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Does computation require representation? To what extent should representation figure within computational models? Can representational properties causally influence computation? How central an explanatory role should semantics occupy within computational psychology? Is the mind a “syntax-driven” machine? Can computational models help elucidate the nature of representation? Can they help us reduce the intentional to the non-intentional? What semantic frameworks are most useful for computer science and Artificial Intelligence? Can we build an artificial computing machine that thinks? How might the construction of such a machine illuminate the mind, including our capacity to represent? Is mental activity best modeled through “classical” computation, through “connectionist” computation, or through some other framework?

Key works The seminal article Turing 1936 introduces the Turing machine, thereby laying the foundation for all subsequent research on computation within computer science, recursion theory, Artificial Intelligence, cognitive psychology, and philosophy. Putnam 1967 introduced philosophers to the thesis that Turing-style computation provides illuminating models of mental activity. Fodor 1975 developed Putnam’s suggestion, combining it with the traditional picture of the mind as a representational organ. Fodor’s subsequent writings, including Fodor 1981 and many other articles and books, investigate the relation between mental computation and mental representation. Stich 1983 combines a computational approach to the mind with eliminativism regarding intentionality. Dennett 1987 advocates a broadly instrumentalist approach to intentionality. Searle 1980 is a widely discussed critique of the computational approach, centered on the relation between syntax and semantics. Putnam 1975 introduces the Twin Earth thought experiment, which crucially informs much of the subsequent literature on computation and representation. Burge 1982 applies the Twin Earth thought experiment to mental representation (whereas Putnam initially applied it only to linguistic representation).
Introductions The first three chapters of Rogers 1987 present the foundations of computation theory, with an emphasis on the Turing machine. Fodor 1981 offers a good (albeit opinionated) introduction to issues surrounding computation and mental representation. Horst 2005 and Pitt 2008 offer helpful surveys of the contemporary literature.
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  1. David Gamez (2012). Empirically Grounded Claims About Consciousness in Computers. International Journal of Machine Consciousness 4 (02):421-438.
    Research is starting to identify correlations between consciousness and some of the spatiotemporal patterns in the physical brain. For theoretical and practical reasons, the results of experiments on the correlates of consciousness have ambiguous interpretations. At any point in time a number of hypotheses co-exist about and the correlates of consciousness in the brain, which are all compatible with the current experimental results. This paper argues that consciousness should be attributed to any system that exhibits spatiotemporal physical patterns that match (...)
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  2. Raffaela Giovagnoli (2013). Representation, Analytic Pragmatism and AI. In Gordana Dodig-Crnkovic Raffaela Giovagnoli (ed.), Computing Nature. 161--169.
    Our contribution aims at individuating a valid philosophical strategy for a fruitful confrontation between human and artificial representation. The ground for this theoretical option resides in the necessity to find a solution that overcomes, on the one side, strong AI (i.e. Haugeland) and, on the other side, the view that rules out AI as explanation of human capacities (i.e. Dreyfus). We try to argue for Analytic Pragmatism (AP) as a valid strategy to present arguments for a form of weak AI (...)
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  3. Stefan Gruner (2013). Eric Winsberg: Science in the Age of Computer Simulation. [REVIEW] Minds and Machines 23 (2):251-254.
  4. David Kirsh (2011). Creative Cognition in Choreography. Proceedings of the 2nd International Conference on Computational Creativity:1-6.
    Contemporary choreography offers a window onto creative processes that rely on harnessing the power of sensory sys- tems. Dancers use their body as a thing to think with and their sensory systems as engines to simulate ideas non- propositionally. We report here on an initial analysis of data collected in a lengthy ethnographic study of the making of a dance by a major choreographer and show how translating between different sensory modalities can help dancers and choreographer to be more creative.
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  5. Marco Mirolli (2012). Representations in Dynamical Embodied Agents: Re-Analyzing a Minimally Cognitive Model Agent. Cognitive Science 36 (5):870-895.
