This book provides a sustained and penetrating critique of a wide range of views in modern cognitive science and philosophy of the mind, from Turing's famous test for intelligence in machines to recent work in computational linguistic theory. While discussing many of the key arguments and topics, the authors also develop a distinctive analytic approach. Drawing on the methods of conceptual analysis first elaborated by Wittgenstein and Ryle, the authors seek to show that these methods still have a (...) great deal to offer in the field of the cognitive theory and the philosophy of mind, providing a powerful alternative to many of the positions put forward in the contemporary literature. Amoung the many issues discussed in the book are the following: the Cartesian roots of modern conceptions of mind; Searle's 'Chinese Room' thought experiment; Fodor's 'language of thought' hypothesis; the place of 'folk psychology' in cognitivist thought; and the question of whether any machine may be said to 'think' or 'understand' in the ordinary senses of these words. Wide ranging, up-to-date and forcefully argued, this book represents a major intervention in contemporary debates about the status of cognitive science an the nature of mind. It will be of particular interest to students and scholars in philosophy, psychology, linguistics and computing sciences. (shrink)

In the book, I argue that the mind can be explained computationally because it is itself computational—whether it engages in mental arithmetic, parses natural language, or processes the auditory signals that allow us to experience music. All these capacities arise from complex information-processing operations of the mind. By analyzing the state of the art in cognitive science, I develop an account of computational explanation used to explain the capacities in question.

While the notion of the mind as information-processor--a kind of computational system--is widely accepted, many scientists and philosophers have assumed that this account of cognition shows that the mind's operations are characterizable independent of their relationship to the external world. Existential Cognition challenges the internalist view of mind, arguing that intelligence, thought, and action cannot be understood in isolation, but only in interaction with the outside world. Arguing that the mind is essentially embedded in the (...) external world, Ron McClamrock provides a schema that allows cognitive scientists to address such long-standing problems in artificial intelligence as the "frame" problem and the issue of "bounded" rationality. Extending this schema to cover progress in other studies of behavior, including language, vision, and action, McClamrock reinterprets the importance of the organism/environment distinction. McClamrock also considers the broader philosophical question of the place of mind in the world, particularly with regard to questions of intentionality, subjectivity, and phenomenology. With implications for philosophy, cognitive and computer science, AI, and psychology, this book synthesizes state-of-the-art work in philosophy and cognitive science on how the mind interacts with the world to produce thoughts, ideas, and actions. (shrink)

Examining one of the fundamental issues in cognitive psychology: How does our conscious experience come to be the way it is?

Discusses the problems that surround the developing science of Artificial Intelligence (AI). This title introduces and clarifies the basic concepts for understanding these problems and also discusses opposing views and possible solutions. It describes the kinds of research that seem to improve our understanding of the mechanisms of intelligence.

The computational mind paradigm proposes that the mind is an information-processing system equivalent to a Turing machine. Some proponents of this view hope to emulate the mind using methods such as symbolism, connectionism or more biological models. In the present work, the following question is posed: is a computational mind capable of deciding (yes or no) whether a proposed emulation of the mind is indeed an emulation of the mind? It is argued (...) that this is not possible. Intuitively, the reason a computational mind cannot recognize an emulation of itself is for much the same reason that a set of scales cannot weigh itself. In this article, a formal argument for this stance is given by noticing the following: for a computational mind to recognize an emulation of itself, it must be capable of deciding whether two Turing Machines (namely, itself and the proposed emulation) are functionally equivalent. This task is uncomputable, and thus there cannot exist a computational procedure in the mind that is capable of recognizing an emulation of itself. (shrink)

