One account of the history of computation might begin in the 1930’s with some of the work of Alonzo Church, Alan Turing, and Emil Post. One might say that this is where something like the core concept of computation was first formally articulated. Here were the first attempts to formalize an informal notion of an algorithm or effective procedure by which a mathematician might decide one or another logico-mathematical question. As each of these formalisms was shown to compute the same (...) set of functions—the partial recursive functions—each of them might be described as a form of Turing-equivalent computation. This work set the cornerstone for what we might call computation theory. This history might then proceed to give pride of place to this form of computation in subsequent developments in cognitive science and in related disciplines and subdisciplines. Such a history might note that, in the 1940’s, the results of this work would have been transferred into the emerging field of computer science with the design and construction of the first electronic digital computers. Here one would mention Turing again, as well as perhaps Norbert Wiener, Julian Bigelow, John von Neumann, and many others. At about the same time, this theory of computation would have been inserted into the theory of neural networks by way of Warren McCulloch and Walter Pitts’s seminal work, “A Logical Calculus of the Ideas Immanent in Nervous Activity.” Somewhat later, during the 1960’s, Hilary Putnam introduced Turing machine tables into the philosophy of mind as a tool for illuminating various features of the mind-body problem, eventually transforming the intellectual landscape in the metaphysics of mind. Also during the 1960’s, Turingequivalent computation would have infiltrated psychology through the influence of Chomskyan linguistics and under the rubric of information processing psychology. Further, such computation would have been integrated into the fields of cognitive science and neuroscience as they emerged during the 1970’s and 1980’s.. (shrink)

One account of the history of computation might begin in the 1930's with some of the work of Alonzo Church, Alan Turing, and Emil Post. One might say that this is where something like the core concept of computation was first formally articulated. Here were the first attempts to formalize an informal notion of an algorithm or effective procedure by which a mathematician might decide one or another logico-mathematical question. As each of these formalisms was shown to compute the same (...) set of functions—the partial recursive functions—each of them might be described as a form of Turing-equivalent computation. This work set the cornerstone for what we might call computation theory. This history might then proceed to give pride of place to this form of computation in subsequent developments in cognitive science and in related disciplines and subdisciplines. Such a history might note that, in the 1940's, the results of this work would have been transferred into the emerging field of computer science with the design and construction of the first electronic digital computers. Here one would mention Turing again, as well as perhaps Norbert Wiener, Julian Bigelow, John von Neumann, and many others. At about the same time, this theory of computation would have been inserted into the theory of neural networks by way of Warren McCulloch and Walter Pitts's seminal work, “A Logical Calculus of the Ideas Immanent in Nervous Activity.” Somewhat later, during the 1960's, Hilary Putnam introduced Turing machine tables into the philosophy of mind as a tool for illuminating various features of the mind-body problem, eventually transforming the intellectual landscape in.. (shrink)

This article discusses the properties of a controllable, flexible, hybrid parallel computing architecture that potentially merges pattern recognition and arithmetic. Humans perform integer arithmetic in a fundamentally different way than logic-based computers. Even though the human approach to arithmetic is both slow and inaccurate it can have substantial advantages when useful approximations ( intuition ) are more valuable than high precision. Such a computational strategy may be particularly useful when computers based on nanocomponents become feasible because it offers a way (...) to make use of the potential power of these massively parallel systems. Because the system architecture is inspired by neuroscience and is applied to cognitive problems, occasional mention is made of experimental data from both fields when appropriate. (shrink)

The nature of cognition is being re-considered. Instead of emphasizing formal operations on abstract symbols, the new approach foregrounds the fact that cognition is, rather, a situated activity, and suggests that thinking beings ought therefore be considered first and foremost as acting beings. The essay reviews recent work in Embodied Cognition, provides a concise guide to its principles, attitudes and goals, and identifies the physical grounding project as its central research focus.

The article presents a critique of John Searle's attack on computationalist theories of mind in his recent book, The Rediscovery of the Mind. Searle is guilty of caricaturing his opponents, and of ignoring their arguments. Moreover, his own positive theory of mind, which he claims "takes account of" subjectivity, turns out to offer no discernible advantages over the views he rejects.

The relation between computational and intentional psychology has always been a vexing issue. The worry is that if mental processes are computational, then these processes, which are defined over symbols, are sensitive solely to the non-semantic properties of symbols. If so, perhaps psychology could dispense with adverting in its laws to intentional/semantic properties of symbols. Stich, as is well-known, has made a great deal out of this tension and argued for a purely "syntactic" psychology by driving a wedge between a (...) semantic individuation of symbol tokens and their narrow functional individuation. If the latter can be carried out, he claimed, we do not need semantic typing. I argue that since a narrow functional individuation cannot type-identify symbol tokens across organisms, a semantic account of typing must be the only option given that interpersonal physical individuation of tokens is not to be taken seriously. (shrink)

Proponents of non-conceptual content have recruited it for various philosophical jobs. Some epistemologists have suggested that it may play the role of “the given” that Sellars is supposed to have exorcised from philosophy. Some philosophers of mind (e.g., Dretske) have suggested that it plays an important role in the project of naturalizing semantics as a kind of halfway between merely information bearing and possessing conceptual content. Here I will focus on a recent proposal by Jerry Fodor. In a recent paper (...) he characterizes non-conceptual content in a particular way and argues that it is plausible that it plays an explanatory role in accounting for certain auditory and visual phenomena. So he thinks that there is reason to believe that there is non-conceptual content. On the other hand, Fodor thinks that non-conceptual content has a limited role. It occurs only in the very early stages of perceptual processing prior to conscious awareness. My paper is examines Fodor’s characterization of non-conceptual content and his claims for its explanatory importance. I also discuss if Fodor has made a case for limiting non-conceptual content to non-conscious, sub-personal mental states. (shrink)

In this section, we will start with an influential attempt to define `intelligence', and then we will move to a consideration of how human intelligence is to be investigated on the machine model. The last part of the section will discuss the relation between the mental and the biological.

