle’s notions of "compulsory" and "ignorance". Information could be "down converted" from meaning to symbols, and manipulated symbolically, but moral agency could be undermined if there was inadequate 'up conversion' into meaning. Hew cited examples from the USS Vincennes incident.

Computer science

The Chinese room argument is primarily an argument in the philosophy of mind, and both major computer scientists and artificial intelligence researchers consider it irrelevant to their fields. However, several concepts developed by computer scientists are essential to understanding the argument, including symbol processing, Turing machines, Turing completeness, and the Turing test.

Strong AI vs. AI research

Searle's arguments are not usually considered an issue for AI research. Stuart Russell and Peter Norvig observe that most AI researchers "don't care about the strong AI hypothesis—as long as the program works, they don't care whether you call it a simulation of intelligence or real intelligence." The primary mission of artificial intelligence research is only to create useful systems that act intelligently, and it does not matter if the intelligence is "merely" a simulation.

Searle does not disagree that AI research can create machines that are capable of highly intelligent behavior. The Chinese room argument leaves open the possibility that a digital machine could be built that acts more intelligently than a person, but does not have a mind or intentionality in the same way that brains do.

Searle's "strong AI" should not be confused with "strong AI" as defined by Ray Kurzweil and other futurists, who use the term to describe machine intelligence that rivals or exceeds human intelligence. Kurzweil is concerned primarily with the amount of intelligence displayed by the machine, whereas Searle's argument sets no limit on this. Searle argues that even a superintelligent machine would not necessarily have a mind and consciousness.

Turing test

The "standard interpretation" of the Turing Test, in which player C, the interrogator, is given the task of trying to determine which player – A or B – is a computer and which is a human. The interrogator is limited to using the responses to written questions to make the determination. Image adapted from Saygin, et al. 2000.

The Chinese room implements a version of the Turing test. Alan Turing introduced the test in 1950 to help answer the question "can machines think?" In the standard version, a human judge engages in a natural language conversation with a human and a machine designed to generate performance indistinguishable from that of a human being. All participants are separated from one another. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test.

Turing then considered each possible objection to the proposal "machines can think", and found that there are simple, obvious answers if the question is de-mystified in this way. He did not, however, intend for the test to measure for the presence of "consciousness" or "understanding". He did not believe this was relevant to the issues that he was addressing. He wrote:

I do not wish to give the impression that I think there is no mystery about consciousness. There is, for instance, something of a paradox connected with any attempt to localise it. But I do not think these mysteries necessarily need to be solved before we can answer the question with which we are concerned in this paper.

To Searle, as a philosopher investigating in the nature of mind and consciousness, these are the relevant mysteries. The Chinese room is designed to show that the Turing test is insufficient to detect the presence of consciousness, even if the room can behave or function as a conscious mind would.

Symbol processing

The Chinese room (and all modern computers) manipulate physical objects in order to carry out calculations and do simulations. AI researchers Allen Newell and Herbert A. Simon called this kind of machine a physical symbol system. It is also equivalent to the formal systems used in the field of mathematical logic.

Searle emphasizes the fact that this kind of symbol manipulation is syntactic (borrowing a term from the study of grammar). The computer manipulates the symbols using a form of syntax rules, without any knowledge of the symbol's semantics (that is, their meaning).

Newell and Simon had conjectured that a physical symbol system (such as a digital computer) had all the necessary machinery for "general intelligent action", or, as it is known today, artificial general intelligence. They framed this as a philosophical position, the physical symbol system hypothesis: "A physical symbol system has the necessary and sufficient means for general intelligent action." The Chinese room argument does not refute this, because it is framed in terms of "intelligent action", i.e. the external behavior of the machine, rather than the presence or absence of understanding, consciousness and mind.

Chinese room and Turing completeness

The Chinese room has a design analogous to that of a modern computer. It has a Von Neumann architecture, which consists of a program (the book of instructions), some memory (the papers and file cabinets), a CPU which follows the instructions (the man), and a means to write symbols in memory (the pencil and eraser). A machine with this design is known in theoretical computer science as "Turing complete", because it has the necessary machinery to carry out any computation that a Turing machine can do, and therefore it is capable of doing a step-by-step simulation of any other digital machine, given enough memory and time. Alan Turing writes, "all digital computers are in a sense equivalent." The widely accepted Church–Turing thesis holds that any function computable by an effective procedure is computable by a Turing machine.

The Turing completeness of the Chinese room implies that it can do whatever any other digital computer can do (albeit much, much more slowly). Thus, if the Chinese room does not or can not contain a Chinese-speaking mind, then no other digital computer can contain a mind. Some replies to Searle begin by arguing that the room, as described, cannot have a Chinese-speaking mind. Arguments of this form, according to Stevan Harnad, are "no refutation (but rather an affirmation)" of the Chinese room argument, because these arguments actually imply that no digital computers can have a mind.

There are some critics, such as Hanoch Ben-Yami, who argue that the Chinese room cannot simulate all the abilities of a digital computer, such as being able to determine the current time.

Complete argument

Searle has produced a more formal version of the argument of which the Chinese Room forms a part. He presented the first version in 1984. The version given below is from 1990. The only part of the argument which should be controversial is A3 and it is this point which the Chinese room thought experiment is intended to prove.

He begins with three axioms:

(A1) "Programs are formal (syntactic)."

A program uses syntax to manipulate symbols and pays no attention to the semantics of the symbols. It knows where to put the symbols and how to move them around, but it doesn't know what they stand for or what they mean. For the program, the symbols are just physical objects like any others.

(A2) "Minds have mental contents (semantics)."

Unlike the symbols used by a program, our thoughts have meaning: they represent things and we know what it is they represent.

(A3) "Syntax by itself is neither constitutive of nor sufficient for semantics."

This is what the Chinese room thought experiment is intended to prove: the Chinese room has syntax (because there is a man in there moving symbols around). The Chinese room has no semantics (because, according to Searle, there is no one or nothing in the room that understands what the symbols mean). Therefore, having syntax is not enough to generate semantics.