We overview logical and computational explanations of the notion of tractability as applied in cognitive science. We start by introducing the basics of mathematical theories of complexity: computability theory, computational complexity theory, and descriptive complexity theory. Computational philosophy of mind often identifies mental algorithms with computable functions. However, with the development of programming practice it has become apparent that for some computable problems finding effective algorithms is hardly possible. Some problems need too much computational resource, (...) e.g., time or memory, to be practically computable. Computational complexity theory is concerned with the amount of resources required for the execution of algorithms and, hence, the inherent difficulty of computational problems. An important goal of computational complexity theory is to categorize computational problems via complexity classes, and in particular, to identify efficiently solvable problems and draw a line between tractability and intractability. -/- We survey how complexity can be used to study computational plausibility of cognitive theories. We especially emphasize methodological and mathematical assumptions behind applying complexity theory in cognitive science. We pay special attention to the examples of applying logical and computational complexity toolbox in different domains of cognitive science. We focus mostly on theoretical and experimental research in psycholinguistics and social cognition. (shrink)

Believing that the enterprise of constructing "artificial intelligence" transcends the bounds of any one discipline, the editors of Mechanical Bodies, Computational Minds have brought together researchers in AI and scholars in the humanities to reexamine the fundamental assumptions of both areas. The AI community, for example, could benefit from explorations of human intelligence and creativity by philosophers, psychologists, anthropologists, literary critics, and others, while analysis of AI's theoretical struggles and technical advances could yield insights into such traditional humanist concerns (...) as the nature of rationality and the mind-body dichotomy.The contributions include a continuation of the famous Hubert Dreyfus-Daniel Dennett debate over Kasparov's defeat by IBM's Deep Blue; Philip Agre's tracing of difficulties in AI research to the inherited tensions of Cartesian dualism; Evelyn Fox Keller's examination of the development of computer technology in relation to biology; Douglas Hofstadter's argument that thinking is more than the theorem-solving activities of AI; and Alison Adam's discussion of the implicitly male universal subject used in AI. (shrink)

Review of Ron McClamrock's Existential Cognition.

Computational approaches dominate contemporary cognitive science, promising a unified, scientific explanation of how the mind works. However, computational approaches raise major philosophical and scientific questions. In what sense is the mind computational? How do computational approaches explain perception, learning, and decision making? What kinds of challenges should computational approaches overcome to advance our understanding of mind, brain, and behaviour? The Routledge Handbook of the Computational Mind is an outstanding overview and (...) exploration of these issues and the first philosophical collection of its kind. Comprising thirty-five chapters by an international team of contributors from different disciplines, the Handbook is organised into four parts: History and future prospects of computational approaches Types of computational approach Foundations and challenges of computational approaches Applications to specific parts of psychology. Essential reading for students and researchers in philosophy of mind, philosophy of psychology, and philosophy of science, The Routledge Handbook of the Computational Mind will also be of interest to those studying computational models in related subjects such as psychology, neuroscience, and computer science. (shrink)

In his bestselling work of popular science, Sir Roger Penrose takes us on a fascinating roller-coaster ride through the basic principles of physics, cosmology, mathematics, and philosophy to show that human thinking can never be emulated by a machine.

L'image de l'écriture est singulièrement fréquente dans des explications cognitivistes du fonctionnement de l'esprit, non seulement comme métaphore mais également comme paradigme conceptuel: la machine de Turing, en particulier, montre un isomorphisme structural complet avec l'utilisation de l'écriture alphabétique. La machine de Turing effectue exactement les mêmes opérations effectuées par un homme écrivant avec le stylo et le papier et cela dépend de deux raisons: 1. il a été conçu dans l'image et la similarité de la pratique concrète humaine de (...) l'écriture ; 2. c'est le produit typique de la rationalité occidentale, dont le développement n'a été rendu possible que par les caractères spécifiques du traitement alphabétique de l'information. Ces aspects sont utiles pour comprendre la raison pour laquelle l'esprit a commencé à être dépeint comme machine d'écriture au moment même où l'alphabet a commencé à se diffuser: une recherche généalogique montrera comment, dans des traités de mnémotechniques et dans la métaphysique de Platon et d'Aristote, nous pouvons trouver la toute première base de chaque modèle logicosymbolique de l'esprit. (shrink)

review of Stefano Franchi and Güven Güzeldere, eds., Mechanical Bodies, Computational Minds: Artificial Intelligence from Automata to Cyborgs.