    Understanding the role of ‘‘representations’’ in cognitive science is a fundamental problem facing the emerging framework of embodied, situated, dynamical cognition. To make progress, I follow the approach proposed by an influential representational skeptic, Randall Beer: building artificial agents capable of minimally cognitive behaviors and assessing whether their internal states can be considered to involve representations. Hence, I operationalize the concept of representing as ‘‘standing in,’’ and I look for representations in embodied agents involved in simple categorization tasks. In a (...)
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  6. Przemysław Nowakowski (2011). Phantom Body as Bodily Self-Consciousness. Avant 2 (1):135–149.
    In the article, I propose that the body phantom is a phenomenal and functional model of one’s own body. This model has two aspects. On the one hand, it functions as a tacit sensory representation of the body that is at the same time related to the motor aspects of body functioning. On the other hand, it also has a phenomenal aspect as it constitutes the content of conscious bodily experience. This sort of tacit, functional and sensory model is related (...)
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  7. Todd Peterson, Autonomous Learning of Sequential Tasks: Experiments and Analyses.
    This paper presents a novel learning model Clarion , which is a hybrid model based on the two-level approach proposed in Sun (1995). The model integrates neural, reinforcement, and symbolic learning methods to perform on-line, bottom-up learning (i.e., learning that goes from neural to symbolic representations). The model utilizes both procedural and declarative knowledge (in neural and symbolic representations respectively), tapping into the synergy of the two types of processes. It was applied to deal with sequential decision tasks. Experiments and (...)
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  8. Henry Prakken & John Horty (2012). An Appreciation of John Pollock's Work on the Computational Study of Argument. Argument and Computation 3 (1):1 - 19.
    John Pollock (1940?2009) was an influential American philosopher who made important contributions to various fields, including epistemology and cognitive science. In the last 25 years of his life, he also contributed to the computational study of defeasible reasoning and practical cognition in artificial intelligence. He developed one of the first formal systems for argumentation-based inference and he put many issues on the research agenda that are still relevant for the argumentation community today. This paper presents an appreciation of Pollock's work (...)
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  9. Georges Rey (2012). The Turing Thesis Vs. The Turing Test. The Philosophers' Magazine 57 (57):84-89.
  10. Aaron Sloman (2011). Evolution: The Computer Systems Engineer Designing Minds. Avant 2 (2):45–69.
    What we have learnt in the last six or seven decades about virtual machinery, as a result of a great deal of science and technology, enables us to offer Darwin a new defence against critics who argued that only physical form, not mental capabilities and consciousness could be products of evolution by natural selection. The defence compares the mental phenomena mentioned by Darwin’s opponents with contents of virtual machinery in computing systems. Objects, states, events, and processes in virtual machinery which (...)
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  11. Aaron Sloman (1992). Prolegomena to a Theory of Communication and Affect. In Andrew Ortony, Jon Slack & Oliviero Stock (eds.), Communication from an Artificial Intelligence Perspective: Theoretical and Applied Issues. Springer.
    As a step towards comprehensive computer models of communication, and effective human machine dialogue, some of the relationships between communication and affect are explored. An outline theory is presented of the architecture that makes various kinds of affective states possible, or even inevitable, in intelligent agents, along with some of the implications of this theory for various communicative processes. The model implies that human beings typically have many different, hierarchically organized, dispositions capable of interacting with new information to produce affective (...)
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  12. Arne M. Weber & Gottfried Vosgerau (2012). Grounding Action Representations. Review of Philosophy and Psychology 3 (1):53-69.
    In this paper we discuss an approach called grounded action cognition, which aims to provide a theory of the interdependencies between motor control and action-related cognitive processes, like perceiving an action or thinking about an action. The theory contrasts with traditional views in cognitive science in that it motivates an understanding of cognition as embodied, through application of Barsalou’s general idea of grounded cognition. To guide further research towards an appropriate theory of grounded action cognition we distinguish between grounding qua (...)