Searle posits that these lead directly to this conclusion:

(C1) Programs are neither constitutive of nor sufficient for minds.

This should follow without controversy from the first three: Programs don't have semantics. Programs have only syntax, and syntax is insufficient for semantics. Every mind has semantics. Therefore no programs are minds.

This much of the argument is intended to show that artificial intelligence can never produce a machine with a mind by writing programs that manipulate symbols. The remainder of the argument addresses a different issue. Is the human brain running a program? In other words, is the computational theory of mind correct? He begins with an axiom that is intended to express the basic modern scientific consensus about brains and minds:

(A4) Brains cause minds.

Searle claims that we can derive "immediately" and "trivially" that:

(C2) Any other system capable of causing minds would have to have causal powers (at least) equivalent to those of brains.

Brains must have something that causes a mind to exist. Science has yet to determine exactly what it is, but it must exist, because minds exist. Searle calls it "causal powers". "Causal powers" is whatever the brain uses to create a mind. If anything else can cause a mind to exist, it must have "equivalent causal powers". "Equivalent causal powers" is whatever else that could be used to make a mind.

And from this he derives the further conclusions:

(C3) Any artifact that produced mental phenomena, any artificial brain, would have to be able to duplicate the specific causal powers of brains, and it could not do that just by running a formal program.

This follows from C1 and C2: Since no program can produce a mind, and "equivalent causal powers" produce minds, it follows that programs do not have "equivalent causal powers."

(C4) The way that human brains actually produce mental phenomena cannot be solely by virtue of running a computer program.

Since programs do not have "equivalent causal powers", "equivalent causal powers" produce minds, and brains produce minds, it follows that brains do not use programs to produce minds.

Replies

Replies to Searle's argument may be classified according to what they claim to show:

• Those which identify who speaks Chinese
• Those which demonstrate how meaningless symbols can become meaningful
• Those which suggest that the Chinese room should be redesigned in some way
• Those which contend that Searle's argument is misleading
• Those which argue that the argument makes false assumptions about subjective conscious experience and therefore proves nothing

Some of the arguments (robot and brain simulation, for example) fall into multiple categories.

Systems and virtual mind replies: finding the mind

These replies attempt to answer the question: since the man in the room doesn't speak Chinese, where is the "mind" that does? These replies address the key ontological issues of mind vs. body and simulation vs. reality. All of the replies that identify the mind in the room are versions of "the system reply".

System reply

The basic version argues that it is the "whole system" that understands Chinese. While the man understands only English, when he is combined with the program, scratch paper, pencils and file cabinets, they form a system that can understand Chinese. "Here, understanding is not being ascribed to the mere individual; rather it is being ascribed to this whole system of which he is a part" Searle explains. The fact that man does not understand Chinese is irrelevant, because it is only the system as a whole that matters.

Searle notes that (in this simple version of the reply) the "system" is nothing more than a collection of ordinary physical objects; it grants the power of understanding and consciousness to "the conjunction of that person and bits of paper" without making any effort to explain how this pile of objects has become a conscious, thinking being. Searle argues that no reasonable person should be satisfied with the reply, unless they are "under the grip of an ideology;" In order for this reply to be remotely plausible, one must take it for granted that consciousness can be the product of an information processing "system", and does not require anything resembling the actual biology of the brain.

Searle then responds by simplifying this list of physical objects: he asks what happens if the man memorizes the rules and keeps track of everything in his head? Then the whole system consists of just one object: the man himself. Searle argues that if the man doesn't understand Chinese then the system doesn't understand Chinese either because now "the system" and "the man" both describe exactly the same object.

Critics of Searle's response argue that the program has allowed the man to have two minds in one head. If we assume a "mind" is a form of information processing, then the theory of computation can account for two computations occurring at once, namely (1) the computation for universal programmability (which is the function instantiated by the person and note-taking materials independently from any particular program contents) and (2) the computation of the Turing machine that is described by the program (which is instantiated by everything including the specific program). The theory of computation thus formally explains the open possibility that the second computation in the Chinese Room could entail a human-equivalent semantic understanding of the Chinese inputs. The focus belongs on the program's Turing machine rather than on the person's. However, from Searle's perspective, this argument is circular. The question at issue is whether consciousness is a form of information processing, and this reply requires that we make that assumption.

More sophisticated versions of the systems reply try to identify more precisely what "the system" is and they differ in exactly how they describe it. According to these replies, the "mind that speaks Chinese" could be such things as: the "software", a "program", a "running program", a simulation of the "neural correlates of consciousness", the "functional system", a "simulated mind", an "emergent property", or "a virtual mind" (Marvin Minsky's version of the systems reply, described below).

Virtual mind reply

The term "virtual" is used in computer science to describe an object that appears to exist "in" a computer (or computer network) only because software makes it appear to exist. The objects "inside" computers (including files, folders, and so on) are all "virtual", except for the computer's electronic components. Similarly, Minsky argues, a computer may contain a "mind" that is virtual in the same sense as virtual machines, virtual communities and virtual reality.

To clarify the distinction between the simple systems reply given above and virtual mind reply, David Cole notes that two simulations could be running on one system at the same time: one speaking Chinese and one speaking Korean. While there is only one system, there can be multiple "virtual minds," thus the "system" cannot be the "mind".

Searle responds that such a mind is, at best, a simulation, and writes: "No one supposes that computer simulations of a five-alarm fire will burn the neighborhood down or that a computer simulation of a rainstorm will leave us all drenched." Nicholas Fearn responds that, for some things, simulation is as good as the real thing. "When we call up the pocket calculator function on a desktop computer, the image of a pocket calculator appears on the screen. We don't complain that 'it isn't really a calculator', because the physical attributes of the device do not matter." The question is, is the human mind like the pocket calculator, essentially composed of information? Or is the mind like the rainstorm, something other than a computer, and not realizable in full by a computer simulation? For decades, this question of simulation has lead AI researchers and philosophers to consider whether the term "synthetic intelligence" is more appropriate than the common description of such intelligences as "artificial."

