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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 1981 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 2020 offer helpful surveys of the contemporary literature.
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  1. How We ‘Know’ There is No Such Thing as ‘Artificial’ Intelligence.Ilexa Yardley - 2018 - Https://Medium.Com/the-Circular-Theory.
    X and Y is X and X. This is all there is, technically, to, what humans call, 'reality.' Meaning, technically, no such thing as 'reality.' No such thing as information. No such thing as 'anything.' An always-present (naturally conserved) circle has control. Explaining self-referential recursion, magical thinking, quantum entanglement, where all of these 'words' articulate (a non-existent) reality.
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  2. Circularity.Ilexa Yardley - 2022 - Https://Medium.Com/the-Circular-Theory/.
  3. Occam's Razor For Big Data?Birgitta Dresp-Langley - 2019 - Applied Sciences 3065 (9):1-28.
    Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties (...)
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  4. Climbing the Ladder: How Agents Reach Counterfactual Thinking.Caterina Moruzzi - 2022 - Proceedings of the 14th International Conference on Agents and Artificial Intelligence.
  5. From Symbols to Knowledge Systems: A. Newell and H. A. Simon's Contribution to Symbolic AI.Luis M. Augusto - 2021 - Journal of Knowledge Structures and Systems 2 (1):29 - 62.
    A. Newell and H. A. Simon were two of the most influential scientists in the emerging field of artificial intelligence (AI) in the late 1950s through to the early 1990s. This paper reviews their crucial contribution to this field, namely to symbolic AI. This contribution was constituted mostly by their quest for the implementation of general intelligence and (commonsense) knowledge in artificial thinking or reasoning artifacts, a project they shared with many other scientists but that in their case was theoretically (...)
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  6. Universal Tokenization.Ilexa Yardley - 2021 - Https://Medium.Com/the-Circular-Theory/.
  7. Saint Thomas d'Aquin contre les robots. Pistes pour une approche philosophique de l'Intelligence Artificielle.Matthieu Raffray - 2019 - Angelicum 4 (96):553-572.
    In light of the pervasive developments of new technologies, such as NBIC (Nanotechnology, biotechnology, information technology, and cognitive science), it is imperative to produce a coherent and deep reflexion on the human nature, on human intelligence and on the limit of both of them, in order to successfully respond to some technical argumentations that strive to depict humanity as a purely mechanical system. For this purpose, it is interesting to refer to the epistemology and metaphysics of Thomas Aquinas as a (...)
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  8. Why Crypto-Everything is Here to Stay.Ilexa Yardley - 2021 - Https://Medium.Com/the-Circular-Theory/.
    Cryptocurrency is just the tip of a never-melting iceberg…because everything in Nature is connected to everything else by an always-conserved (and uber-simple) circle. Giving us, finally, an explanation (and, technically, a use-case, and proof) for a 'self.'.
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  9. Abstraction: How to Understand It.Ilexa Yardley - 2021 - Https://Medium.Com/the-Circular-Theory/.
  10. What Modern Physicists Are 'Discovering'.Ilexa Yardley - 2021 - Https://Medium.Com/the-Circular-Theory/.
  11. Foucault, Deleuze, and Nietzsche.Ilexa Yardley - 2021 - Https://Medium.Com/the-Circular-Theory/.
  12. Game-Theoretic Robustness in Cooperation and Prejudice Reduction: A Graphic Measure.Patrick Grim - 2006 - In Luis M. Rocha, Larry S. Yaeger, Mark A. Bedau, Dario Floreano & Robert L. Goldstine (eds.), Artificial Life X: Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems. MIT Press. pp. 445-451.
    Talk of ‘robustness’ remains vague, despite the fact that it is clearly an important parameter in evaluating models in general and game-theoretic results in particular. Here we want to make it a bit less vague by offering a graphic measure for a particular kind of robustness— ‘matrix robustness’— using a three dimensional display of the universe of 2 x 2 game theory. In a display of this form, familiar games such as the Prisoner’s Dilemma, Stag Hunt, Chicken and Deadlock appear (...)
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  13. Introduction to CAT4. Part 3. Semantics.Andrew Thomas Holster - manuscript
    CAT4 is proposed as a general method for representing information, enabling a powerful programming method for large-scale information systems. It enables generalised machine learning, software automation and novel AI capabilities. This is Part 3 of a five-part introduction. The focus here is on explaining the semantic model for CAT4. Points in CAT4 graphs represent facts. We introduce all the formal (data) elements used in the classic semantic model: sense or intension (1st and 2nd joins), reference (3rd join), functions (4th join), (...)
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  14. Events and Machine Learning.Augustus Hebblewhite, Jakob Hohwy & Tom Drummond - 2021 - Topics in Cognitive Science 13 (1):243-247.
