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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. Louis Dessagne (1938). Essai de représentation concrète du processus physiologique de l'intelligence. Revue Philosophique de la France Et de l'Etranger 126 (9/10):129 - 160.
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  2. 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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  3. Adam Leroy Jones (1905). Ernaer on Die Einfuhlung Und Das Symbol. [REVIEW] Journal of Philosophy 2 (23):639.
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  4. Lucyna Juśkiewicz (2001). Liczba i symbol. Kilka uwag o renesansowym matematyzowaniu uniwersum. Filozofia Nauki 3.
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  5. Douglas Kelly (1972). Symbol Und Mythus Im Altfranzösischen Rolandslied. [REVIEW] Speculum 47 (1):142-144.
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  6. 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.
  7. Robert Shamms Mortier (1988). Symbol and Self: A Heuristic Journey Orbiting Symbols and Transformative Symbol Systems. Dissertation, The Union for Experimenting Colleges and Universities
    This work investigates symbols and transformative symbol systems from a variety of angles and philosophical/religious viewpoints. Discourses on the idea and term of the symbol are defined and integrated with cultural, philosophical, and historical time-frames begin the inquiry. This is carried into an investigation of both the original and essential qualities involved, and an exploration of the purposes and intentionalities of symbolic perception. Throughout the work, a secondary theme is that of occultic and messianic connections and undertones, and speculations are (...)
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  8. Vincent C. Müller (2016). New Developments in the Philosophy of AI. In Fundamental Issues of Artificial Intelligence. Springer
    The philosophy of AI has seen some changes, in particular: 1) AI moves away from cognitive science, and 2) the long term risks of AI now appear to be a worthy concern. In this context, the classical central concerns – such as the relation of cognition and computation, embodiment, intelligence & rationality, and information – will regain urgency.
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  9. Vincent C. Müller (ed.) (2016). Fundamental Issues of Artificial Intelligence. Springer.
    PT-AI 2013: This volume offers a look at the fundamental issues of present and future AI, especially from cognitive science, computer science, neuroscience and philosophy. This work examines the conditions for artificial intelligence, how these relate to the conditions for intelligence in humans and other natural agents, as well as ethical and societal problems that artificial intelligence raises or will raise. The key issues this volume investigates include the relation of AI and cognitive science, ethics of AI and robotics, brain (...)
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  10. Vincent C. Müller (2014). Pancomputationalism: Theory or Metaphor? In Ruth Hagengruber & Uwe Riss (eds.), Philosophy, computing and information science. Pickering & Chattoo 213-221.
    The theory that all processes in the universe are computational is attractive in its promise to provide an understandable theory of everything. I want to suggest here that this pancomputationalism is not sufficiently clear on which problem it is trying to solve, and how. I propose two interpretations of pancomputationalism as a theory: I) the world is a computer and II) the world can be described as a computer. The first implies a thesis of supervenience of the physical over computation (...)
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  11. Vincent C. Müller (ed.) (2013). Philosophy and Theory of Artificial Intelligence. Springer.
    Can we make machines that think and act like humans or other natural intelligent agents? The answer to this question depends on how we see ourselves and how we see the machines in question. Classical AI and cognitive science had claimed that cognition is computation, and can thus be reproduced on other computing machines, possibly surpassing the abilities of human intelligence. This consensus has now come under threat and the agenda for the philosophy and theory of AI must be set (...)
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  12. Vincent C. Müller (2011). Philosophy and Theory of Artificial Intelligence, 3–4 October (Report on PT-AI 2011). The Reasoner 5 (11):192-193.
    Report for "The Reasoner" on the conference "Philosophy and Theory of Artificial Intelligence", 3 & 4 October 2011, Thessaloniki, Anatolia College/ACT, http://www.pt-ai.org. --- Organization: Vincent C. Müller, Professor of Philosophy at ACT & James Martin Fellow, Oxford http://www.sophia.de --- Sponsors: EUCogII, Oxford-FutureTech, AAAI, ACM-SIGART, IACAP, ECCAI.
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  13. Vincent C. Müller (2011). The Hard and Easy Grounding Problems (Comment on A. Cangelosi). International Journal of Signs and Semiotic Systems 1 (1):70-70.
    I see four symbol grounding problems: 1) How can a purely computational mind acquire meaningful symbols? 2) How can we get a computational robot to show the right linguistic behavior? These two are misleading. I suggest an 'easy' and a 'hard' problem: 3) How can we explain and re-produce the behavioral ability and function of meaning in artificial computational agents?4) How does physics give rise to meaning?
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  14. Yair Neuman (2012). The Immune Self: Practicing Meaning in Vivo. Avant: Trends in Interdisciplinary Studies 3 (1):55-62.
    The immune self is our reified way to describe the processes through which the immune system maintains the differentiated identity of the organism and itself. This is an interpretative process, and to study it in a scientifically constructive way we should merge a long hermeneutical tradition asking questions about the nature of interpretation, together with modern understanding of the immune system, emerging sensing technologies and advanced computational tools for analyzing the sensors' data.
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  15. Antoni J. Nowak (1971). The Symbol as Seen by Igor A. Caruso. Roczniki Filozoficzne 19 (4):175.
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  16. James Newton Powell (1982). The Tao of Symbols.
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  17. Athanassios Raftopoulos & Vincent C. Müller (2006). The Phenomenal Content of Experience. Mind and Language 21 (2):187-219.
    We discuss at some length evidence from the cognitive science suggesting that the representations of objects based on spatiotemporal information and featural information retrieved bottomup from a visual scene precede representations of objects that include conceptual information. We argue that a distinction can be drawn between representations with conceptual and nonconceptual content. The distinction is based on perceptual mechanisms that retrieve information in conceptually unmediated ways. The representational contents of the states induced by these mechanisms that are available to a (...)