What is the mind? How does it work? How does it influence behavior? Some psychologists hope to answer such questions in terms of concepts drawn from computer science and artificial intelligence. They test their theories by modeling mental processes in computers. This book shows how computer models are used to study many psychological phenomena--including vision, language, reasoning, and learning. It also shows that computer modeling involves differing theoretical approaches. Computational psychologists disagree about some basic questions. For instance, should the mind (...) be modeled by digital computers, or by parallel-processing systems more like brains? Do computer programs consist of meaningless patterns, or do they embody (and explain) genuine meaning? (shrink)

In an effort to uncover fundamental differences between computers and brains, this paper identifies computation with a particular kind of physical process, in contrast to interpreting the behaviors of physical systems as one or more abstract computations. That is, whether or not a system is computing depends on how those aspects of the system we consider to be informational physically cause change rather than on our capacity to describe its behaviors in computational terms. A physical framework based on the notion (...) of causal mechanism is used to distinguish different kinds of information processing in a physically-principled way; each information processing type is associated with a particular causal mechanism. The causal mechanism associated with computation is pattern matching, which isphysically defined as the fitting of physical structures such that they cause a simple change. It is argued that information processing in the brain is based on a causal mechanism different than pattern matching so defined, implying that brains do not compute, at least not in the physical sense that digital computers do. This causal difference may also mean that computers cannot have mental states. (shrink)

... Studien-26 THEMES IN MY PHILOSOPHICAL WORK Terry HORGAN University of Memphis This paper is an overview of my philosophical work. ...

Andrew Boucher (1997) argues that ``parallel computation is fundamentally different from sequential computation'' (p. 543), and that this fact provides reason to be skeptical about whether AI can produce a genuinely intelligent machine. But parallelism, as I prove herein, is irrelevant. What Boucher has inadvertently glimpsed is one small part of a mathematical tapestry portraying the simple but undeniable fact that physical computation can be fundamentally different from ordinary, ``textbook'' computation (whether parallel or sequential). This tapestry does indeed immediately imply (...) that human cognition may be uncomputable. (shrink)

The dominant scientific and philosophical view of the mind – according to which, put starkly, cognition is computation – is refuted herein, via specification and defense of the following new argument: Computation is reversible; cognition isn't; ergo, cognition isn't computation. After presenting a sustained dialectic arising from this defense, we conclude with a brief preview of the view we would put in place of the cognition-is-computation doctrine.

What''s computation? The received answer is that computation is a computer at work, and a computer at work is that which can be modelled as a Turing machine at work. Unfortunately, as John Searle has recently argued, and as others have agreed, the received answer appears to imply that AI and Cog Sci are a royal waste of time. The argument here is alarmingly simple: AI and Cog Sci (of the Strong sort, anyway) are committed to the view that cognition (...) is computation (or brains are computers); butall processes are computations (orall physical things are computers); so AI and Cog Sci are positively silly.I refute this argument herein, in part by defining the locutions x is a computer and c is a computation in a way that blocks Searle''s argument but exploits the hard-to-deny link between What''s Computation? and the theory of computation. However, I also provide, at the end of this essay, an argument which, it seems to me, implies not that AI and Cog Sci are silly, but that they''re based on a form of computation that is well beneath human persons. (shrink)

This paper is a revised and extended version of a keynote contribution to a recent conference on Cognitive Informatics. It offers a brief summary of some of the core concerns of other contributions to the conference, highlighting the range of issues under discussion; and argues that many of the central concepts and preoccupations of cognitive informatics as understood by participants--and others in the general field of computation--rely on ill-founded realist assumptions, and what has been termed the functionalist view of representation. (...) Even if such ideas--albeit in a revised form -- can be defended, there must be a more extensive engagement with the literature and issues outside the confines of the computing and computational orthodoxy. (shrink)

The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that (...) none of these kinds of evidence can be relevant to a choice among competing computational models unless there are in fact type-type correlations between the states postulated by computational models and brain states. Thus, I conclude, research into the computational procedures employed in human cognition must be conducted hand-in-hand with research into the brain processes which realize those procedures. (shrink)

The role of content in computational accounts of cognition is a matter of some controversy. An early prominent view held that the explanatory relevance of content consists in its supervenience on the the formal properties of computational states (see, e.g., Fodor 1980). For reasons that derive from the familiar Twin Earth thought experiments, it is usually thought that if content is to supervene on formal properties, it must be narrow; that is, it must not be the sort of content that (...) determines reference and truth-conditions. An interesting alternative to this view has recently been proposed by Egan (1995). According to Egan, the explanatory role of content is such that contents must in general be broad to be explanatorily relevant. But Egan’s view involves a non-realist interpretation of content assignments. I will argue here that this non-realism about contents is undermotivated. A realist variation on her view of the explanatory role of content, however, would survive this criticism. This realist variation, I suggest, shares with the views of other commentators on Marr’s theory (e.g., Burge 1986; Shapiro 1993; forthcoming) certain commitments concerning the supervenience base of visual contents and processes. I will argue, however, that these commitments beg important questions regarding the individuation of cognitive states and processes. I conclude, contrary to Burge and Shapiro, that Marr’s theory does not favor anti-individualism. (shrink)

The use of the computer metaphor has led to the proposal of mind architecture (Pylyshyn 1984; Newell 1990) as a model of the organization of the mind. The dualist computational model, however, has, since the earliest days of psychological functionalism, required that the concepts mind architecture and brain architecture be remote from each other. The development of both connectionism and neurocomputational science, has sought to dispense with this dualism and provide general models of consciousness – a uniform cognitive architecture –, (...) which is in general reductionist, but which retains the computer metaphor. This paper examines, in the first place, the concepts of mind architecture and brain architecture, in order to evaluate the syntheses which have recently been offered. It then moves on to show how modifications which have been made to classical functionalist mind architectures, with the aim of making them compatible with brain architectures, are unable to resolve some of the most serious problems of functionalism. Some suggestions are given as to why it is not possible to relate mind structures and brain structures by using neurocomputational approaches, and finally the question is raised of the validity of reductionism in a theory which sets out to unite mind and brain architectures. (shrink)

Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these questions. Justifying the role of computation (...) requires analysis of implementation, the nexus between abstract computations and concrete physical systems. I give such an analysis, based on the idea that a system implements a computation if the causal structure of the system mirrors the formal structure of the computation. This account can be used to justify the central commitments of artificial intelligence and computational cognitive science: the thesis of computational sufficiency, which holds that the right kind of computational structure suffices for the possession of a mind, and the thesis of computational explanation, which holds that computation provides a general framework for the explanation of cognitive processes. The theses are consequences of the facts that (a) computation can specify general patterns of causal organization, and (b) mentality is an organizational invariant, rooted in such patterns. Along the way I answer various challenges to the computationalist position, such as those put forward by Searle. I close by advocating a kind of minimal computationalism, compatible with a very wide variety of empirical approaches to the mind. This allows computation to serve as a true foundation for cognitive science. (shrink)