The term 'consciousness' has not been consistently used in the history of philosophy and psychology. It has been taken to stand for the mental activity in which all of us are engaged during our waking lives, whether absorbed in the solving of a task or in calm moments of contemplation. It has also been allied with the term 'introspection' to stand for a self-monitoring activity, one in which we are not simply engaged, but in which we aware of the succession (...) of mental items that constitute our experiences. In Consciousness and the Computational Mind, Ray Jackendoff opts for this second sense and exploits it to criticize the "central processing" theory of consciousness. The background assumption for all such theories is that the human mind is to be conceived on the model of a computer whose central processor controls in accordance with programmed instructions the reception of inputs, delegation of tasks to secondary processors carrying out sub-routines, storage and retrieval of information, and finally the production of outputs. The central processing theory identifies consciousness with the activities of those areas of the brain which function as central processor as defined by this model. Jackendoff's alternative theory correlates consciousness with intermediate-level informational structures operated on by secondary processors. Of the higher-level structures and the processes operating on them, the level we refer to with such terms as 'conceptualization' and 'understanding,' we are, he contends, unconscious. (shrink)

By reconciling the linguistic device and the linguistic person, his book argues for a Vygotskyan cognitive science.

"The Emperor's New Mind" by Roger Penrose has received a great deal of both praise and criticism. This review discusses philosophical aspects of the book that form an attack on the "strong" AI thesis. Eight different versions of this thesis are distinguished, and sources of ambiguity diagnosed, including different requirements for relationships between program and behaviour. Excessively strong versions attacked by Penrose (and Searle) are not worth defending or attacking, whereas weaker versions remain problematic. Penrose (like Searle) regards the (...) notion of an algorithm as central to AI, whereas it is argued here that for the purpose of explaining mental capabilities the architecture of an intelligent system is more important than the concept of an algorithm, using the premise that what makes something intelligent is not what it does but how it does it. What needs to be explained is also unclear: Penrose thinks we all know what consciousness is and claims that the ability to judge Go "del's formula to be true depends on it. He also suggests that quantum phenomena underly consciousness. This is rebutted by arguing that our existing concept of "consciousness" is too vague and muddled to be of use in science. This and related concepts will gradually be replaced by a more powerful theory-based taxonomy of types of mental states and processes. The central argument offered by Penrose against the strong AI thesis depends on a tempting but unjustified interpretation of Goedel's incompleteness theorem. Some critics are shown to have missed the point of his argument. A stronger criticism is mounted, and the relevance of mathematical Platonism analysed. Architectural requirements for intelligence are discussed and differences between serial and parallel implementations analysed. (shrink)

Existential Cognition, divided into four parts of three chapters each, argues that the mind “is an essentially embedded entity; one such that analyzing it in isolation from the environmental context in which it functions will be fundamentally misleading”. Disputing internalists who accept, and who reject, information processing accounts of the mind, as well as anti-cognitivists who reject internalism, McClamrock argues for an externalist information processing account of mental states and processes.

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

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)

Extract from Hofstadter's revew in Bulletin of American Mathematical Society : http://www.ams.org/journals/bull/1980-02-02/S0273-0979-1980-14752-7/S0273-0979-1980-14752-7.pdf -/- "Aaron Sloman is a man who is convinced that most philosophers and many other students of mind are in dire need of being convinced that there has been a revolution in that field happening right under their noses, and that they had better quickly inform themselves. The revolution is called "Artificial Intelligence" (Al)-and Sloman attempts to impart to others the "enlighten- ment" which he clearly regrets not (...) having experienced earlier himself. Being somewhat of a convert, Sloman is a zealous campaigner for his point of view. Now a Reader in Cognitive Science at Sussex, he began his academic career in more orthodox philosophy and, by exposure to linguistics and AI, came to feel that all approaches to mind which ignore AI are missing the boat. I agree with him, and I am glad that he has written this provocative book. The tone of Sloman's book can be gotten across by this quotation (p. 5): "I am prepared to go so far as to say that within a few years, if there remain any philosophers who are not familiar with some of the main developments in artificial intelligence, it will be fair to accuse them of professional incom- petence, and that to teach courses in philosophy of mind, epistemology, aesthetics, philosophy of science, philosophy of language, ethics, metaphysics, and other main areas of philosophy, without discussing the relevant aspects of artificial intelligence will be as irresponsible as giving a degree course in physics which includes no quantum theory." -/- (The author now regrets the extreme polemical tone of the book.). (shrink)