These replies provide an explanation of exactly who it is that understands Chinese. If there is something besides the man in the room that can understand Chinese, Searle can't argue that (1) the man doesn't understand Chinese, therefore (2) nothing in the room understands Chinese. This, according to those who make this reply, shows that Searle's argument fails to prove that "strong AI" is false.

These replies, by themselves, do not provide any evidence that strong AI exists or can exist, however. They do not show that the system (or the virtual mind) understands Chinese, other than the hypothetical premise that it passes the Turing Test. Searle argues that, if we are to consider Strong AI remotely plausible, the Chinese Room is an example that requires explanation, and it is difficult or impossible to explain how consciousness might "emerge" from the room or how the system would have consciousness. As Searle writes "the systems reply simply begs the question by insisting that the system must understand Chinese" and thus is dodging the question or hopelessly circular.

Robot and semantics replies: finding the meaning

As far as the person in the room is concerned, the symbols are just meaningless "squiggles." But if the Chinese room really "understands" what it is saying, then the symbols must get their meaning from somewhere. These arguments attempt to connect the symbols to the things they symbolize. These replies address Searle's concerns about intentionality, symbol grounding and syntax vs. semantics.

Robot reply

Suppose that instead of a room, the program was placed into a robot that could wander around and interact with its environment. This would allow a "causal connection" between the symbols and things they represent. Hans Moravec comments: "If we could graft a robot to a reasoning program, we wouldn't need a person to provide the meaning anymore: it would come from the physical world."

Searle's reply is to suppose that, unbeknownst to the individual in the Chinese room, some of the inputs came directly from a camera mounted on a robot, and some of the outputs were used to manipulate the arms and legs of the robot. Nevertheless, the person in the room is still just following the rules, and does not know what the symbols mean. Searle writes "he doesn't see what comes into the robot's eyes."

Derived meaning

Some respond that the room, as Searle describes it, is connected to the world: through the Chinese speakers that it is "talking" to and through the programmers who designed the knowledge base in his file cabinet. The symbols Searle manipulates are already meaningful, they're just not meaningful to him.

Searle says that the symbols only have a "derived" meaning, like the meaning of words in books. The meaning of the symbols depends on the conscious understanding of the Chinese speakers and the programmers outside the room. The room, like a book, has no understanding of its own.

Commonsense knowledge / contextualist reply

Some have argued that the meanings of the symbols would come from a vast "background" of commonsense knowledge encoded in the program and the filing cabinets. This would provide a "context" that would give the symbols their meaning.

Searle agrees that this background exists, but he does not agree that it can be built into programs. Hubert Dreyfus has also criticized the idea that the "background" can be represented symbolically.

To each of these suggestions, Searle's response is the same: no matter how much knowledge is written into the program and no matter how the program is connected to the world, he is still in the room manipulating symbols according to rules. His actions are syntactic and this can never explain to him what the symbols stand for. Searle writes "syntax is insufficient for semantics."

However, for those who accept that Searle's actions simulate a mind, separate from his own, the important question is not what the symbols mean to Searle, what is important is what they mean to the virtual mind. While Searle is trapped in the room, the virtual mind is not: it is connected to the outside world through the Chinese speakers it speaks to, through the programmers who gave it world knowledge, and through the cameras and other sensors that roboticists can supply.

Brain simulation and connectionist replies: redesigning the room

These arguments are all versions of the systems reply that identify a particular kind of system as being important; they identify some special technology that would create conscious understanding in a machine. (The "robot" and "commonsense knowledge" replies above also specify a certain kind of system as being important.)

Brain simulator reply

Suppose that the program simulated in fine detail the action of every neuron in the brain of a Chinese speaker. This strengthens the intuition that there would be no significant difference between the operation of the program and the operation of a live human brain.

Searle replies that such a simulation does not reproduce the important features of the brain—its causal and intentional states. Searle is adamant that "human mental phenomena [are] dependent on actual physical–chemical properties of actual human brains." Moreover, he argues:

[I]magine that instead of a monolingual man in a room shuffling symbols we have the man operate an elaborate set of water pipes with valves connecting them. When the man receives the Chinese symbols, he looks up in the program, written in English, which valves he has to turn on and off. Each water connection corresponds to a synapse in the Chinese brain, and the whole system is rigged up so that after doing all the right firings, that is after turning on all the right faucets, the Chinese answers pop out at the output end of the series of pipes. Now where is the understanding in this system? It takes Chinese as input, it simulates the formal structure of the synapses of the Chinese brain, and it gives Chinese as output. But the man certainly doesn't understand Chinese, and neither do the water pipes, and if we are tempted to adopt what I think is the absurd view that somehow the conjunction of man and water pipes understands, remember that in principle the man can internalize the formal structure of the water pipes and do all the "neuron firings" in his imagination.

Two variations on the brain simulator reply are the China brain and the brain-replacement scenario.

China brain

What if we ask each citizen of China to simulate one neuron, using the telephone system to simulate the connections between axons and dendrites? In this version, it seems obvious that no individual would have any understanding of what the brain might be saying. It is also obvious that this system would be functionally equivalent to a brain, so if consciousness is a function, this system would be conscious.

Brain replacement scenario

In this, we are asked to imagine that engineers have invented a tiny computer that simulates the action of an individual neuron. What would happen if we replaced one neuron at a time? Replacing one would clearly do nothing to change conscious awareness. Replacing all of them would create a digital computer that simulates a brain. If Searle is right, then conscious awareness must disappear during the procedure (either gradually or all at once). Searle's critics argue that there would be no point during the procedure when he can claim that conscious awareness ends and mindless simulation begins. Searle predicts that, while going through the brain prosthesis, "you find, to your total amazement, that you are indeed losing control of your external behavior. You find, for example, that when doctors test your vision, you hear them say 'We are holding up a red object in front of you; please tell us what you see.' You want to cry out 'I can't see anything. I'm going totally blind.' But you hear your voice saying in a way that is completely outside of your control, 'I see a red object in front of me.' [...] [Y]our conscious experience slowly shrinks to nothing, while your externally observable behavior remains the same."