(1) Van Gelder's concession that the dynamical hypothesis is not in opposition to computation in general does not agree well with his anticomputational stance. (2) There are problems with the claim that dynamic systems allow for nonrepresentational aspects of computation in a way in which digital computation cannot. (3) There are two senses of the “cognition is computation” claim and van Gelder argues against only one of them. (4) Dynamical systems as characterized in the target article share problems of universal (...) realizability with formal notions of computation, but differ in that there is no solution available for them. (5) The dynamical hypothesis cannot tell us what cognition is, because instantiating a particular dynamical system is neither necessary nor sufficient for being a cognitive agent. (shrink)

Two very different insights motivate characterizing the brain as a computer. One depends on mathematical theory that defines computability in a highly abstract sense. Here the foundational idea is that of a Turing machine. Not an actual machine, the Turing machine is really a conceptual way of making the point that any well-defined function could be executed, step by step, according to simple 'if-you-are-in-state-P-and-have-input-Q-then-do-R' rules, given enough time (maybe infinite time) [see COMPUTATION]. Insofar as the brain is a device whose (...) input and output can be characterized in terms of some mathematical function -- however complicated -- then in that very abstract sense, it can be mimicked by a Turning machine. Given what is known so far brains do seem to depend on cause-effect operations, and hence brains appear to be, in some formal sense, equivalent to a Turing machine [see CHURCH-TURING THESIS]. On its own, however, this reveals nothing at all of how the mind-brain actually works. The second insight depends on looking at the brain as a biological device that processes information from the environment to build complex representations that enable the brain to make predictions and select advantageous behaviors. Where necessary to avoid ambiguity, we will refer to the first notion of computation as algorithmic computation, and the second as information processing computation. (shrink)

Marr's computational theory of stereopsis is shown to imply that human vision employs a system of representation which has all the properties of a number system. Claims for an internal number system and for neural computation should be taken literally. I show how these ideas withstand various skeptical attacks, and analyze the requirements for describing neural operations as computations. Neural encoding of numerals is shown to be distinct from our ability to measure visual physiology. The constructs in Marr's theory are (...) neither propositional nor pictorial, and provide a counter example to many commonly held dichotomies concerning mental representation. (shrink)

Philosophy and AI presents invited contributions that focus on the different perspectives and techniques that philosophy and AI bring to the theory of ...

The purpose of this paper is to set forth a sense in which programs can and do explain behavior, and to distinguish from this a number of senses in which they do not. Once we are tolerably clear concerning the sort of explanatory strategy being employed, two rather interesting facts emerge; (1) though it is true that programs are "internally represented," this fact has no explanatory interest beyond the mere fact that the program is executed; (2) programs which are couched (...) in information processing terms may have an explanatory interest for a given range of behavior which is independent of physiological explanations of the same range of behavior. (shrink)

The following paper presents a characterization of three distinctions fundamental to computationalism, viz., the distinction between analog and digital machines, representation and nonrepresentation-using systems, and direct and indirect perceptual processes. Each distinction is shown to rest on nothing more than the methodological principles which justify the explanatory framework of the special sciences.

Under the Superstition Mountains in central Arizona toil those who would rob humankind of its humanity. These gray, soulless monsters methodically tear away at our meaning, our subjectivity, our essence as transcendent beings. With each advance, they steal our freedom and dignity. Who are these denizens of darkness, these usurpers of all that is good and holy? None other than humanity’s arch-foe: The Cognitive Scientists -- AI researchers, fallen philosophers, psychologists, and other benighted lovers of computers. Unless they are stopped, (...) humanity -- you and I -- will soon be nothing but numbers and algorithms locked away on magnetic tape. (shrink)

Years ago, when I was an undergraduate math major at the University of Wyoming, I came across an interesting book in our library. It was a book of counterexamples t o propositions in real analysis (the mathematics of the real numbers). Mathematicians work more or less like the rest of us. They consider propositions. If one seems to them to be plausibly true, then they set about to prove it, to establish the proposition as a theorem. Instead o f setting (...) out to prove propositions, the psychologists, neuroscientists, and other empirical types among us, set out to show that a proposition is supported by the data, and that it is the best such proposition so supported. The philosophers among us, when they are not causing trouble by arguing that AI is a dead end or that cognitive science can get along without representations, work pretty much like the mathematicians: we set out to prove certain propositions true on the basis of logic, first principles, plausible assumptions, and others' data. But, back to the book of real analysis counterexamples. If some mathematician happened t o think that some proposition about continuity, say, was plausibly true, he or she would then set out to prove it. If the proposition was in fact not a theorem, then a lot of precious time would be wasted trying to prove it. Wouldn't it be great to have a book that listed plausibly true propositions that were in fact not true, and listed with each such proposition a counterexample to it? Of course it would. (shrink)

There is a prevalent notion among cognitive scientists and philosophers of mind that computers are merely formal symbol manipulators, performing the actions they do solely on the basis of the syntactic properties of the symbols they manipulate. This view of computers has allowed some philosophers to divorce semantics from computational explanations. Semantic content, then, becomes something one adds to computational explanations to get psychological explanations. Other philosophers, such as Stephen Stich, have taken a stronger view, advocating doing away with semantics (...) entirely. This paper argues that a correct account of computation requires us to attribute content to computational processes in order to explain which functions are being computed. This entails that computational psychology must countenance mental representations. Since anti-semantic positions are incompatible with computational psychology thus construed, they ought to be rejected. Lastly, I argue that in an important sense, computers are not formal symbol manipulators. (shrink)

Written by world-leading experts, this book draws together a number of important strands in contemporary approaches to the philosophical and scientific questions that emerge when dealing with the issues of computing, information, cognition and the conceptual issues that arise at their intersections. It discovers and develops the connections at the borders and in the interstices of disciplines and debates. This volume presents a range of essays that deal with the currently vigorous concerns of the philosophy of information, ontology creation and (...) control, bioinformation and biosemiotics, computational and post-computation approaches to the philosophy of cognitive science, computational linguistics, ethics, and education. http://www.amazon.ca/Computation-Information-Cognition-Gordana-Dodig-Crnkovic/dp/1847180906. (shrink)