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Symbols and Symbol Systems
  1. Fred Adams & Rebecca Garrison (2013). The Mark of the Cognitive. Minds and Machines 23 (3):339-352.
    It is easy to give a list of cognitive processes. They are things like learning, memory, concept formation, reasoning, maybe emotion, and so on. It is not easy to say, of these things that are called cognitive, what makes them so? Knowing the answer is one very important reason to be interested in the mark of the cognitive. In this paper, consider some answers that we think do not work and then offer one of our own which ties cognition to (...)
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  2. Michael L. Anderson & Donald R. Perlis (2002). Symbol Systems. In L. Nagel (ed.), Encyclopedia of Cognitive Science. Macmillan.
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  3. Lawrence W. Barsalou (2010). Grounded Cognition: Past, Present, and Future. Topics in Cognitive Science 2 (4):716-724.
    Thirty years ago, grounded cognition had roots in philosophy, perception, cognitive linguistics, psycholinguistics, cognitive psychology, and cognitive neuropsychology. During the next 20 years, grounded cognition continued developing in these areas, and it also took new forms in robotics, cognitive ecology, cognitive neuroscience, and developmental psychology. In the past 10 years, research on grounded cognition has grown rapidly, especially in cognitive neuroscience, social neuroscience, cognitive psychology, social psychology, and developmental psychology. Currently, grounded cognition appears to be achieving increased acceptance throughout cognitive (...)
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  4. Lawrence W. Barsalou (1999). Perceptions of Perceptual Symbols. Behavioral and Brain Sciences 22 (4):637-660.
    Various defenses of amodal symbol systems are addressed, including amodal symbols in sensory-motor areas, the causal theory of concepts, supramodal concepts, latent semantic analysis, and abstracted amodal symbols. Various aspects of perceptual symbol systems are clarified and developed, including perception, features, simulators, category structure, frames, analogy, introspection, situated action, and development. Particular attention is given to abstract concepts, language, and computational mechanisms.
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  5. Istvan S. Berkeley (2008). What the <0.70, 1.17, 0.99, 1.07> is a Symbol? Minds and Machines 18 (1):93-105.
    The notion of a ‘symbol’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that give rise to (...)
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  6. Istvan S. N. Berkeley (2008). What the is a Symbol? Minds and Machines 18 (1).
    The notion of a ‘symbol’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that give (...)
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  7. Istvan S. N. Berkeley (2001). Peter Novak, Mental Symbols: A Defence of the Classical Theory of Mind. Studies in Cognitive Systems 19, Dordrecht, Netherlands: Kluwer Academic Publishers, 1997, XXII + 266 Pp., $114.00, ISBN 0-7923-4370-. [REVIEW] Minds and Machines 11 (1):148-150.
  8. Paul Richard Blum (2010). MICHAEL POLANYI: CAN THE MIND BE REPRESENTED BY A MACHINE? Polanyiana 19 (1-2):35-60.
    In 1949, the Department of Philosophy at the University of Manchester organized a symposium “Mind and Machine” with Michael Polanyi, the mathematicians Alan Turing and Max Newman, the neurologists Geoff rey Jeff erson and J. Z. Young, and others as participants. Th is event is known among Turing scholars, because it laid the seed for Turing’s famous paper on “Computing Machinery and Intelligence”, but it is scarcely documented. Here, the transcript of this event, together with Polanyi’s original statement and his (...)
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  9. C. Franklin Boyle (2001). Transduction and Degree of Grounding. Psycoloquy 12 (36).
    While I agree in general with Stevan Harnad's symbol grounding proposal, I do not believe "transduction" (or "analog process") PER SE is useful in distinguishing between what might best be described as different "degrees" of grounding and, hence, for determining whether a particular system might be capable of cognition. By 'degrees of grounding' I mean whether the effects of grounding go "all the way through" or not. Why is transduction limited in this regard? Because transduction is a physical process which (...)
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  10. Warren Breckman (2012). Lefort and the Symbolic Dimension. Constellations 19 (1):30-36.
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  11. Selmer Bringsjord, People Are Infinitary Symbol Systems: No Sensorimotor Capacity Necessary.