Connectionist replies

Closely related to the brain simulator reply, this claims that a massively parallel connectionist architecture would be capable of understanding.

Combination reply

This response combines the robot reply with the brain simulation reply, arguing that a brain simulation connected to the world through a robot body could have a mind.

Many mansions / wait till next year reply

Better technology in the future will allow computers to understand. Searle agrees that this is possible, but considers this point irrelevant. Searle agrees that there may be designs that would cause a machine to have conscious understanding.

These arguments (and the robot or commonsense knowledge replies) identify some special technology that would help create conscious understanding in a machine. They may be interpreted in two ways: either they claim (1) this technology is required for consciousness, the Chinese room does not or cannot implement this technology, and therefore the Chinese room cannot pass the Turing test or (even if it did) it would not have conscious understanding. Or they may be claiming that (2) it is easier to see that the Chinese room has a mind if we visualize this technology as being used to create it.

In the first case, where features like a robot body or a connectionist architecture are required, Searle claims that strong AI (as he understands it) has been abandoned. The Chinese room has all the elements of a Turing complete machine, and thus is capable of simulating any digital computation whatsoever. If Searle's room can't pass the Turing test then there is no other digital technology that could pass the Turing test. If Searle's room could pass the Turing test, but still does not have a mind, then the Turing test is not sufficient to determine if the room has a "mind". Either way, it denies one or the other of the positions Searle thinks of as "strong AI", proving his argument.

The brain arguments in particular deny strong AI if they assume that there is no simpler way to describe the mind than to create a program that is just as mysterious as the brain was. He writes "I thought the whole idea of strong AI was that we don't need to know how the brain works to know how the mind works." If computation does not provide an explanation of the human mind, then strong AI has failed, according to Searle.

Other critics hold that the room as Searle described it does, in fact, have a mind, however they argue that it is difficult to see—Searle's description is correct, but misleading. By redesigning the room more realistically they hope to make this more obvious. In this case, these arguments are being used as appeals to intuition (see next section).

In fact, the room can just as easily be redesigned to weaken our intuitions. Ned Block's Blockhead argument suggests that the program could, in theory, be rewritten into a simple lookup table of rules of the form "if the user writes S, reply with P and goto X". At least in principle, any program can be rewritten (or "refactored") into this form, even a brain simulation. In the blockhead scenario, the entire mental state is hidden in the letter X, which represents a memory address—a number associated with the next rule. It is hard to visualize that an instant of one's conscious experience can be captured in a single large number, yet this is exactly what "strong AI" claims. On the other hand, such a lookup table would be ridiculously large (to the point of being physically impossible), and the states could therefore be extremely specific.

Searle argues that however the program is written or however the machine is connected to the world, the mind is being simulated by a simple step-by-step digital machine (or machines). These machines are always just like the man in the room: they understand nothing and don't speak Chinese. They are merely manipulating symbols without knowing what they mean. Searle writes: "I can have any formal program you like, but I still understand nothing."

Speed and complexity: appeals to intuition

The following arguments (and the intuitive interpretations of the arguments above) do not directly explain how a Chinese speaking mind could exist in Searle's room, or how the symbols he manipulates could become meaningful. However, by raising doubts about Searle's intuitions they support other positions, such as the system and robot replies. These arguments, if accepted, prevent Searle from claiming that his conclusion is obvious by undermining the intuitions that his certainty requires.

Several critics believe that Searle's argument relies entirely on intuitions. Ned Block writes "Searle's argument depends for its force on intuitions that certain entities do not think." Daniel Dennett describes the Chinese room argument as a misleading "intuition pump" and writes "Searle's thought experiment depends, illicitly, on your imagining too simple a case, an irrelevant case, and drawing the 'obvious' conclusion from it."

Some of the arguments above also function as appeals to intuition, especially those that are intended to make it seem more plausible that the Chinese room contains a mind, which can include the robot, commonsense knowledge, brain simulation and connectionist replies. Several of the replies above also address the specific issue of complexity. The connectionist reply emphasizes that a working artificial intelligence system would have to be as complex and as interconnected as the human brain. The commonsense knowledge reply emphasizes that any program that passed a Turing test would have to be "an extraordinarily supple, sophisticated, and multilayered system, brimming with 'world knowledge' and meta-knowledge and meta-meta-knowledge", as Daniel Dennett explains.

Speed and complexity replies

The speed at which human brains process information is (by some estimates) 100 billion operations per second. Several critics point out that the man in the room would probably take millions of years to respond to a simple question, and would require "filing cabinets" of astronomical proportions. This brings the clarity of Searle's intuition into doubt.

An especially vivid version of the speed and complexity reply is from Paul and Patricia Churchland. They propose this analogous thought experiment:

Churchland's luminous room

"Consider a dark room containing a man holding a bar magnet or charged object. If the man pumps the magnet up and down, then, according to Maxwell's theory of artificial luminance (AL), it will initiate a spreading circle of electromagnetic waves and will thus be luminous. But as all of us who have toyed with magnets or charged balls well know, their forces (or any other forces for that matter), even when set in motion produce no luminance at all. It is inconceivable that you might constitute real luminance just by moving forces around!" The problem is that he would have to wave the magnet up and down something like 450 trillion times per second in order to see anything.

Stevan Harnad is critical of speed and complexity replies when they stray beyond addressing our intuitions. He writes "Some have made a cult of speed and timing, holding that, when accelerated to the right speed, the computational may make a phase transition into the mental. It should be clear that is not a counterargument but merely an ad hoc speculation (as is the view that it is all just a matter of ratcheting up to the right degree of 'complexity.')"