I critically examine some provocative arguments that John Searle presents in his book The Rediscovery of Mind to support the claim that the syntactic states of a classical computational system are "observer relative" or "mind dependent" or otherwise less than fully and objectively real. I begin by explaining how this claim differs from Searle's earlier and more well-known claim that the physical states of a machine, including the syntactic states, are insufficient to determine its semantics. In contrast, his more recent (...) claim concerns the syntax, in particular, whether a machine actually has symbols to underlie its semantics. I then present and respond to a number of arguments that Searle offers to support this claim, including whether machine symbols are observer relative because the assignment of syntax is arbitrary, or linked to universal realizability, or linked to the sub-personal interpretive acts of a homunculus, or linked to a person's consciousness. I conclude that a realist about the computational model need not be troubled by such arguments. Their key premises need further support. (shrink)

The aims of this paper are threefold: To show that game-playing (GP), the discipline of Artificial Intelligence (AI) concerned with the development of automated game players, has a strong epistemological relevance within both AI and the vast area of cognitive sciences. In this context games can be seen as a way of securely reducing (segmenting) real-world complexity, thus creating the laboratory environment necessary for testing the diverse types and facets of intelligence produced by computer models. This paper aims to promote (...) the belief that games represent an excellent tool for the project of computational psychology (CP). To underline how, despite this, GP has mainly adopted an engineering-inspired methodology and in doing so has distorted the framework of cognitive functionalism. Many successes (i.e. chess, checkers) have been achieved refusing human-like reasoning. The AI has appeared to work well despite ignoring an intrinsic motivation, that of creating an explanatory link between machines and mind. To assert that substantial improvements in GP may be obtained in the future only by renewed interest in human-inspired models of reasoning and in other cognitive studies. In fact, if we increase the complexity of games (from NP-Completeness to AI-Completeness) in order to reproduce real-life problems, computer science techniques enter an impasse. Many of AI’s recent GP experiences can be shown to validate this. The lack of consistent philosophical foundations for cognitive AI and the minimal philosophical commitment of AI investigation are two of the major reasons that play an important role in explaining why CP has been overlooked. (shrink)

This collection of essays examines the philosophical and cultural aspects of technology. The issues range widely - from quantum technology to problems of technology and culture in a developing country and contributors approach the issues from a variety of perspectives. The volume includes case-studies, and also more theoretical pieces which consider the fundamental question of whether technology should be perceived as a force for liberation or enslavement. The volume aims to stimulate debate about the relation between technology and philosophy and (...) society in general, and to open a field of enquiry that has been relatively neglected. Written in an accessible style, the contributions are intended equally for philosophers exploring the novel problems arising in the twentieth and twenty-first centuries, but also for technologists interested in the philosophical implications of their work. (shrink)

My purpose in this essay is to clarify the notion of explanation by computer simulation in artificial intelligence and cognitive science. My contention is that computer simulation may be understood as providing two different kinds of explanation, which makes the notion of explanation by computer simulation ambiguous. In order to show this, I shall draw a distinction between two possible ways of understanding the notion of simulation, depending on how one views the relation in which a computing system that performs (...) a cognitive task stands to the program that the system runs while performing that task. Next, I shall suggest that the kind of explanation that results from simulation is radically different in each case. In order to illustrate the difference, I will point out some prima facie methodological difficulties that need to be addressed in order to ensure that simulation plays a legitimate explanatory role in cognitive science, and I shall emphasize how those difficulties are very different depending on the notion of explanation involved. (shrink)

Cognitive science has been dominated by the computational conception that cognition is computation across representations. To the extent to which cognition as computation across representations is supposed to be a purposive, meaningful, algorithmic, problem-solving activity, however, computers appear to be incapable of cognition. They are devices that can facilitate computations on the basis of semantic grounding relations as special kinds of signs. Even their algorithmic, problem-solving character arises from their interpretation by human users. Strictly speaking, computers as such — apart (...) from human users — are not only incapable of cognition, but even incapable of computation, properly construed. If we want to understand the nature of thought, then we have to study thinking, not computing, because they are not the same thing. (shrink)

I review a widely accepted argument to the conclusion that the contents of our beliefs, desires and other mental states cannot be causally efficacious in a classical computational model of the mind. I reply that this argument rests essentially on an assumption about the nature of neural structure that we have no good scientific reason to accept. I conclude that computationalism is compatible with wide semantic causal efficacy, and suggest how the computational model might be modified to accommodate this possibility.

Jerry Fodor argues against the widely held view that mental processes are largely computations, that the architecture of cognition is massively modular, and...

The computational theory of cognition (CTC) holds that the mind is akin to computer software. This article aims to show that CTC is incorrect because it is not able to distinguish the ability to solve a maze from the ability to solve its mirror image. CTC cannot do so because it only individuates brain states up to isomorphism. It is shown that a finer individuation that would distinguish left-handed from right-handed abilities is not compatible with CTC. The view is explored (...) that CTC correctly individuates in an autonomous domain of the mental, leaving discrimination between left and right to some non-cognitive component of psychology such as physiology. I object by showing that the individuation provided by CTC does not properly describe in any domain. An embodied computational taxonomy, rather than software alone, is required for an adequate science of the mind. (shrink)

Cognitive science uses the notion of computational information processing to explain cognitive information processing. Some philosophers have argued that anything can be described as doing computational information processing; if so, it is a vacuous notion for explanatory purposes.An attempt is made to explicate the notions of cognitive information processing and computational information processing and to specify the relationship between them. It is demonstrated that the resulting notion of computational information processing can only be realized in a restrictive class of dynamical (...) systems called physical notational systems (after Goodman's theory of notationality), and that the systems generally appealed to by cognitive science-physical symbol systems-are indeed such systems. Furthermore, it turns out that other alternative conceptions of computational information processing, Fodor's (1975) Language of Thought and Cummins' (1989) Interpretational Semantics appeal to substantially the same restrictive class of systems. (shrink)