    Topics in Cognitive Science, Volume 13, Issue 1, Page 243-247, January 2021.
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  15. K některým extravagantním teoriím významu.Filip Tvrdý - 2013 - In Božena Bednaříková & Pavla Hernandezová (eds.), Od slova k modelu jazyka. Olomouc: pp. 343-349.
    Semantics based on representational theories of mind has met challenges recently. Traditional accounts consider meaning as an entity with semantic properties, i.e. a mental object that denotes or represents a real-world object. The paper discusses ways of constructing meaning without representations, as shown in Rapaport’s syntactic semantics and Rosenberg’s eliminative theory of mind and language.
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  16. Updating the Frame Problem for Artificial Intelligence Research.Lisa Miracchi - 2020 - Journal of Artificial Intelligence and Consciousness 7 (2):217-230.
    The Frame Problem is the problem of how one can design a machine to use information so as to behave competently, with respect to the kinds of tasks a genuinely intelligent agent can reliably, effectively perform. I will argue that the way the Frame Problem is standardly interpreted, and so the strategies considered for attempting to solve it, must be updated. We must replace overly simplistic and reductionist assumptions with more sophisticated and plausible ones. In particular, the standard interpretation assumes (...)
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  17. Modal Cognitivism and Modal Expressivism.Hasen Khudairi - manuscript
    This paper aims to provide a mathematically tractable background against which to model both modal cognitivism and modal expressivism. I argue that epistemic modal algebras, endowed with a hyperintensional, topic-sensitive epistemic two-dimensional truthmaker semantics, comprise a materially adequate fragment of the language of thought. I demonstrate, then, how modal expressivism can be regimented by modal coalgebraic automata, to which the above epistemic modal algebras are categorically dual. I examine, in particular, the virtues unique to the modal expressivist approach here proffered (...)
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  18. Modeling Artificial Agents’ Actions in Context – a Deontic Cognitive Event Ontology.Miroslav Vacura - 2020 - Applied Ontology 15 (4):493-527.
    Although there have been efforts to integrate Semantic Web technologies and artificial agents related AI research approaches, they remain relatively isolated from each other. Herein, we introduce a new ontology framework designed to support the knowledge representation of artificial agents’ actions within the context of the actions of other autonomous agents and inspired by standard cognitive architectures. The framework consists of four parts: 1) an event ontology for information pertaining to actions and events; 2) an epistemic ontology containing facts about (...)
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  19. Computational Capacity of Pyramidal Neurons in the Cerebral Cortex.Danko D. Georgiev, Stefan K. Kolev, Eliahu Cohen & James F. Glazebrook - 2020 - Brain Research 1748:147069.
    The electric activities of cortical pyramidal neurons are supported by structurally stable, morphologically complex axo-dendritic trees. Anatomical differences between axons and dendrites in regard to their length or caliber reflect the underlying functional specializations, for input or output of neural information, respectively. For a proper assessment of the computational capacity of pyramidal neurons, we have analyzed an extensive dataset of three-dimensional digital reconstructions from the NeuroMorphoOrg database, and quantified basic dendritic or axonal morphometric measures in different regions and layers of (...)
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  20. Artificial Intelligence: Philosophical and Epistemological Perspectives.Pierre Livet & Franck Varenne - 2020 - In H. Prade, Papini O. & Marquis P. (eds.), A Guided Tour of Artificial Intelligence Research. pp. 437-455.
    Research in artificial intelligence (AI) has led to revise the challenges of the AI initial programme as well as to keep us alert to peculiarities and limitations of human cognition. Both are linked, as a careful further reading of the Turing’s test makes it clear from Searle’s Chinese room apologue and from Dreyfus’ suggestions, and in both cases, ideal had to be turned into operating mode. In order to rise these more pragmatic challenges AI does not hesitate to link together (...)
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  21. Externalism.Robert A. Wilson - 2003 - In Lynn Nadel (ed.), Encyclopedia of Cognitive Science. London: pp. 92-97.
    Introduction to externalism in the philosophy of mind and cognitive science.
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  22. Tacit Representations and Artificial Intelligence: Hidden Lessons From an Embodied Perspective on Cognition.Elena Spitzer - 2016 - In Vincent Müller (ed.), Fundamental Issues of Artificial Intelligence. Springer. pp. 425-441.
    In this paper, I explore how an embodied perspective on cognition might inform research on artificial intelligence. Many embodied cognition theorists object to the central role that representations play on the traditional view of cognition. Based on these objections, it may seem that the lesson from embodied cognition is that AI should abandon representation as a central component of intelligence. However, I argue that the lesson from embodied cognition is actually that AI research should shift its focus from how to (...)