Searle argues that his critics are also relying on intuitions, however his opponents' intuitions have no empirical basis. He writes that, in order to consider the "system reply" as remotely plausible, a person must be "under the grip of an ideology". The system reply only makes sense (to Searle) if one assumes that any "system" can have consciousness, just by virtue of being a system with the right behavior and functional parts. This assumption, he argues, is not tenable given our experience of consciousness.

Other minds and zombies: meaninglessness

Several replies argue that Searle's argument is irrelevant because his assumptions about the mind and consciousness are faulty. Searle believes that human beings directly experience their consciousness, intentionality and the nature of the mind every day, and that this experience of consciousness is not open to question. He writes that we must "presuppose the reality and knowability of the mental." These replies question whether Searle is justified in using his own experience of consciousness to determine that it is more than mechanical symbol processing. In particular, the other minds reply argues that we cannot use our experience of consciousness to answer questions about other minds (even the mind of a computer), and the epiphenomena reply argues that Searle's consciousness does not "exist" in the sense that Searle thinks it does.

Other minds reply

This reply points out that Searle's argument is a version of the problem of other minds, applied to machines. There is no way we can determine if other people's subjective experience is the same as our own. We can only study their behavior (i.e., by giving them our own Turing test). Critics of Searle argue that he is holding the Chinese room to a higher standard than we would hold an ordinary person.

Nils Nilsson writes "If a program behaves as if it were multiplying, most of us would say that it is, in fact, multiplying. For all I know, Searle may only be behaving as if he were thinking deeply about these matters. But, even though I disagree with him, his simulation is pretty good, so I'm willing to credit him with real thought."

Alan Turing anticipated Searle's line of argument (which he called "The Argument from Consciousness") in 1950 and makes the other minds reply. He noted that people never consider the problem of other minds when dealing with each other. He writes that "instead of arguing continually over this point it is usual to have the polite convention that everyone thinks." The Turing test simply extends this "polite convention" to machines. He doesn't intend to solve the problem of other minds (for machines or people) and he doesn't think we need to.

Eliminative Materialism reply

Several philosophers argue that consciousness, as Searle describes it, does not exist. This position is sometimes referred to as eliminative materialism: the view that consciousness is a property that can be reduced to a strictly mechanical description, and that our experience of consciousness is, as Daniel Dennett describes it, a "user illusion". Other mental properties, such as original intentionality (also called “meaning”, “content”, and “semantic character”), is also commonly regarded as something special about beliefs and other propositional attitudes. Eliminative materialism maintains that propositional attitudes such as beliefs and desires, among other intentional mental states that have content, do not exist. If eliminative materialism is the correct scientific account of human cognition then the assumption of the Chinese room argument that "minds have mental contents (semantics)" must be rejected.

Stuart Russell and Peter Norvig argue that, if we accept Searle's description of intentionality, consciousness and the mind, we are forced to accept that consciousness is epiphenomenal: that it "casts no shadow", that it is undetectable in the outside world. They argue that Searle must be mistaken about the "knowability of the mental", and in his belief that there are "causal properties" in our neurons that give rise to the mind. They point out that, by Searle's own description, these causal properties can't be detected by anyone outside the mind, otherwise the Chinese Room couldn't pass the Turing test—the people outside would be able to tell there wasn't a Chinese speaker in the room by detecting their causal properties. Since they can't detect causal properties, they can't detect the existence of the mental. In short, Searle's "causal properties" and consciousness itself is undetectable, and anything that cannot be detected either does not exist or does not matter.

Daniel Dennett provides this extension to the "epiphenomena" argument.

Dennett's reply from natural selection

Suppose that, by some mutation, a human being is born that does not have Searle's "causal properties" but nevertheless acts exactly like a human being. (This sort of animal is called a "zombie" in thought experiments in the philosophy of mind). This new animal would reproduce just as any other human and eventually there would be more of these zombies. Natural selection would favor the zombies, since their design is (we could suppose) a bit simpler. Eventually the humans would die out. So therefore, if Searle is right, it is most likely that human beings (as we see them today) are actually "zombies", who nevertheless insist they are conscious. It is impossible to know whether we are all zombies or not. Even if we are all zombies, we would still believe that we are not.

Searle disagrees with this analysis and argues that "the study of the mind starts with such facts as that humans have beliefs, while thermostats, telephones, and adding machines don't ... what we wanted to know is what distinguishes the mind from thermostats and livers." He takes it as obvious that we can detect the presence of consciousness and dismisses these replies as being off the point.

Newton's flaming laser sword reply

Mike Alder argues that the entire argument is frivolous, because it is non-verificationist: not only is the distinction between simulating a mind and having a mind ill-defined, but it is also irrelevant because no experiments were, or even can be, proposed to distinguish between the two.

English reply

Margaret Boden provided this reply in her paper "Escaping from the Chinese Room." In it she suggests, that even if the person in the room does not understand the Chinese, it does not mean there is no understanding in the room. The person in the room at least understands the rule book used to provide output responses.

In popular culture

The Chinese room argument is a central concept in Peter Watts's novels Blindsight and (to a lesser extent) Echopraxia. It is also a central theme in the video game Virtue's Last Reward, and ties into the game's narrative. In Season 4 of the American crime drama Numb3rs there is a brief reference to the Chinese room.

The Chinese Room is also the name of a British independent video game development studio best known for working on experimental first-person games, such as Everybody's Gone to the Rapture, or Dear Esther.

In the 2016 video game The Turing Test, the Chinese Room thought experiment is explained to the player by an AI.

Theory of mind in animals

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Theory_of_mind_in_animals 

 

Theory of mind in animals is the ability of nonhuman animals to attribute mental states to themselves and others

Theory of mind in animals is an extension to non-human animals of the philosophical and psychological concept of theory of mind (ToM), sometimes known as mentalisation or mind-reading. It involves an inquiry into whether animals have the ability to attribute mental states (such as intention, desires, pretending, knowledge) to themselves and others, including recognition that others have mental states that are different from their own. To investigate this issue experimentally, researchers place animals in situations where their resulting behavior can be interpreted as supporting ToM or not.