    Stevan Harnad and I seem to be thinking about many of the same issues. Sometimes we agree, sometimes we don't; but I always find his reasoning refreshing, his positions sensible, and the problems with which he's concerned to be of central importance to cognitive science. His "Grounding Symbols in the Analog World with Neural Nets" (= GS) is no exception. And GS not only exemplifies Harnad's virtues, it also provides a springboard for diving into Harnad- Bringsjord terrain.
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  12. P. Cariani (2012). Mind, a Machine? Review of “The Search for a Theory of Cognition: Early Mechanisms and New Ideas” Edited by Stefano Franchi and Francesco Bianchini. Constructivist Foundations 7 (3):222-227.
    Upshot: Written by recognized experts in their fields, the book is a set of essays that deals with the influences of early cybernetics, computational theory, artificial intelligence, and connectionist networks on the historical development of computational-representational theories of cognition. In this review, I question the relevance of computability arguments and Jonasian phenomenology, which has been extensively invoked in recent discussions of autopoiesis and Ashby’s homeostats. Although the book deals only indirectly with constructivist approaches to cognition, it is useful reading for (...)
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  13. Andrew Chignell (2009). Are Supersensibles Really Possible? The Evidential Role of Symbols. In V. Rhoden, T. Terra & G. Almeida (eds.), Recht und Frieden in der Philosophie Kants. DeGruyter.
    Kant on how certain experiences might give us considerations counting in favor of the real possibility of certain things. -/- .
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  14. Noam A. Chomsky (1980). Rules and Representations. Behavioral and Brain Sciences 3 (127):1-61.
    The book from which these sections are excerpted (N. Chomsky, Rules and Representations, Columbia University Press, 1980) is concerned with the prospects for assimilating the study of human intelligence and its products to the natural sciences through the investigation of cognitive structures, understood as systems of rules and representations that can be regarded as These mental structui′es serve as the vehicles for the exercise of various capacities. They develop in the mind on the basis of an innate endowment that permits (...)
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  15. Patricia Smith Churchland, Rick Grush, Rob Wilson & Frank Keil, Computation and the Brain.
    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 (...)
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  16. A. Clark & Ronald Lutz (eds.) (1992). Connectionism in Context. Springer-Verlag.
  17. Andy Clark (2006). Material Symbols. Philosophical Psychology 19 (3):291-307.
    What is the relation between the material, conventional symbol structures that we encounter in the spoken and written word, and human thought? A common assumption, that structures a wide variety of otherwise competing views, is that the way in which these material, conventional symbol-structures do their work is by being translated into some kind of content-matching inner code. One alternative to this view is the tempting but thoroughly elusive idea that we somehow think in some natural language (such as English). (...)
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  18. Robert Cummins (2010). The World in the Head. OUP Oxford.
    The World in the Head collects the best of Robert Cummins' papers on mental representation and psychological explanation. Running through these papers are a pair of themes: that explaining the mind requires functional analysis, not subsumption under "psychological laws", and that the propositional attitudes--belief, desire, intention--and their interactions, while real, are not the key to understanding the mind at a fundamental level. Taking these ideas seriously puts considerable strain on standard conceptions of rationality and reasoning, on truth-conditional semantics, and on (...)
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  19. Robert C. Cummins (1996). Representations, Targets, and Attitudes. MIT Press.
    "This is an important new Cummins work.
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  20. Robert C. Cummins (1996). Why There is No Symbol Grounding Problem? In Representations, Targets, and Attitudes. MIT Press.
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  21. Eric Dietrich (2002). Subvert the Dominant Paradigm! J. Of Experimental and Theoretical AI.
    We again press the case for computationalism by considering the latest in ill- conceived attacks on this foundational idea. We briefly but clearly define and delimit computationalism and then consider three authors from a new anti- computationalist collection.
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  22. Eric Dietrich (2001). AI, Concepts, and the Paradox of Mental Representation, with a Brief Discussion of Psychological Essentialism. J. Of Exper. And Theor. AI 13 (1):1-7.