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  23. Integrating Computation Into the Mechanistic Hierarchy in the Cognitive and Neural Sciences.Lotem Elber-Dorozko & Oron Shagrir - 2019 - Synthese 199 (Suppl 1):43-66.
    It is generally accepted that, in the cognitive and neural sciences, there are both computational and mechanistic explanations. We ask how computational explanations can integrate into the mechanistic hierarchy. The problem stems from the fact that implementation and mechanistic relations have different forms. The implementation relation, from the states of an abstract computational system to the physical, implementing states is a homomorphism mapping relation. The mechanistic relation, however, is that of part/whole; the explaining features in a mechanistic explanation are the (...)
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  24. Walking in the Shoes of the Brain: An "Agent" Approach to Phenomenality and the Problem of Consciousness.Dan J. Bruiger - manuscript
    Abstract: Given an embodied evolutionary context, the (conscious) organism creates phenomenality and establishes a first-person point of view with its own agency, through intentional relations made by its own acts of fiat, in the same way that human observers create meaning in language.
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  25. Evolution: The Computer Systems Engineer Designing Minds.Aaron Sloman - 2011 - 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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  26. The Immune Self: Practicing Meaning in Vivo.Yair Neuman - 2012 - 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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  27. Cognitivism About Epistemic Modality.Hasen Khudairi - manuscript
    This paper aims to vindicate the thesis that cognitive computational properties are abstract objects implemented in physical systems. I avail of the equivalence relations countenanced in Homotopy Type Theory, in order to specify an abstraction principle for epistemic intensions. The homotopic abstraction principle for epistemic intensions provides an epistemic conduit into our knowledge of intensions as abstract objects. I examine, then, how intensional functions in Epistemic Modal Algebra are deployed as core models in the philosophy of mind, Bayesian perceptual psychology, (...)
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  28. Epistemic Modality, Mind, and Mathematics.Hasen Khudairi - 2021 - Dissertation, University of St Andrews
    This book concerns the foundations of epistemic modality. I examine the nature of epistemic modality, when the modal operator is interpreted as concerning both apriority and conceivability, as well as states of knowledge and belief. The book demonstrates how epistemic modality relates to the computational theory of mind; metaphysical modality; the types of mathematical modality; to the epistemic status of large cardinal axioms, undecidable propositions, and abstraction principles in the philosophy of mathematics; to the modal profile of rational intuition; and (...)
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  29. On the Analogy Between Cognitive Representation and Truth.Suárez Mauricio & Solé Albert - 2006 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 21 (1):39-48.
    In this paper we claim that the notion of cognitive representation is irreducibly plural. By means of an analogy with the minimalist conception of truth, we show that this pluralism is compatible with a generally deflationary attitude towards representation. We then explore the extent and nature of representational pluralism by discussing the positive and negative analogies between the inferential conception of representation advocated by one of us and the minimalist conception of truth.
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  30. A General Representation for Internal Proportional Cornbinatorial Measurement Systems When the Operation Is Not Necessari!Y Closed.José A. Díez - 1999 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 14 (1):157-178.
    The aim of this paper is to give one kind of internal proportional systems with general representation and without closure and finiteness assumptions. First, we introduce the notions of internal proportional system and of general representation. Second, we briefly review the existing results which motivate our generalization. Third, we present the new systems, characterized by the fact that the linear order induced by the comparison weak order ≥ at the level of equivalence classes is also a weIl order. We prove (...)
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  31. Peter Gärdenfors: The Geometry of Meaning: Semantics Based on Conceptual Spaces: MIT Press, Cambridge, MA, 2014, 360 Pp, $37.00, ISBN 9780262026789.Ramesh Mishra - 2016 - Minds and Machines 26 (3):313-316.
  32. Symbolic Nature of Cognition.Małgorzata Czarnocka - 2016 - Dialogue and Universalism 26 (1):121-136.
    I propose here an image of knowledge based on the concept of symbol: according to it, the relation of representation that constituting cognition is a symbolization. It is postulated that both the representing conceptual model, i.e. a pre-linguistic entity acquired in cognition, and the true sentence it generates are of symbolic and not of mirroring character. The symbolic nature of cognition carries dialectical tension. We have at our disposal conceptual models and true sentences which symbolically represent reality. However, it is (...)
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  33. On the Symbols of Abbreviations for -Tur.Edward Kennard Rand - 1927 - Speculum 2 (1):52-65.
  34. Multiple-Interval-Dependent Robust Stability Analysis for Uncertain Stochastic Neural Networks with Mixed-Delays.Jianwei Xia, Ju H. Park & Hao Shen - 2016 - Complexity 21 (1):147-162.