The existence of theory of mind in animals is controversial. On the one hand, one hypothesis proposes that some animals have complex cognitive processes which allow them to attribute mental states to other individuals, sometimes called "mind-reading". A second, more parsimonious, hypothesis proposes that animals lack these skills and that they depend instead on more simple learning processes such as associative learning; or in other words, they are simply behaviour-reading.

Several studies have been designed specifically to test whether animals possess theory of mind by using interspecific or intraspecific communication. Several taxa have been tested including primates, birds and canines. Positive results have been found; however, these are often qualified as showing only low-grade ToM, or rejected as not convincing by other researchers.

History and development

Much of the early work on ToM in animals focused on the understanding chimpanzees have of human knowledge

The term "theory of mind" was originally proposed by Premack and Woodruff in 1978. Early studies focused almost entirely on studying if chimpanzees could understand the knowledge of humans. This approach turned out not to be particularly fruitful and 20 years later, Heyes, reviewing all the extant data, observed that there had been "no substantial progress" in the subject area.

A 2000 paper approached the issue differently by examining competitive foraging behaviour between primates of the same species (conspecifics). This led to the rather limited conclusion that "chimpanzees know what conspecifics do and do not see". Next, brain activity in higher primates was studied and as a result, a 2003 study of the human brain suggested that the a functioning ToM system activated three major nodes, the medial prefrontal, superior temporal sulcus, and inferior frontal: the medial prefrontal node handles the mental state of the self, that the superior temporal sulcus detects the behaviour of other animals and analyzes the goals and outcomes of this behaviour, and the inferior frontal region maintains representations of actions and goals.

In 2007, Penn and Povinelli wrote "there is still little consensus on whether or not nonhuman animals understand anything about unobservable mental states or even what it would mean for a non-verbal animal to understand the concept of a 'mental state'." They went on further to suggest that ToM was "any cognitive system, whether theory-like or not, that predicts or explains the behaviour of another agent by postulating that unobservable inner states particular to the cognitive perspective of that agent causally modulate that agent's behaviour".

In 2010, an article in Scientific American acknowledged that dogs are considerably better at using social direction cues (e.g. pointing by humans) than are chimpanzees. In the same year, Towner wrote, "the issue may have evolved beyond whether or not there is theory of mind in non-human primates to a more sophisticated appreciation that the concept of mind has many facets and some of these may exist in non-human primates while others may not." Horowitz, working with dogs, agreed with this and suggested that her recent results and previous findings called for the introduction of an intermediate stage of ability, a rudimentary theory of mind, to describe animals' performance.

In 2013, Whiten reviewed the literature and concluded that regarding the question "Are chimpanzees truly mentalists, like we are?", he stated he could not offer an affirmative or negative answer. A similarly equivocal view was stated in 2014 by Brauer, who suggested that many previous experiments on ToM could be explained by the animals possessing other abilities. They went on further to make reference to several authors who suggest it is pointless to ask a "yes or no" question, rather, it makes more sense to ask which psychological states animals understand and to what extent. At the same time, it was suggested that a "minimal theory of mind" may be "what enables those with limited cognitive resources or little conceptual sophistication, such as infants, chimpanzees, scrub-jays and human adults under load, to track others' perceptions, knowledge states and beliefs."

In 2015, Cecilia Heyes, Professor of Psychology at the University of Oxford, wrote about research on ToM, "Since that time [2000], many enthusiasts have become sceptics, empirical methods have become more limited, and it is no longer clear what research on animal mindreading is trying to find" and "However, after some 35 years of research on mindreading in animals, there is still nothing resembling a consensus about whether any animal can ascribe any mental state" (Heyes' emphasis). Heyes further suggested that "In combination with the use of inanimate control stimuli, species that are unlikely to be capable of mindreading, and the 'goggles method' [see below], these approaches could restore both vigour and rigour to research on animal mindreading."

Methods

Specific categories of behaviour are sometimes used as evidence of animal ToM, including imitation, self-recognition, social relationships, deception, role-taking (empathy), perspective-taking, teaching and co-operation, however, this approach has been criticised. Some researchers focus on animals' understanding of intention, gaze, perspective, or knowledge, i.e. what another being has seen. Several experimental methods have been developed which are widely used or suggested as appropriate tests for nonhuman animals possessing ToM. Some studies look at communication between individuals of the same species (intraspecific) whereas others investigate behaviour between individuals of different species (interspecific).

Knower-Guesser

The Knower-Guesser method has been used in many studies relating to animal ToM. Animals are tested in a two-stage procedure. At the beginning of each trial in the first discrimination training stage, an animal is in a room with two humans. One human, designated the "Guesser," leaves the room, and the other, the "Knower," baits one of several containers. The containers are screened so that the animal can see who does the baiting, but not where the food has been placed. After baiting, the Guesser returns to the room, the screen is removed, and each human points directly at a container. The Knower points at the baited container, and the Guesser at one of the other containers, chosen at random. The animal is allowed to search one container and to keep the food if it is found.

Competitive feeding paradigm

The competitive feeding paradigm approach is considered by some as evidence that animals have some understanding of the relationship between "seeing" and "knowing".

At the beginning of each trial in the paradigm, a subordinate animal (the individual thought to be doing the mind-reading) and a dominant animal are kept on opposite sides of a test arena which contains two visual barriers. In all trials, a researcher enters the enclosure and places food on the subordinate's side of one of the visual barriers (one baiting event), and in some trials the researcher re-enters the enclosure several seconds later and moves the food to the subordinate's side of the other visual barrier (second baiting event). The door to the subordinate's cage is open during any baiting by the researcher. The conditions vary according to whether the dominant's door is open or closed during the baiting events, and therefore whether the subordinate individual can see the dominant. After baiting, both of the animals are released into the test arena, with the subordinate being released several seconds before the dominant. If the animals possess ToM, it is expected that subordinates are more likely to gain the food, and more likely to approach the food under several circumstances: (1) When the dominant's door is closed during trials with a single baiting event; (2) when the dominant's door is open during a first baiting event but closed during a second; (3) in single baiting event trials with the dominant's door open, subordinates are more likely to get the food when they compete at the end of the trial with a dominant individual who did not see the baiting.