    Mostly philosophers cause trouble. I know because on alternate Thursdays I am one -- and I live in a philosophy department where I watch all of them cause trouble. Everyone in artificial intelligence knows how much trouble philosophers can cause (and in particular, we know how much trouble one philosopher -- John Searle -- has caused). And, we know where they tend to cause it: in knowledge representation and the semantics of data structures. This essay is about a recent case (...)
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  23. Andrew A. Fingelkurts, Alexander A. Fingelkurts & Carlos F. H. Neves (2012). “Machine” Consciousness and “Artificial” Thought: An Operational Architectonics Model Guided Approach. Brain Research 1428:80-92.
    Instead of using low-level neurophysiology mimicking and exploratory programming methods commonly used in the machine consciousness field, the hierarchical Operational Architectonics (OA) framework of brain and mind functioning proposes an alternative conceptual-theoretical framework as a new direction in the area of model-driven machine (robot) consciousness engineering. The unified brain-mind theoretical OA model explicitly captures (though in an informal way) the basic essence of brain functional architecture, which indeed constitutes a theory of consciousness. The OA describes the neurophysiological basis of the (...)
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  24. Marcello Frixione & Antonio Lieto (2013). Dealing with Concepts: From Cognitive Psychology to Knowledge Representation. Frontiers of Psychological and Behevioural Science 2 (3):96-106.
    Concept representation is still an open problem in the field of ontology engineering and, more generally, of knowledge representation. In particular, the issue of representing “non classical” concepts, i.e. concepts that cannot be defined in terms of necessary and sufficient conditions, remains unresolved. In this paper we review empirical evidence from cognitive psychology, according to which concept representation is not a unitary phenomenon. On this basis, we sketch some proposals for concept representation, taking into account suggestions from psychological research. In (...)
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  25. Marcello Frixione & Antonio Lieto (2012). Representing Concepts in Formal Ontologies: Compositionality Vs. Typicality Effects&Quot;,. Logic and Logical Philosophy 21 ( Logic, Reasoning and Rationalit):391-414.
    The problem of concept representation is relevant for many sub-fields of cognitive research, including psychology and philosophy, as well as artificial intelligence. In particular, in recent years it has received a great deal of attention within the field of knowledge representation, due to its relevance for both knowledge engineering as well as ontology-based technologies. However, the notion of a concept itself turns out to be highly disputed and problematic. In our opinion, one of the causes of this state of affairs (...)
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  26. A. Glenberg (2008). Grounding Symbolic Operations in the Brain's Modal Systems. In G. R. Semin & Eliot R. Smith (eds.), Embodied Grounding: Social, Cognitive, Affective, and Neuroscientific Approaches. Cambridge University Press.
  27. Miles Groth (1995). Psychology and Nihilism. A Genealogical Critique of the Computational Model of Mind. Review of Metaphysics 48 (4):894-895.
  28. Stevan Harnad, Grounding Symbolic Capacity in Robotic Capacity.
    According to "computationalism" (Newell, 1980; Pylyshyn 1984; Dietrich 1990), mental states are computational states, so if one wishes to build a mind, one is actually looking for the right program to run on a digital computer. A computer program is a semantically interpretable formal symbol system consisting of rules for manipulating symbols on the basis of their shapes, which are arbitrary in relation to what they can be systematically interpreted as meaning. According to computationalism, every physical implementation of the right (...)
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  29. Stevan Harnad, Psychophysical and Cognitive Aspects of Categorical Perception:A Critical Overview.
    There are many entry points into the problem of categorization. Two particularly important ones are the so-called top-down and bottom-up approaches. Top-down approaches such as artificial intelligence begin with the symbolic names and descriptions for some categories already given; computer programs are written to manipulate the symbols. Cognitive modeling involves the further assumption that such symbol-interactions resemble the way our brains do categorization. An explicit expectation of the top-down approach is that it will eventually join with the bottom-up approach, which (...)