Two very different insights motivate characterizing the brain as a computer. One depends on mathematical theory that defines computability in a highly abstract sense. Here the foundational idea is that of a Turing machine. Not an actual machine, the Turing machine is really a conceptual way of making the point that any well-defined function could be executed, step by step, according to simple 'if-you-are-in-state-P-and-have-input-Q-then-do-R' rules, given enough time (maybe infinite time) [see COMPUTATION]. Insofar as the brain is a device whose (...) input and output can be characterized in terms of some mathematical function -- however complicated -- then in that very abstract sense, it can be mimicked by a Turning machine. Given what is known so far brains do seem to depend on cause-effect operations, and hence brains appear to be, in some formal sense, equivalent to a Turing machine [see CHURCH-TURING THESIS]. On its own, however, this reveals nothing at all of how the mind-brain actually works. The second insight depends on looking at the brain as a biological device that processes information from the environment to build complex representations that enable the brain to make predictions and select advantageous behaviors. Where necessary to avoid ambiguity, we will refer to the first notion of computation as. (shrink)

What counts as a computation and how it relates to cognitive function are important questions for scientists interested in understanding how the mind thinks. This paper argues that pragmatic aspects of explanation ultimately determine how we answer those questions by examining what is needed to make rigorous the notion of computation used in the (cognitive) sciences. It (1) outlines the connection between the Church-Turing Thesis and computational theories of physical systems, (2) differentiates merely satisfying a computational function from true computation, (...) and finally (3) relates how we determine a true computation to the functional methodology in cognitive science. All of the discussion will be directed toward showing that the only way to connect formal notions of computation to empirical theory will be in virtue of the pragmatic aspects of explanation. (shrink)

Computation is interpretable symbol manipulation. Symbols are objects that are manipulated on the basis of rules operating only on theirshapes, which are arbitrary in relation to what they can be interpreted as meaning. Even if one accepts the Church/Turing Thesis that computation is unique, universal and very near omnipotent, not everything is a computer, because not everything can be given a systematic interpretation; and certainly everything can''t be givenevery systematic interpretation. But even after computers and computation have been successfully distinguished (...) from other kinds of things, mental states will not just be the implementations of the right symbol systems, because of the symbol grounding problem: The interpretation of a symbol system is not intrinsic to the system; it is projected onto it by the interpreter. This is not true of our thoughts. We must accordingly be more than just computers. My guess is that the meanings of our symbols are grounded in the substrate of our robotic capacity to interact with that real world of objects, events and states of affairs that our symbols are systematically interpretable as being about. (shrink)

What is the relation between computation and intennonality? Cognition presup- poses intentionality (or semantics). This much is certain. So, if, according to com- putationalism, cognition is computation, then computation, mo, presupposes..

I invoked the notion of supervenience in my doctoral disseration, Microreduction and the Mind-Body Problem, completed at the University of Michigan in 1974 under the direction of Jaegwon Kim. I had been struck by the appeal to supervenience in Hare (1952), a classic work in twentieth century metaethics that I studied at Michigan in a course on metaethics taught by William Frankena; and I also had been struck by the brief appeal to supervenience in Davidson (1970). Kim was already, in (...) effect, construing the relation between physical and mental properties as a supervenience relation?although he was not yet using the word ?supervenience?. I assumed that a materialistic metaphysics was correct, and that integral to materialism is the idea that higher-level sciences (including psychology) are reducible to lower-level ones?ultimately to microphysics. One idea I pressed in the dissertation was that biconditional ?bridge laws? would not suffice for genuine intertheoretic reduction if these inter-level laws were additional fundamental laws of nature alongside those of the reducing science; they would be what Herbert Feigl and J.J. C. Smart, in their writings on the psychophysical identity theory, called ?nomological danglers.? I argued that the higher-level property in a bridge law should bear a relation of strict supervenience to its correlated lower-level property, rather than merely being nomically correlated with it. The basic idea was that there are no two physically possible worlds w1 and w2?where a physically possible world is, roughly, a world in which the laws of microphysics obtain and in which there are no nonphysical substances like entelechies or Cartesian souls?such that the actual-world bridge laws obtain in world w1 but not in world w2. (Thus, the bridge laws themselves are fixed relative to the fundamental physical facts and fundamental laws, rather than being fundamental laws themselves alongside those of microphysics.) Already when. (shrink)

Computational properties, it is standardly assumed, are to be sharply distinguished from semantic properties. Specifically, while it is standardly assumed that the semantic properties of a cognitive system are externally or non-individualistically individuated, computational properties are supposed to be individualistic and internal. Yet some philosophers (e.g., Tyler Burge) argue that content impacts computation, and further, that environmental factors impact computation. Oron Shagrir has recently argued for these theses in a novel way, and gave them novel interpretations. In this paper I (...) present a conception of computation in cognitive science that takes Shagrir's conception as its starting point, but further develops it in various directions and strengthens it. I argue that the explanatory role of computational properties emerges from the idea that syntactical properties and the relevant external factors presented by cognitive systems compose wide computational properties. I also elaborate upon the notion of content that is in play, and argue that it is contents of the kind that are ascribed by transparent interpretations of content ascriptions that impact computation. This fact enables the thesis that external factors impact computation to rebuff the challenge which concerns the claim that psychology must be individualistic. (shrink)

Over the past thirty years, it is been common to hear the mind likened to a digital computer. This essay is concerned with a particular philosophical view that holds that the mind literally is a digital computer (in a specific sense of “computer” to be developed), and that thought literally is a kind of computation. This view—which will be called the “Computational Theory of Mind” (CTM)—is thus to be distinguished from other and broader attempts to connect the mind with computation, (...) including (a) various enterprises at modeling features of the mind using computational modeling techniques, and (b) employing some feature or features of production-model computers (such as the stored program concept, or the distinction between hardware and software) merely as a guiding metaphor for understanding some feature of the mind. This entry is therefore concerned solely with the Computational Theory of Mind (CTM) proposed by Hilary Putnam [1961] and developed most notably for philosophers by Jerry Fodor [1975, 1980, 1987, 1993]. The senses of ‘computer’ and ‘computation’ employed here are technical; the main tasks of this entry will therefore be to elucidate: (a) the technical sense of ‘computation’ that is at issue, (b) the ways in which it is claimed to be applicable to the mind, (c) the philosophical problems this understanding of the mind is claimed to solve, and (d) the major criticisms that have accrued to this view. (shrink)