Goggles Method

In one suggested protocol, chimpanzees are given first-hand experience of wearing two mirrored visors. One of the visors is transparent whereas the other is not. The visors themselves are of markedly different colours or shapes. During the subsequent test session, the chimpanzees are given the opportunity to use their species-typical begging behaviour to request food from one of the two humans, one wearing the transparent visor and the other wearing the opaque. If chimpanzees possess ToM, it would be expected they would beg more often from the human wearing the transparent visor.

False Belief Test

A method used to test ToM in human children has been adapted for testing non-human animals. The basis of the test is to track the gaze of the animal. One human hides an object in view of a second human who then leaves the room. The object is then removed. The second human returns whereupon they will mistakenly look for the object where they last saw it. If the animal stares first and longest at the location where the human last saw the object, this suggests they expect him to believe it is still hidden in that place.

In nonhuman primates

Many ToM studies have used nonhuman primates (NHPs). One study that examined the understanding of intention in orangutans (Pongo pygmaeus), chimpanzees (Pan troglodytes) and children showed that all three species understood the difference between accidental and intentional acts.

Chimpanzees

There is controversy over the interpretation of evidence purporting to show ToM in chimpanzees.

William Field and Sue Savage-Rumbaugh have no doubt that bonobos have evolved ToM and cite their communications with a captive bonobo (Pan paniscus), Kanzi, as evidence.

However, empirical studies show that chimpanzees are unable to follow a human's gaze, and are unable to use other human-eye information. Attempts to use the "Goggles Method" (see above) on highly human-enculturated chimpanzees failed to demonstrate they possess ToM.

In contrast, chimpanzees use the gaze of other chimpanzees to gain information about whether food is accessible. Subordinate chimpanzees are able to use the knowledge state of dominant chimpanzees to determine which container has hidden food.

If chimpanzees can see two opaque boards on a table and are expecting to find food, they do not choose a board lying flat because if food was under there, it would not be lying flat. Rather, they choose a slanted board, presumably inferring that food underneath is causing the slant. Chimpanzees appear able to know that other chimpanzees in the same situation make a similar inference. In a foraging game, when their competitor had chosen before them, chimpanzees avoided the slanted board on the assumption that the competitor had already chosen it. In a similar study, chimps were provided with a preference box with two compartments, one containing a picture of food, the other containing a picture of nothing (the pictures had no causal relation to the contents). In a foraging competition game, chimpanzees avoided the chamber with the picture of food when their competitor had chosen one of the chambers before them. The authors suggested this was presumably on the assumption that the competitor shared their own preference for it and had already chosen it.

One study tested another sensory mode of ToM. In a food competition, a human sat inside a booth with one piece of food to their left and one to their right. The food could be withdrawn from the competing chimpanzee's reach when necessary. In the first experiment, the chimpanzee could approach either side of the booth unseen by the human, but then had to reach through either a transparent or opaque tube to get the food. In a second experiment, both were transparent and the human was looking away, but one of the tubes made a loud rattle when it was opened. Chimpanzees reached through the opaque tube in the first experiment and the silent tube in the second. The chimpanzees successfully concealed their food-stealing from their human competitor in both cases.

Chimpanzees have passed the False Belief Test (see above) involving anticipating the gaze of humans when objects have been removed.

Other primates

Rhesus macaques selectively steal grapes from humans who are incapable of seeing the grape compared to humans who can see the grape.

In one approach testing monkeys, rhesus macaques (Macaca mulatta) are able to "steal" a contested grape from one of two human competitors. In six experiments, the macaques selectively stole the grape from a human who was incapable of seeing the grape, rather than from the human who was visually aware. The authors suggest that rhesus macaques possess an essential component of ToM: the ability to deduce what others perceive on the basis of where they are looking. Similarly, free ranging rhesus macaques preferentially choose to steal food items from locations where they can be less easily observed by humans, or where they will make less noise.

A comparative psychology approach tested six species of captive NHPs (three species of great apes: orangutans, gorillas, chimpanzees, and three species of old-world monkeys: lion-tailed macaques (Macaca silenus), rhesus macaques and collared mangabeys (Cercocebus torquatus)) in a "hide and seek" game in which the NHPs played against a human opponent. In each trial, the NHP has to infer where food has been hidden (either in their right or left hand) by the human opponent. In general, the NHPs failed the test (whereas humans did not), but surprisingly, performances between the NHP species did not reveal any inter-species differences. The authors also reported that at least one individual of each of the species showed (weak) evidence of ToM.

In a multi-species study, it was shown that chimpanzees, bonobos and orangutans passed the False Belief Test (see above).

In 2009, a summary of the ToM research, particularly emphasising an extensive comparison of humans, chimpanzees and orang-utans, concluded that great apes do not exhibit understanding of human referential intentions expressed in communicative gestures, such as pointing.

In birds

Parrots

Grey parrots (Psittacus erithacus) have demonstrated high levels of intelligence. Irene Pepperberg did experiments with these and her most accomplished parrot, Alex, demonstrated behaviour which seemed to manipulate the trainer, possibly indicating theory of mind.

Ravens

Ravens adjust their caching behaviour according to whether they have been watched and who was watching them.

Ravens are members of the family Corvidae and are widely regarded as having complex cognitive abilities.

Food-storing ravens cache (hoard) their food and pilfer (steal) from other ravens' caches. They protect their caches from being pilfered by conspecifics using aggression, dominance and re-caching. Potential pilferers rarely approach caches until the storing birds have left the cache vicinity. When storers are experimentally prevented from leaving the vicinity of the cache, pilferers first search at places other than the cache sites. When ravens (Corvus corax) witness a conspecific making caches, to pilfer those caches they (1) delay approaching the cache only when in the presence of the storer, and (2) quickly engage in searching away from the caches when together with dominant storers. These behaviours raise the possibility that ravens are capable of withholding their intentions, and also providing false information to avoid provoking the storer's aggression to protect its cache. Ravens adjust their pilfering behaviour according to when the storers are likely to defend the caches. This supports the suggestion that they are deceptively manipulating the other's behaviour. Other studies indicate that ravens recall who was watching them during caching, but also know the effects of visual barriers on what competitors can and can not see, and how this affects their pilfering.