: This paper uses Extended Mind Theory to explore Brain-Computer Interfaces, demonstrating how this conceptual framework provides a wide-ranging interpretation of the potential integration of user and computer. After a preliminary analysis of first- and second-wave EMT arguments and other pragmatic criteria, I present BCI technology, addressing the issues that arise. Can BCIs extend our mental processes and to what degree? What EMT criteria should be applied to this technology? What is the role of the body in the process (...) of integrating user and computer? What are current limits to complete cognitive and bodily extension by BCIs? In line with this discussion, I suggest a pluralist approach to BCIs, allowing for specific and appropriate application of the various models and paradigms. I also advocate greater focus on the integration of body and tool, primarily for clinical purposes, but also for applications that will meet daily needs in the future. Keywords : Extended Mind; Brain-Computer Interface; Embodiment; Parity Principle; Cognitive Artefacts. Mente estesa e brain-computer interface. Un approccio pluralista all’integrazione uomo-macchina Riassunto: Il presente articolo fa uso della Extended Mind Theory per indagare le Brain-Computer Interfaces, dimostrando che questo framework concettuale offre un’interpretazione ad ampio raggio della potenziale integrazione tra utente e computer. Dopo un’analisi preliminare degli argomenti della EMT di prima e seconda generazione e altri criteri pragmatici, presenterò la tecnologia delle BCIs, affrontando alcune questioni a essa collegate. Le BCIs possono estendere i nostri processi mentali e fino a che punto? Quali criteri della EMT dovrebbero essere applicati a questa tecnologia? Qual è il ruolo del corpo nel processo di integrazione utente-computer? Quali sono gli attuali limiti per completare l’estensione cognitiva e corporea da parte delle BCIs? In linea con questa discussione, suggerirò un approccio pluralistico alle BCIs che permetta un’applicazione specifica e appropriata dei vari modelli e paradigmi. Sosterrò inoltre la necessità di una maggiore attenzione all’integrazione tra corpo e strumento, principalmente per scopi clinici ma anche per applicazioni future che soddisfino le esigenze quotidiane. Parole chiave: Mente estesa; Brain-Computer Interface ; Incorporamento; Principio di Parità; Artefatti cognitivi. (shrink)

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)

According to pancomputationalism, everything is a computing system. In this paper, I distinguish between different varieties of pancomputationalism. I find that although some varieties are more plausible than others, only the strongest variety is relevant to the philosophy of mind, but only the most trivial varieties are true. As a side effect of this exercise, I offer a clarified distinction between computational modelling and computational explanation.<br><br>.

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.

_Minds, Brains, Computers_ serves as both an historical and interdisciplinary introduction to the foundations of cognitive science.

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

I argue that Stich's Syntactic Theory of Mind (STM) and a naturalistic narrow content functionalism run on a Language of Though story have the same exact structure. I elaborate on the argument that narrow content functionalism is either irremediably holistic in a rather destructive sense, or else doesn't have the resources for individuating contents interpersonally. So I show that, contrary to his own advertisement, Stich's STM has exactly the same problems (like holism, vagueness, observer-relativity, etc.) that he claims plague (...) content-based psychologies. So STM can't be any better than the Representational Theory of Mind (RTM) in its prospects for forming the foundations of a scientifically respectable psychology, whether or not RTM has the problems that Stich claims it does. (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)

An important, but relatively neglected, aspect of human theory of mind is emotion inference: understanding how and why a person feels a certain why is central to reasoning about their beliefs, desires and plans. The authors review recent work that has begun to unveil the structure and determinants of emotion inference, organizing them within a unified probabilistic framework.