Over the past several decades, the philosophical community has witnessed the emergence of an important new paradigm for understanding the mind.1 The paradigm is that of machine computation, and its influence has been felt not only in philosophy, but also in all of the empirical disciplines devoted to the study of cognition. Of the several strategies for applying the resources provided by computer and cognitive science to the philosophy of mind, the one that has gained the most attention from philosophers (...) has been the Computational Theory of Mind (CTM). CTM was first articulated by Hilary Putnam (1960, 1961), but finds perhaps its most consistent and enduring advocate in Jerry Fodor (1975, 1980, 1981, 1987, 1990, 1994). It is this theory, and not any broader interpretations of what it would be for the mind to be a computer, that I wish to address in this paper. What I shall argue here is that the notion of symbolic representation employed by CTM is fundamentally unsuited to providing an explanation of the intentionality of mental states (a major goal of CTM), and that this result undercuts a second major goal of CTM, sometimes refered to as the vindication of intentional psychology. This line of argument is related to the discussions of derived intentionality by Searle (1980, 1983, 1984) and Sayre (1986, 1987). But whereas those discussions seem to be concerned with the causal dependence of familiar sorts of symbolic representation upon meaning-bestowing acts, my claim is rather that there is not one but several notions of meaning to be had, and that the notions that are applicable to symbols are conceptually dependent upon the notion that is applicable to mental states in the fashion that Aristotle refered to as paronymy. That is, an analysis of the notions of meaning applicable to symbols reveals that they contain presuppositions about meaningful mental states, much as Aristotle's analysis of the sense of healthy that is applied to foods reveals that it means conducive to having a healthy body, and hence any attempt to explain mental semantics in terms of the semantics of symbols is doomed to circularity and regress. I shall argue, however, that this does not have the consequence that computationalism is bankrupt as a paradigm for cognitive science, as it is possible to reconstruct CTM in a fashion that avoids these difficulties and makes it a viable research framework for psychology, albeit at the cost of losing its claims to explain intentionality and to vindicate intentional psychology. I have argued elsewhere (Horst, 1996) that local special sciences such as psychology do not require vindication in the form of demonstrating their reducibility to more fundamental theories, and hence failure to make good on these philosophical promises need not compromise the broad range of work in empirical cognitive science motivated by the computer paradigm in ways that do not depend on these problematic treatments of symbols. (shrink)

In this carefully argued critique, Steven Horst pronounces the theory deficient.

This paper surveys applications of logical methods in the cognitive sciences. Special attention is paid to non-monotonic logics and complexity theory. We argue that these particular tools have been useful in clarifying the debate between symbolic and connectionist models of cognition.

According to the computational theory of mind (CTM), to think is to compute. But what is meant by the word 'compute'? The generally given answer is this: Every case of computing is a case of manipulating symbols, but not vice versa - a manipulation of symbols must be driven exclusively by the formal properties of those symbols if it is qualify as a computation. In this paper, I will present the following argument. Words like 'form' and 'formal' are ambiguous, as (...) they can refer to form in either the syntactic or the morphological sense. CTM fails on each disambiguation, and the arguments for CTM immediately cease to be compelling once we register that ambiguity. The terms 'mechanical' and 'automatic' are comparably ambiguous. Once these ambiguities are exposed, it turns out that there is no possibility of mechanizing thought, even if we confine ourselves to domains (such as first-order sentential logic) where all problems can be settled through decision-procedures. The impossibility of mechanizing thought thus has nothing to do with recherché mathematical theorems, such as those proven by Gödel and Rosser. A related point is that CTM involves, and is guilty of reinforcing, a misunderstanding of the concept of an algorithm. (shrink)

For reasons internal to the concepts of thought and causality, a series of representations must be semantics-driven if that series is to add up to a single, unified thought. Where semantics is not operative, there is at most a series of disjoint representations that add up to nothing true or false, and therefore do not constitute a thought at all. There is necessarily a gulf between simulating thought, on the one hand, and actually thinking, on the other. It doesn't matter (...) how perfect the simulation is; nor does it matter how reliable the causal mechanism involved is. Where semantics is inert, there is no thought. In connection with this, this paper also argues that a popular doctrine - the so-called 'computational theory of mind' (CTM) - is based on a confusion. CTM is the view that thought-processes consist in 'computations', where a computation is defined as a 'form-driven' operation on symbols. The expression 'form-driven operation' is ambiguous, and may refer either to syntax-driven operations or to morphology-driven operations. Syntax-driven operations presuppose the existence of operations that are driven by semantic and extra-semantic knowledge. So CTM is false if the terms 'computation' and 'form-driven operation' are taken to refer to syntax-driven operations. So if CTM is to work, those expressions must be taken to refer to morphology-driven operations. But, as previously stated, an operation must be semantics-driven if it is to qualify as a thought. Thus CTM fails on every disambiguation of the expressions 'formal operation' and 'computation'. (shrink)

In The Mind Doesn’t Work that Way, Jerry Fodor argues that mental representations have context sensitive features relevant to cognition, and that, therefore, the Classical Computational Theory of Mind (CTM) is mistaken. We call this the Globality Argument. This is an in principle argument against CTM. We argue that it is self-defeating. We consider an alternative argument constructed from materials in the discussion, which avoids the pitfalls of the official argument. We argue that it is also unsound and that, while (...) it is an empirical issue whether context sensitive features of mental representations are relevant to cognition, it is empirically implausible. (shrink)

Stevan Harnad correctly perceives a deep problem in computationalism, the hypothesis that cognition is computation, namely, that the symbols manipulated by a computational entity do not automatically mean anything. Perhaps, he proposes, transducers and neural nets will not have this problem. His analysis goes wrong from the start, because computationalism is not as rigid a set of theories as he thinks. Transducers and neural nets are just two kinds of computational system, among many, and any solution to the semantic problem (...) that works for them will work for most other computational systems. (shrink)

In this paper, I want to deal with the triviality threat to computationalism. On one hand, the controversial and vague claim that cognition involves computation is still denied. On the other, contemporary physicists and philosophers alike claim that all physical processes are indeed computational or algorithmic. This claim would justify the computationalism claim by making it utterly trivial. I will show that even if these two claims were true, computationalism would not have to be trivial.