Ravens have been tested for their understanding of "seeing" as a mental state in other ravens. It appears they take into account the visual access of other ravens, even when they cannot see the other raven. In one study, ravens were tested in two rooms separated by a wooden wall. The wall had two functional windows that could be closed with covers; each cover had a peephole drilled into it. In the next familiarization step, the ravens are trained to use a peephole to observe and pilfer human-made caches in the adjacent room. Under test conditions, there was no other raven present in the adjacent room, however, a hidden loudspeaker played a series of sounds recorded from a competitor raven. The storing raven generalized from their own experience when using the peephole to pilfer the human-made caches and predicted that the audible (raven) competitors could potentially see their caches through the peep-hole and took appropriate action, i.e. the storing ravens finished their caches more quickly and they returned to improve their caches less often. The researchers pointed out that this represented "seeing" in a way that cannot be reduced to the tracking of gaze cues – a criticism leveled at many other studies of ToM. The researchers further suggested that their findings could be considered in terms of the "minimal" (as opposed to "full-blown") ToM recently suggested.

Using the Knower-Guesser approach, ravens observing a human hiding food are capable of predicting the behaviour of bystander ravens that had been visible at both, none or just one of two baiting events. The visual field of the competitors was manipulated independently of the view of the test-raven. The findings indicate that ravens not only remember whom they have seen at caching but they also take into account that the other raven's view was blocked.

Scrub jays

Western scrub jays may show evidence of possessing theory of mind

Scrub jays are also corvids. Western scrub jays (Aphelocoma californica) both cache food and pilfer other scrub jays' caches. They use a range of tactics to minimise the possibility that their own caches will be pilfered. One of these tactics is to remember which individual scrub jay watched them during particular caching events and adjust their re-caching behaviour accordingly. One study with particularly interesting results found that only scrub jays which had themselves pilfered would re-cache when they had been observed making the initial cache. This has been interpreted as the re-caching bird projecting its own experiences of pilfering intent onto those of another potential pilferer, and taking appropriate action. Another tactic used by scrub jays is if they are observed caching, they re-cache their food when they are subsequently in private. In a computer modeling study using "virtual birds", it was suggested that re-caching is not motivated by a deliberate effort to protect specific caches from pilfering, but by a general motivation to simply cache more. This motivation is brought on by stress, which is affected by the presence and dominance of onlookers, and by unsuccessful recovery attempts.

In dogs

Dogs can use the pointing behaviour of humans to determine the location of food.

Domestic dogs (Canis familiaris) show an impressive ability to use the behaviour of humans to find food and toys using behaviours such as pointing and gazing. The performance of dogs in these studies is superior to that of NHPs, however, some have stated categorically that dogs do not possess a human-like ToM.

The Guesser-Knower approach has been used with ToM studies in dogs. In one study, each of two toys was placed on the dog's side of two barriers, one opaque and one transparent. In experimental conditions, a human sat on the opposite side of the barriers, such that they could see only the toy behind the transparent barrier. The human then told the dog to 'Fetch' without indicating either toy in any way. In a control, the human sat on the opposite side but with their back turned so that they could see neither toy. In a second control, the human sat on the same side as the dog such that they could see both toys. When the toys were differentiable, dogs approached the toy behind the transparent barrier in the "experimental" condition (i.e., the human facing the toys on the opposite side of the barrier from the dog) as compared to "back-turned" and "same-side" condition. Dogs did not differentiate between the two control conditions. The authors suggested that, even in the absence of overt behavioral cues, dogs are sensitive to others' visual access, even if that differs from their own. Similarly, dogs preferentially use the behaviour of the human Knower to indicate the location of food. This is unrelated to the sex or age of the dog. In another study, 14 of 15 dogs preferred the location indicated by the Knower on the first trial, whereas chimpanzees require approximately 100 trials to reliably exhibit the preference.

Human infants (10 months old) continue to search for hidden objects at their initial hiding place, even after observing them being hidden at another location. This perseverance of searching errors is at least partly contributed to by behavioural cues from the experimenter. Domestic dogs also commit more search errors in communicative trials than in non-communicative or non-social hiding trials. However, human-encultured wolves (Canis lupus) do not show this context-dependent perseverance in searching. This common sensitivity to human communication behaviour may arise from convergent evolution.

Dogs which have been forbidden to take food are more likely to steal the food if a human observer has their back turned or eyes closed than when the human is looking at them. Dogs are also more likely to beg for food from an observer whose eyes are visible compared to an observer whose eyes are covered by a blindfold.

In a study of the way that dogs interact, play signals were sent almost exclusively to forward-facing partners. In contrast, attention-getting behaviors were used most often when the other dog was facing away, and before signaling an interest to play. Furthermore, the type of attention-getting behaviour matched the inattentiveness of the playmate. Stronger attention-getting behaviours were used when a playmate was looking away or distracted, less forceful ones when the partner was facing forward or laterally,

In pigs

An experiment at the University of Bristol found that one out of ten pigs was possibly able to understand what other pigs can see. That pig observed another pig which had view of a maze in which food was being hidden, and trailed that pig through the maze to the food. The other pigs involved in the experiment did not.

In goats

A 2006 study found that goats exhibited intricate social behaviours indicative of high-level cognitive processes, particularly in competitive situations. The study included an experiment in which a subordinate animal was allowed to choose between food that a dominant animal could also see and food that it could not; those who were subject to aggressive behaviour selected the food that the dominant animal could not see, suggesting that they are able to perceive a threat based on being within the dominant animal's view – in other words, visual perspective taking.