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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. Omofolabo Ajayi (2008). From His Symbol to Her Icon. American Journal of Semiotics 8 (3):31 - 52.
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  2. Torin Alter (2000). Symbolic Meaning and the Confederate Battle Flag. Philosophy in the Contemporary World 7 (2/3):1-4.
    The Confederate Battle Flag (CBF) is in the news again. On January 16th, 2000, 46,000 people came to Columbia, South Carolina, to protest its display over the state’s capital dome. On July 1st, the CBF was removed. But on the same day, it was raised in front of the Statehouse steps. The controversy has received a great deal of media coverage and was a factor in the 2000 presidential primaries. CBF displays raise a philosophical question I wish to address: What (...)
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  3. Edward G. Armstrong (1994). From Symbol to Simulacrum. Semiotics:3-9.
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  4. J. E. B. (1957). Sound and Symbol. Review of Metaphysics 10 (3):546-546.
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  5. N. D. B. (1961). Formal Representation of Intentionally Structured Systems. Review of Metaphysics 15 (1):195-195.
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  6. Engen Biser (2013). Symbol and Man. Philosophy Today 4 (4):238-249.
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  7. M. Born (1966). Symbol and Reality. Dialectica 20 (2):143-157.
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  8. Xiang Chen (2001). Perceptual Symbols and Taxonomy Comparison. Philosophy of Science 3 (September):S200-S212.
    Many recent cognitive studies reveal that human cognition is inherently perceptual, sharing systems with perception at both the conceptual and the neural levels. This paper introduces Barsalou's theory of perceptual symbols and explores its implications for philosophy of science. If perceptual symbols lie in the heart of conceptual processing, the process of attribute selection during concept representation, which is critical for defining similarity and thus for comparing taxonomies, can no longer be determined solely by background beliefs. The analogous nature of (...)
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  9. Andy Clark & Chris Thornton (1997). Trading Spaces: Computation, Representation, and the Limits of Uninformed Learning. Behavioral and Brain Sciences 20 (1):57-66.
  10. John Coates (1978). Symbol and Structure In. The Chesterton Review 4 (2):246-259.
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  11. John Coates (1978). Symbol and Structure in "The Flying Inn". The Chesterton Review 4 (2):246-259.
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  12. R. Cummins (1986). Inexplicit Representation. In Myles Brand (ed.), The Representation of Knowledge and Belief. Tucson: University of Arizona Press.
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  13. Izydora Dąmbska (1982). Symbol. Studia Semiotyczne 12:125-132.
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  14. Paul de Man (1999). Znak i symbol w estetyce Hegla. Sztuka I Filozofia 16:254.
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  15. Tanya De Villiers, Why Peirce Matters : The Symbol in Deacon’s Symbolic Species.
    The original publication is available at htt://www.sciencedirect.com.
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  16. Fred Dretske (1986). Aspects of Cognitive Representation. In Myles Brand & Robert M. Harnish (eds.), The Representation of Knowledge and Belief. University of Arizona Press.
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  17. M. Teresa Espinal (1992). On the Representation of Linguistic Information. In Jes Ezquerro (ed.), Cognition, Semantics and Philosophy. Kluwer. 75--105.
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  18. J. Fodor & E. Lepore (1996). Churchland on State Space Semantics. In Robert N. McCauley (ed.), The Churchlands and Their Critics. Blackwell Publishers. 145--158.
  19. Michael J. Giordano (1981). Icon and Symbol. Semiotics:29-37.
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  20. Ben Goertzel, Moshe Looks, Ari Heljakka & Cassio Pennachin (2007). Toward a Pragmatic Understanding of the Cognitive Underpinnings of Symbol Grounding. In R. Gudwin & J. Queiroz (eds.), Semiotics and Intelligent Systems Development. Idea Group Inc..
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  21. Stefan Gruner (2013). Eric Winsberg: Science in the Age of Computer Simulation. [REVIEW] Minds and Machines 23 (2):251-254.
  22. Mazen Maurice Guirguis (1998). Peter Novak, Mental Symbols: A Defense of the Classical Theory of Mind Reviewed By. Philosophy in Review 18 (2):139-141.
  23. Stevan Hamad (1993). Grounding Symbols in the Analog World with Neural Nets: A Hybrid Model. Think 2:12-20.
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  24. Stevan Harnad (2003). Symbol‐Grounding Problem. In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group.
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  25. Stevan Harnad (1995). Grounding Symbols in Sensorimotor Categories with Neural Networks. Institute of Electrical Engineers Colloquium on "Grounding Representations.
    It is unlikely that the systematic, compositional properties of formal symbol systems -- i.e., of computation -- play no role at all in cognition. However, it is equally unlikely that cognition is just computation, because of the symbol grounding problem (Harnad 1990): The symbols in a symbol system are systematically interpretable, by external interpreters, as meaning something, and that is a remarkable and powerful property of symbol systems. Cognition (i.e., thinking), has this property too: Our thoughts are systematically interpretable by (...)