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  30. Stevan Harnad (2002). Symbol Grounding and the Origin of Language. In Matthias Scheutz (ed.), Computationalism: New Directions. MIT Press.
    What language allows us to do is to "steal" categories quickly and effortlessly through hearsay instead of having to earn them the hard way, through risky and time-consuming sensorimotor "toil" (trial-and-error learning, guided by corrective feedback from the consequences of miscategorisation). To make such linguistic "theft" possible, however, some, at least, of the denoting symbols of language must first be grounded in categories that have been earned through sensorimotor toil (or else in categories that have already been "prepared" for us (...)
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  31. Stevan Harnad, Symbol Grounding is an Empirical Problem: Neural Nets Are Just a Candidate Component.
    "Symbol Grounding" is beginning to mean too many things to too many people. My own construal has always been simple: Cognition cannot be just computation, because computation is just the systematically interpretable manipulation of meaningless symbols, whereas the meanings of my thoughts don't depend on their interpretability or interpretation by someone else. On pain of infinite regress, then, symbol meanings must be grounded in something other than just their interpretability if they are to be candidates for what is going on (...)
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  32. Stevan Harnad (1992). Connecting Object to Symbol in Modeling Cognition. In A. Clark & Ronald Lutz (eds.), Connectionism in Context. Springer-Verlag. 75--90.
    Connectionism and computationalism are currently vying for hegemony in cognitive modeling. At first glance the opposition seems incoherent, because connectionism is itself computational, but the form of computationalism that has been the prime candidate for encoding the "language of thought" has been symbolic computationalism (Dietrich 1990, Fodor 1975, Harnad 1990c; Newell 1980; Pylyshyn 1984), whereas connectionism is nonsymbolic (Fodor & Pylyshyn 1988, or, as some have hopefully dubbed it, "subsymbolic" Smolensky 1988). This paper will examine what is and is not (...)
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  33. Stevan Harnad (1992). Virtual Symposium on Virtual Mind. Minds and Machines 2 (3):217-238.
    When certain formal symbol systems (e.g., computer programs) are implemented as dynamic physical symbol systems (e.g., when they are run on a computer) their activity can be interpreted at higher levels (e.g., binary code can be interpreted as LISP, LISP code can be interpreted as English, and English can be interpreted as a meaninguful conversation). These higher levels of interpretability are called ‘virtual’ systems. If such a virtual system is interpretable as if it had a mind, is such a ‘virtual (...)
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  34. Stevan Harnad (1990). The Symbol Grounding Problem. 42:335-346.
    There has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the symbol grounding problem: How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their (arbitrary) shapes, be grounded (...)
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  35. Stevan Harnad (1989). Minds, Machines and Searle. .
    Searle's celebrated Chinese Room Argument has shaken the foundations of Artificial Intelligence. Many refutations have been attempted, but none seem convincing. This paper is an attempt to sort out explicitly the assumptions and the logical, methodological and empirical points of disagreement. Searle is shown to have underestimated some features of computer modeling, but the heart of the issue turns out to be an empirical question about the scope and limits of the purely symbolic (computational) model of the mind. Nonsymbolic modeling (...)
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  36. Terence E. Horgan (1992). From Cognitive Science to Folk Psychology: Computation, Mental Representation, and Belief. Philosophy and Phenomenological Research 52 (2):449-484.
  37. Steven Horst (1999). Symbols and Computation: A Critique of the Computational Theory of Mind. Minds and Machines 9 (3):347-381.
    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 (...)
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  38. John E. Hummel (2010). Symbolic Versus Associative Learning. Cognitive Science 34 (6):958-965.
    Ramscar and colleagues (2010, this volume) describe the “feature-label-order” (FLO) effect on category learning and characterize it as a constraint on symbolic learning. I argue that FLO is neither a constraint on symbolic learning in the sense of “learning elements of a symbol system” (instead, it is an effect on nonsymbolic, association learning) nor is it, more than any other constraint on category learning, a constraint on symbolic learning in the sense of “solving the symbol grounding problem.”.
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