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  18. Prof Andreas Schierwagen, On Reverse Engineering in the Cognitive and Brain Sciences.
    Various research initiatives try to utilize the operational principles of organisms and brains to develop alternative, biologically inspired computing paradigms and artificial cognitive systems. This article reviews key features of the standard method applied to complexity in the cognitive and brain sciences, i.e. decompositional analysis or reverse engineering. The indisputable complexity of brain and mind raise the issue of whether they can be understood by applying the standard method. Actually, recent findings in the experimental and theoretical fields, question central assumptions (...)
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  19. Valerie L. Shalin (2000). Mark H. Bickhard and Loren Terveen, Foundational Issues in Artificial Intelligence and Cognitive Science: Impasse and Solution, Advances in Psychology, Vol. 109. [REVIEW] Minds and Machines 10 (3):435-439.
  20. Stuart C. Shapiro (2003). Knowledge Representation. In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group
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  21. Aaron Sloman (2011). Evolution: The Computer Systems Engineer Designing Minds. Avant: Trends in Interdisciplinary Studies 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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  22. 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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  23. Elvira Spirova (2013). The Symbol as an Anthropological Concept. Russian Studies in Philosophy 52 (2):46-60.
    A great deal of literature on the symbol approached this notion from epistemological, ontological, and hermeneutic perspectives. This article examines the symbol as an important category of philosophical anthropology that sheds light on the issue of man's origins and culture.
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  24. 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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  25. 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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  26. Mariarosaria Taddeo & Luciano Floridi, Solving the Symbol Grounding Problem: A Critical Review of Fifteen Years of Research.
    This article reviews eight proposed strategies for solving the Symbol Grounding Problem (SGP), which was given its classic formulation in Harnad (1990). After a concise introduction, we provide an analysis of the requirement that must be satisfied by any hypothesis seeking to solve the SGP, the zero semantical commitment condition. We then use it to assess the eight strategies, which are organised into three main approaches: representationalism, semi-representationalism and non-representationalism. The conclusion is that all the strategies are semantically committed and (...)
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  27. Rene Thom (forthcoming). From the Icon to the Symbol. Semiotics: An Introductory Anthology.
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  28. Wilbur Urban (1908). Ernaer on Das Asthetische Symbol. [REVIEW] Journal of Philosophy 5 (6):164.
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  29. Antoine Vergote (1960). The Symbol. Philosophy Today 4 (1):53-72.
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  30. Paul Vogt (2006). Language Evolution and Robotics: Issues on Symbol Grounding. In A. Loula, R. Gudwin & J. Queiroz (eds.), Artificial Cognition Systems. Idea Group Publishers 176.
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  31. Paul Vogt (2002). The Physical Symbol Grounding Problem. Philosophical Explorations.
    This paper presents an approach to solve the symbol grounding problem within the framework of embodied cognitive science. It will be argued that symbolic structures can be used within the paradigm of embodied cognitive science by adopting an alternative definition of a symbol. In this alternative definition, the symbol may be viewed as a structural coupling between an agent's sensorimotor activations and its environment. A robotic experiment is presented in which mobile robots develop a (...)
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  32. Eileen Cornell Way (1991). Knowledge Representation and Metaphor.
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  33. C. H. Whiteley (1960). Truth and Symbol. Philosophical Books 1 (2):13-14.
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  34. Kurt Nieder Wimmer (1962). Kerygmatisches Symbol und Analyse. Archive for the Psychology of Religion 7 (1):203-223.
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  35. Anna Wolińska (2004). "Symbol jako" eksplozja znaczeń". Estetyka I Krytyka 1 (6).
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  36. W. K. Yeap (1993). On Symbol Grounding. Idealistic Studies 23 (2/3):179-185.
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  37. W. K. Yeap (1993). On Symbol Grounding. Idealistic Studies 23 (2-3):179-185.
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  38. Mitbericht zu Scherings Vortrag (1927). Symbol in der Musik. Zeitschrift für Ästhetik Und Allgemeine Kunstwissenschaft 21:3.
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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 (...)
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  2. Evandro Agazzi (1981). Intentionality and Artificial Intelligence. Epistemologia 4:195.
  3. Dr Michael L. Anderson (2003). Representations, Symbols and Embodiment. Philosophical Explorations.
    Response to "Embodied artificial intelligence", a commentary by Ron Chrisley.
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  4. Michael L. Anderson & Donald R. Perlis (2002). Symbol Systems. In L. Nagel (ed.), Encyclopedia of Cognitive Science. Macmillan
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  5. Edward G. Armstrong (1994). From Symbol to Simulacrum. Semiotics:3-9.
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  6. Din Aslamazishvili (2008). Structure of Symbol Within Cultural Transitions. Proceedings of the Xxii World Congress of Philosophy 12:3-7.
    Among such social-philosophic notions as society, culture, civilization, system, human, sense, sign, truth and others, concept “symbol” takes a special place. Most of the researchers meet the view, that symbol possesses an important place in the development of culture as a social phenomenon. The role of symbol in cultures birth and development is characterized by antipathy and polysemy. However revelation of the symbol role in spiritual processes of cultural transitions is beyond question one of the urgent philosophic issues. Symbol is (...)
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  7. L. K. B. (1957). Metafisica de la Expresion. Review of Metaphysics 11 (2):350-350.
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  8. N. D. B. (1961). Formal Representation of Intentionally Structured Systems. Review of Metaphysics 15 (1):195-195.
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  9. 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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  10. 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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  11. 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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  12. Istvan S. N. Berkeley (2008). What the 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 (...)
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