This work addresses a broad range of questions which belong to four fields: computation theory, general philosophy of science, philosophy of cognitive science, and philosophy of mind. Dynamical system theory provides the framework for a unified treatment of these questions. ;The main goal of this dissertation is to propose a new view of the aims and methods of cognitive science--the dynamical approach . According to this view, the object of cognitive science is a particular set of dynamical systems, which (...) I call "cognitive systems". The goal of a cognitive study is to specify a dynamical model of a cognitive system, and then use this model to produce a detailed account of the specific cognitive abilities of that system. The dynamical approach does not limit a-priori the form of the dynamical models which cognitive science may consider. In particular, this approach is compatible with both computational and connectionist modeling, for both computational systems and connectionist networks are special types of dynamical systems. ;To substantiate these methodological claims about cognitive science, I deal first with two questions in two different fields: What is a computational system? What is a dynamical explanation of a deterministic process? ;Intuitively, a computational system is a deterministic system which evolves in discrete time steps, and which can be described in an effective way. In chapter 1, I give a formal definition of this concept which employs the notions of isomorphism between dynamical systems, and of Turing computable function. In chapter 2, I propose a more comprehensive analysis which is based on a natural generalization of the concept of Turing machine. ;The goal of chapter 3 is to develop a theory of the dynamical explanation of a deterministic process. By a "dynamical explanation" I mean the specification of a dynamical model of the system or process which we want to explain. I start from the analysis of a specific type of explanandum--dynamical phenomena--and I then use this analysis to shed light on the general form of a dynamical explanation. Finally, I analyze the structure of those theories which generate explanations of this form, namely dynamical theories. (shrink)

Van Gelder (1995) has recently spearheaded a movement to challenge the dominance of connectionist and classicist models in cognitive science. The dynamical conception of cognition is van Gelder's replacement for the computation bound paradigms provided by connectionism and classicism. He relies on the Watt governor to fulfill the role of a dynamicist Turing machine and claims that the Motivational Oscillatory Theory (MOT) provides a sound empirical basis for dynamicism. In other words, the Watt governor is to be the theoretical exemplar (...) of the class of systems necessary for cognition and MOT is an empirical instantiation of that class. However, I shall argue that neither the Watt governor nor MOT successfully fulfill these prescribed roles. This failure, along with van Gelder's peculiar use of the concept of computation and his struggle with representationalism, prevent him from providing a convincing alternative to current cognitive theories. (shrink)

The Computational Theory of Mind (CTM) holds that the mind is a computer and that cognition is the manipulation of representations. CTM is commonly viewed as the main hypothesis in cognitive science, with classical CTM (related to the Language of Thought Hypothesis) being the most popular variant. However, other computational accounts of the mind either reject LOTH or do not subscribe to RTM. CTM proponents argue that it clarifies how thought and content are causally relevant (...) in the physical world, and they frame this argument in terms of the physical symbol hypothesis, formalism, or syntactic engines. This article focuses on specific problems with CTM, with four main sections covering the introduction of the three main variants of CTM, the most important conceptions of computational explanation in cognitive science, skeptical arguments against CTM raised by Hilary Putnam, and common objections to CTM. (shrink)

Could a computer have a mind? What kind of machine would this be? Exactly what do we mean by 'mind' anyway?The notion of the 'intelligent' machine, whilst continuing to feature in numerous entertaining and frightening fictions, has also been the focus of a serious and dedicated research tradition. Reflecting on these fictions, and on the research tradition that pursues 'Artificial Intelligence', raises a number of vexing philosophical issues. Minds and Computers introduces readers to these issues by offering an (...) engaging, coherent, and highly approachable interdisciplinary introduction to the Philosophy of Artificial Intelligence.Readers are presented with introductory material from each of the disciplines which constitute Cognitive Science: Philosophy, Neuroscience, Psychology, Computer Science, and Linguistics. Throughout, readers are encouraged to consider the implications of this disparate and wide-ranging material for the possibility of developing machines with minds. And they can expect to de. (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)