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  26. Stevan Harnad (1987). Category Induction and Representation. In [Book Chapter].
    A provisional model is presented in which categorical perception (CP) provides our basic or elementary categories. In acquiring a category we learn to label or identify positive and negative instances from a sample of confusable alternatives. Two kinds of internal representation are built up in this learning by "acquaintance": (1) an iconic representation that subserves our similarity judgments and (2) an analog/digital feature-filter that picks out the invariant information allowing us to categorize the instances correctly. This second, categorical representation is (...)
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  27. Stevan Harnad & Stephen J. Hanson, Learned Categorical Perception in Neural Nets: Implications for Symbol Grounding.
    After people learn to sort objects into categories they see them differently. Members of the same category look more alike and members of different categories look more different. This phenomenon of within-category compression and between-category separation in similarity space is called categorical perception (CP). It is exhibited by human subjects, animals and neural net models. In backpropagation nets trained first to auto-associate 12 stimuli varying along a onedimensional continuum and then to sort them into 3 categories, CP arises as a (...)
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  28. Peter Jackson & Frank Schilder (2005). Natural Language Processing: Overview. In Alex Barber (ed.), Encyclopedia of Language and Linguistics. Elsevier. 2--503.
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  29. Wolfgang Janke (1968). Das Symbol. Philosophisches Jahrbuch 76 (1):164-180.
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  30. Adam Leroy Jones (1905). Ernaer on Die Einfuhlung Und Das Symbol. [REVIEW] Journal of Philosophy 2 (23):639.
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  31. Lucyna Juśkiewicz (2001). Liczba i symbol. Kilka uwag o renesansowym matematyzowaniu uniwersum. Filozofia Nauki 3.
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  32. Jerzy Kmita & Włodzimierz Ławniczak (1970). Znak - symbol - alegoria. Studia Semiotyczne 1:75-108.
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  33. Justyna Kroczak (2010). Ikona jako symbol wieczności. Hybris 13.
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  34. Stan C. Kwasny & Kanaan A. Faisal (1992). Symbolic Parsing Via Subsymbolic Rules. In J. Dinsmore (ed.), The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum. 209--236.
  35. Arthur B. Markman (2002). Knowledge Representation. In J. Wixted & H. Pashler (eds.), Stevens' Handbook of Experimental Psychology. Wiley.
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  36. Norman Myers (1986). A Symbol of Threatened Wildlife. BioScience 36 (10):676-677.
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  37. 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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  38. A. J. O. (1976). Symbol and Interpretation. Review of Metaphysics 29 (3):558-558.
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  39. H. H. Pattee (2008). Physical and Functional Conditions for Symbols, Codes, and Languages. Biosemiotics 1 (2):147-168.
    All sciences have epistemic assumptions, a language for expressing their theories or models, and symbols that reference observables that can be measured. In most sciences the language in which their models are expressed are not the focus of their attention, although the choice of language is often crucial for the model. On the contrary, biosemiotics, by definition, cannot escape focusing on the symbol–matter relationship. Symbol systems first controlled material construction at the origin of life. At this molecular level it is (...)
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  40. John Perry, Richly Grounding Symbols in ASL.
    It was once common to regard ASL as less than a full-fledged language, as a mere combination of miming, pointing and a few primitive gestures. That conception of ASL was laid to rest by William Stokoe’s landmark work [22] and much careful research that has come in its wake. This work..
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  41. 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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  42. 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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  43. Anna Przeclawska (1994). Are Symbols Still the Carriers of Sense? Paideia 17:103.
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  44. Georges Rey (2012). The Turing Thesis Vs. The Turing Test. The Philosophers' Magazine 57 (57):84-89.
  45. Gary E. Schwartz (1996). Symbols and Thought. Synthese 106 (3):399-407.
    No one need deny the importance of language to thought and cognition. At the same time, there is a tendency in studies of mind and mental functioning to assume that properties and principles of linguistic, or language-like, forms of representation must hold of forms of thought and representation in general. Consideration of a wider range of symbol systems shows that this is not so. In turn, various claims and arguments in cognitive theory that depend on assumptions applicable only to linguistic (...)
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  46. Stuart C. Shapiro (2003). Knowledge Representation. In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group.
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  47. 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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  48. 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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  49. Luc Steels (2008). The Symbol Grounding Problem has Been Solved. So What's Next. In Manuel de Vega, Arthur M. Glenberg & Arthur C. Graesser (eds.), Symbols and Embodiment: Debates on Meaning and Cognition. Oxford University Press. 223--244.
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  50. Luc Steels (2002). Grounding Symbols Through Evolutionary Language Games. In A. Cangelosi & D. Parisi (eds.), Simulating the Evolution of Language. Springer-Verlag. 211--226.
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