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  35. A Symbolic-Connectionist Theory of Relational Inference and Generalization.John E. Hummel & Keith J. Holyoak - 2003 - Psychological Review 110 (2):220-264.
  36. Formal Representations of Orbits.Robert J. Rovetto -
  37. Homunkulismus in den Kognitionswissenschaften.Geert Keil - 2003 - In Wolfgang R. Köhler & Hans-Dieter Mutschler (eds.), Ist der Geist berechenbar? Wissenschaftliche Buchgesellschaft. pp. 77-112.
    1. Was ist ein Homunkulus-Fehlschluß? 2. Analyse des Mentalen und Naturalisierung der Intentionalität 3. Homunkulismus in Theorien der visuellen Wahrnehmung 4. Homunkulismus und Repräsentationalismus 5. Der homunkulare Funktionalismus 6. Philosophische Sinnkritik und empirische Wissenschaft Literatur .
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  38. 24. Symbols.Jean O'Grady - 2000 - In Northrop Frye on Religion. University of Toronto Press. pp. 287-289.
  39. Principles of Knowledge Representation.Gerhard Brewka - 1996 - Center for the Study of Language and Inf.
    The book contains a collection of eight survey papers written by some of the most excellent researchers in foundations of knowledge representation and reasoning. It covers topics like theories of uncertainty, nonmonotonic and causal reasoning, logic programming, abduction, inductive logic programming, description logics, complexity in Artificial Intelligence, and model based diagnosis. It thus provides an up-to-date coverage of recent approaches to some of the most challenging problems underlying knowledge representation and Artificial Intelligence in general.
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  40. Argumenty kondukcyjne.Marcin Selinger - 2014 - Studia Philosophica Wratislaviensia 9 (4):53-63.
    The term "conduction" introduced by Wellman in 1971 is almost absent in the Polish literature on arguments. Contemporarily, conductive arguments are mostly understood as pro and contra arguments, which consist not only of normal pro-premises supporting a conclusion, but also of contra-premises (exceptions) denying it. We explain why such an interpretation seems to be attractive from the logical point of view, and we propose a formal method of representing conductive arguments and calculating the acceptability of their conclusions. The method allows (...)
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  41. New Developments in the Philosophy of AI.Vincent C. Müller - 2016 - In Vincent Müller (ed.), 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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  42. Pancomputationalism: Theory or Metaphor?Vincent C. Müller - 2014 - In Ruth Hagengruber & Uwe Riss (eds.), Philosophy, computing and information science. Pickering & Chattoo. pp. 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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  43. Philosophy and Theory of Artificial Intelligence, 3–4 October (Report on PT-AI 2011).Vincent C. Müller - 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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  44. The Hard and Easy Grounding Problems (Comment on A. Cangelosi).Vincent C. Müller - 2011 - 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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  45. On the Metaphysical Meaning of Major Religious Symbols for a Globalized World.U. A. Vinay Kumar - 2015 - AI and Society 30 (2):147-165.
  46. Embodied Cognition and the Orwell’s Problem in Cognitive Science.V. Hari Narayanan - 2015 - AI and Society 30 (2):193-197.
    Embodied approach to cognition has taken roots in cognitive studies with developments in diverse fields such as robotics, artificial life and cognitive linguistics. Taking cue from the metaphor of a Watt governor, this approach stresses on the coupling between the organism and the environment and the continuous nature of the cognitive processes. This results in questioning the viability of computational–representational understanding of mind as a comprehensive theory of cognition. The paper, after giving an overview of embodied approach based on some (...)
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  47. Commentary On: Douglas Walton and Thomas F. Gordon's "How to Formalize Informal Logic".Marcin Koszowy & Marcin Selinger - 2013 - Virtues of Argumentation. Proceedings of the 10th International Conference of the Ontario Society for the Study of Argumentation (OSSA), 22-26 May 2013, Windsor:1-7.
  48. Knowledge Representation and Metaphor.E. Cornell Way - 1991 - Springer Verlag.
    This series will include monographs and collections of studies devoted to the investigation and exploration of knowledge, information, and data processing systems of all kinds, no matter whether human, animal, or machine. Its scope is intended to span the full range of interests from classical problems in the philosophy of mind and philosophical psychol ogy through issues in cognitive psychology and sociobiology to ideas related to artificial intelligence and computer science. While primary emphasis will be placed upon theoretical, conceptual, and (...)
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  49. The Tao of Symbols.James N. Powell - 1982 - Harper Perennial.
    Argues that poetry, prayer, meditation, and questioning can be used to go beyond the symbols of single culture and find deeper meaning.
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  50. Symbol and Self: A Heuristic Journey Orbiting Symbols and Transformative Symbol Systems.Robert Shamms Mortier - 1988 - 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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