In Connectionism and the Philosophy of Psychology, Horgan and Tienson (1996) argue that cognitive processes, pace classicism, are not governed by exceptionless, “representation-level” rules; they are instead the work of defeasible cognitive tendencies subserved by the non-linear dynamics of the brain’s neural networks. Many theorists are sympathetic with the dynamical characterisation of connectionism and the general (re)conception of cognition that it affords. But in all the excitement surrounding the connectionist revolution in cognitive science, it has largely gone unnoticed that connectionism (...) adds to the traditional focus on computational processes, a new focus – one on the vehicles of mental representation, on the entities that carry content through the mind. Indeed, if Horgan and Tienson’s dynamical characterisation of connectionism is on the right track, then so intimate is the relationship between computational processes and representational vehicles, that connectionist cognitive science is committed to a resemblance theory of mental content. (shrink)

This essential book provides a comprehensive explanation of the key topics and debates arising in the philosophy of psychology. In editors William O'Donohue and Richard Kitchener's thoughtful examination, philosophy and psychology converge on several themes of great importance such as the foundations of knowledge, the nature of science, rationality, behaviorism, cognitive science, folk psychology, neuropsychology, psychoanalysis, professionalism, and research ethics. The Philosophy of Psychology also provides an in-depth discussion of ethics in counseling and psychiatry while exploring the diverse topics listed (...) above. The internationally renowned group of contributors to this volume both stimulating and informative. The Philosophy of Psychology will be invaluable for students and academics in theories and systems in psychology, cognitive psychology, cognitive science, philosophy of the social sciences, philosophy of the mind, and related courses. (shrink)

The volumes are self contained and can be used individually in upper-level undergraduate and graduate courses ranging from introductory psychology, linguistics, ...

Tradition compels me to write dissertation acknowledgements that are long, effusive, and unprofessional. Fortunately for me, I heartily endorse that tradition.

Defending or attacking either functionalism or computationalism requires clarity on what they amount to and what evidence counts for or against them. My goal here is not to evaluate their plausibility. My goal is to formulate them and their relationship clearly enough that we can determine which type of evidence is relevant to them. I aim to dispel some sources of confusion that surround functionalism and computationalism, recruit recent philosophical work on mechanisms and computation to shed light on them, and (...) clarify how functionalism and computationalism may or may not legitimately come together. (shrink)

Roughly speaking, computationalism says that cognition is computation, or that cognitive phenomena are explained by the agent‘s computations. The cognitive processes and behavior of agents are the explanandum. The computations performed by the agents‘ cognitive systems are the proposed explanans. Since the cognitive systems of biological organisms are their nervous 1 systems (plus or minus a bit), we may say that according to computationalism, the cognitive processes and behavior of organisms are explained by neural computations. Some people might prefer to (...) say that cognitive systems are ―realized‖ by nervous systems, and thus that—according to computationalism—cognitive computations are ―realized‖ by neural processes. In this paper, nothing hinges on the nature of the relation between cognitive systems and nervous systems, or between computations and neural processes. For present purposes, if a neural process realizes a computation, then that neural process is a computation. Thus, I will couch much of my discussion in terms of nervous systems and neural computation.1 Before proceeding, we should dispense with a possible red herring. Contrary to a common assumption, computationalism does not stand in opposition to connectionism. Connectionism, in the most general and common sense of the term, is the claim that cognitive phenomena are explained (at some level and at least in part) by the processes of neural networks. This is a truism, supported by most neuroscientific evidence. Everybody ought to be a connectionist in this general sense. The relevant question is, are neural processes computations? More precisely, are the neural processes to be found in the nervous systems of organisms computations? Computationalists say ―yes‖, anti-computationalists say ―no‖. This paper investigates whether any of the arguments on offer against computationalism have a chance at knocking it off.2 Ever since Warren McCulloch and Walter Pitts (1943) first proposed it, computationalism has been subjected to a wide range of objections.. (shrink)

According to the computational theory of mind (CTM), mental capacities are explained by inner computations, which in biological organisms are realized in the brain. Computational explanation is so popular and entrenched that it’s common for scientists and philosophers to assume CTM without argument.

I argue that neural activity, strictly speaking, is not computation. This is because computation, strictly speaking, is the processing of strings of symbols, and neuroscience shows that there are no neural strings of symbols. This has two consequences. On the one hand, the following widely held consequences of computationalism must either be abandoned or supported on grounds independent of computationalism: (i) that in principle we can capture what is functionally relevant to neural processes in terms of some formalism taken from (...) computability theory (such as Turing Machines), (ii) that it is possible to design computer programs that are functionally equivalent to neural processes in the same sense in which it is possible to design computer programs that are functionally equivalent to each other, (iii) that the study of neural (or mental) computation is independent of the study of neural implementation, (iv) that the Church-Turing thesis applies to neural activity in the sense in which it applies to digital computers. On the other hand, we need to gradually reinterpret or replace computational theories in psychology in terms of theoretical constructs that can be realized by known neural processes, such as the spike trains of neuronal ensembles. (shrink)

Some philosophers have conflated functionalism and computationalism. I reconstruct how this came about and uncover two assumptions that made the conflation possible. They are the assumptions that (i) psychological functional analyses are computational descriptions and (ii) everything may be described as performing computations. I argue that, if we want to improve our understanding of both the metaphysics of mental states and the functional relations between them, we should reject these assumptions.

Some philosophers have conflated functionalism and computationalism. I reconstruct how this came about and uncover two assumptions that made the conflation possible. They are the assumptions that (i) psychological functional analyses are computational descriptions and (ii) everything may be described as performing computations. I argue that, if we want to improve our understanding of both the metaphysics of mental states and the functional relations between them, we should reject these assumptions. # 2004 Elsevier Ltd. All rights reserved.

Computationalism says that brains are computing mechanisms, that is, mechanisms that perform computations. At present, there is no consensus on how to formulate computationalism precisely or adjudicate the dispute between computationalism and its foes, or between different versions of computationalism. An important reason for the current impasse is the lack of a satisfactory philosophical account of computing mechanisms. The main goal of this dissertation is to offer such an account.
I also believe that the history of computationalism sheds light on the (...) current debate. By tracing different versions of computationalism to their common historical origin, we can see how the current divisions originated and understand their motivation. Reconstructing debates over computationalism in the context of their own intellectual history can contribute to philosophical progress on the relation between brains and computing mechanisms and help determine how brains and computing mechanisms are alike, and how they differ. Accordingly, my dissertation is divided into a historical part, which traces the early history of computationalism up to 1946, and a philosophical part, which offers an account of computing mechanisms.
The two main ideas developed in this dissertation are that (1) computational states are to be identified functionally not semantically, and (2) computing mechanisms are to be studied by functional analysis. The resulting account of computing mechanism, which I call the functional account of computing mechanisms, can be used to identify computing mechanisms and the functions they compute. I use the functional account of computing mechanisms to taxonomize computing mechanisms based on their different computing power, and I use this taxonomy of computing mechanisms to taxonomize different versions of computationalism based on the functional properties that they ascribe to brains. By doing so, I begin to tease out empirically testable statements about the functional organization of the brain that different versions of computationalism are committed to. I submit that when computationalism is reformulated in the more explicit and precise way I propose, the disputes about computationalism can be adjudicated on the grounds of empirical evidence from neuroscience. (shrink)

Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In (...) this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism and connectionism on the other. We defend the relevance to cognitive science of both computation, in a generic sense that we fully articulate for the first time, and information processing, in three important senses of the term. Our account advances some foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates’ empirical aspects. (shrink)

Since the cognitive revolution, it’s become commonplace that cognition involves both computation and information processing. Is this one claim or two? Is computation the same as information processing? The two terms are often used interchangeably, but this usage masks important differences. In this paper, we distinguish information processing from computation and examine some of their mutual relations, shedding light on the role each can play in a theory of cognition. We recommend that theoristError: Illegal entry in bfrange block in ToUnicode (...) CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMaps of cognition be explicit and careful in choosing 1 notions of computation and information and connecting them together. Much confusion can be avoided by doing so. Keywords: computation, information processing, computationalism, computational theory of mind, cognitivism. (shrink)

Paul Pietroski, McGill University The general topic of_ Mind and World_, the written version of John McDowell's 1991 John Locke Lectures, is how `concepts mediate the relation between minds and the world'. And one of the main aims is `to suggest that Kant should still have a central place in our discussion of the way thought bears on reality' (1).1 In particular, McDowell urges us to adopt a thesis that he finds in Kant, or perhaps in Strawson's Kant: the content (...) of experience is conceptualized; _what_ we experience is always the kind of thing that we could also believe. When an agent has a veridical experience, she `takes in, for instance sees, _that things are thus and so_' (9). McDowell's argument for this thesis is indirect, but potentially powerful. He discusses a tension concerning the roles of experience and conceptual capacities in thought, and he claims that the only adequate resolution involves granting that experiences have conceptualized content. The tension, elaborated below, can be expressed roughly as follows: judgments must be somehow constrained by features of the external environment, else judgments would be utterly divorced from the world they purport to be about; yet our judgments must be somehow free of external control, else we could give no sense to the idea that we are responsible for our judgments. (shrink)

Pollock describes an exciting theory of rationality and its partial implementation in OSCAR, a computer system whose descendants will literally be persons.

All of the chapters have been written especially for the book by the leading scholars in the field.Michael I. Posner is Professor of Psychology at the ...

influence. One of the principal characteristics that distinguishes Cognitive Science from more traditional studies of cognition within Psychology, is the extent to which it has been influenced by both the ideas and the techniques of computing. It may come as a surprise to the outsider, then, to discover that there is no unanimity within the discipline on either (a) the nature (and in some cases the desireabilty) of the influence and (b) what computing is –- or at least on its.

Discussions about the achievements and limitations of the various approaches to the development of intelligent systems can have an essential impact on empirically based research, and with that also on the future development of computer technologies. However, such discussions are often based on vague concepts and assumptions. In this context, we claim that the proposed `three-world ontology'' offers the most appropriate conceptual framework in which the basic problems concerned with cognition and computation can be suitably expressed and discussed, although the (...) solutions of some of these problems seem to lie beyond the horizon of our current understanding. We stress the necessity to differentiate between authentic and functional cognitive abilities; although computation is not a plausible way towards authentic intelligence, we claim that computational systems do offer virtually unlimited possibilities to replicate and surpass human cognitive abilities on the functional level. (shrink)

The proper treatment of computationalism, as the thesis that cognition is computable, is presented and defended. Some arguments of James H. Fetzer against computationalism are examined and found wanting, and his positive theory of minds as semiotic systems is shown to be consistent with computationalism. An objection is raised to an argument of Selmer Bringsjord against one strand of computationalism, namely, that Turing-Test± passing artifacts are persons, it is argued that, whether or not this objection holds, such artifacts will inevitably (...) be persons. (shrink)

How can abductive reasoning be physical, feasible, and reliable? This is Fodor’s riddle of abduction, and its apparent intractability is the cause of Fodor’s recent pessimism regarding the prospects for cognitive science. I argue that this riddle can be solved if we augment the computational theory of mind to allow for non-computational mental processes, such as those posited by classical associationists and contemporary connectionists. The resulting hybrid theory appeals to computational mechanisms to explain the semantic coherence of inference and associative (...) mechanisms to explain the efficient retrieval of relevant information from memory. The interaction of these mechanisms explains how abduction can be physical, feasible, and reliable. (shrink)

This paper is a follow-up of the first part of the persons reply to the Chinese Room Argument. The first part claims that the mental properties of the person appearing in that argument are what matter to whether computational cognitive science is true. This paper tries to discern what those mental properties are by applying a series of hypothetical psychological and strengthened Turing tests to the person, and argues that the results support the thesis that the Man performing the computations (...) characteristic of understanding Chinese actually understands Chinese. The supposition that the Man does not understand Chinese has gone virtually unquestioned in this foundational debate. The persons reply acknowledges the intuitive power behind that supposition, but knows that brute intuitions are not epistemically sacrosanct. Like many intuitions humans have had, and later deposed, this intuition does not withstand experimental scrutiny. The second part of the persons reply consequently holds that computational cognitive science is confirmed by the Chinese Room thought experiment. (shrink)

Wittgenstein’s views invite a modest, functionalist account of mental states and regularities, or more specifically a causal/computational, representational theory of the mind (CRTT). It is only by understandingWittgenstein’s remarks in the context of a theory like CRTT that his insights have any real force; and it is only by recognizing those insights that CRTT can begin to account for sensations and our thoughts about them. For instance, Wittgenstein’s (in)famous remark that “an inner process stands in need of outward criteria” (PI:§580), (...) so implausible read behaviorally, is entirely plausible if the “outward” is allowed to include computational facts about our brains. But what is especially penetrating about Wittgenstein’s discussion is his unique diagnosis of our puzzlement in this area, in particular, his suggestion that it is due to our captivation by “pictures” whose application to reality is left crucially under-specified. It is only by understanding. What sustains the naive picture is not a captivation by language, but, at least in part, our largely involuntary reactions to things that look and act like our conspecifics. We project a property into them correlative to that reaction in ourselves, and are, indeed, unwilling to project it into things that do not induce that reaction. (shrink)