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  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. Dr Michael L. Anderson (2003). Representations, Symbols and Embodiment. .
    Response to "Embodied artificial intelligence", a commentary by Ron Chrisley.
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  3. Michael L. Anderson & Donald R. Perlis (2002). Symbol Systems. In L. Nagel (ed.), Encyclopedia of Cognitive Science. Macmillan.
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  4. 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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  5. 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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  6. 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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  7. 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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  8. 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.
  9. 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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  10. 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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  11. Warren Breckman (2012). Lefort and the Symbolic Dimension. Constellations 19 (1):30-36.
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  12. 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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  13. 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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  14. 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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  15. 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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  16. 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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  17. A. Clark & Ronald Lutz (eds.) (1992). Connectionism in Context. Springer-Verlag.
  18. 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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  19. 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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  20. Robert C. Cummins (1996). Why There is No Symbol Grounding Problem? In Representations, Targets, and Attitudes. MIT Press.
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  21. Robert C. Cummins (1996). Representations, Targets, and Attitudes. MIT Press.
    "This is an important new Cummins work.
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  22. 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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  23. 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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  24. 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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  25. 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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  26. 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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  27. 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.
  28. Miles Groth (1995). Psychology and Nihilism. A Genealogical Critique of the Computational Model of Mind. Review of Metaphysics 48 (4):894-895.
  29. 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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  30. 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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  31. 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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  32. 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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  33. 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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  34. 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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  35. 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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  36. 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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  37. Terence E. Horgan (1992). From Cognitive Science to Folk Psychology: Computation, Mental Representation, and Belief. Philosophy and Phenomenological Research 52 (2):449-484.
  38. 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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  39. 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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  40. Bernard W. Kobes (1990). Individualism and Artificial Intelligence. Philosophical Perspectives 4:429-56.
  41. Stephen M. Kosslyn & Gary Hatfield (1984). Representation Without Symbol Systems. Social Research 51:1019-1045.
  42. Alexandre Linhares (2005). An Active Symbols Theory of Chess Intuition. Minds and Machines 15 (2):131-181.
    The well-known game of chess has traditionally been modeled in artificial intelligence studies by search engines with advanced pruning techniques. The models were thus centered on an inference engine manipulating passive symbols in the form of tokens. It is beyond doubt, however, that human players do not carry out such processes. Instead, chess masters instead carry out perceptual processes, carefully categorizing the chunks perceived in a position and gradually building complex dynamic structures to represent the subtle pressures embedded in the (...)
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  43. Max M. Louwerse (2011). Symbol Interdependency in Symbolic and Embodied Cognition. Topics in Cognitive Science 3 (2):273-302.
    Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to (...)
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  44. David Lumsden (2005). How Can a Symbol System Come Into Being? Dialogue 44 (1):87-96.
    One holistic thesis about symbols is that a symbol cannot exist singly, but only as apart of a symbol system. There is also the plausible view that symbol systems emerge gradually in an individual, in a group, and in a species. The problem is that symbol holism makes it hard to see how a symbol system can emerge gradually, at least if we are considering the emergence of a first symbol system. The only way it seems possible is if being (...)
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  45. Karl F. MacDorman (1997). How to Ground Symbols Adaptively. In S. O'Nuillain, Paul McKevitt & E. MacAogain (eds.), Two Sciences of Mind. John Benjamins. 9--135.
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  46. Bruce J. MacLennan (1993). Grounding Analog Computers. Think 2:8-51.
    In this commentary on Harnad's "Grounding Symbols in the Analog World with Neural Nets: A Hybrid Model," the issues of symbol grounding and analog (continuous) computation are separated, it is argued that symbol graounding is as important an issue for analog cognitive models as for digital (discrete) models. The similarities and differences between continuous and discrete computation are discussed, as well as the grounding of continuous representations. A continuous analog of the Chinese Room is presented.
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  47. Arthur B. Markman & Eric Dietrich (2000). Extending the Classical View of Representation. Trends in Cognitive Sciences 4 (12):470-475.
    Representation is a central part of models in cognitive science, but recently this idea has come under attack. Researchers advocating perceptual symbol systems, situated action, embodied cognition, and dynamical systems have argued against central assumptions of the classical representational approach to mind. We review the core assumptions of the dominant view of representation and the four suggested alternatives. We argue that representation should remain a core part of cognitive science, but that the insights from these alternative approaches must be incorporated (...)
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  48. Arthur B. Markman & Eric Dietrich (1999). Whither Structured Representation? Behavioral and Brain Sciences 22 (4):626-627.
    The perceptual symbol system view assumes that perceptual representations have a role-argument structure. A role-argument structure is often incorporated into amodal symbol systems in order to explain conceptual functions like abstraction and rule use. The power of perceptual symbol systems to support conceptual functions is likewise rooted in its use of structure. On Barsalou's account, this capacity to use structure (in the form of frames) must be innate.
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  49. Lorraine McCune (1999). Development, Consciousness, and the Perception/Mental Representation Distinction. Behavioral and Brain Sciences 22 (4):627-628.
    Perceptual symbol systems provide a welcome alternative to amodal encapsulated means of cognitive processing. However, the relations between perceived reality and internal mentation require a more differentiated approach, reflecting both developmental differences between infant and adult experience and qualitative differences between consciously perceived and mentally represented contents. Neurological evidence suggests a developmental trajectory from initial perceptual states in infancy to a more differentiated consciousness from two years of age on. Children's processing of and verbal expressions regarding motion events provides an (...)
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  50. Alex McLean (2010). Unifying Conceptual Spaces: Concept Formation in Musical Creative Systems. [REVIEW] Minds and Machines 20 (4):503-532.
    We examine Gärdenfors’ theory of conceptual spaces, a geometrical form of knowledge representation (Conceptual spaces: The geometry of thought, MIT Press, Cambridge, 2000 ), in the context of the general Creative Systems Framework introduced by Wiggins (J Knowl Based Syst 19(7):449–458, 2006a ; New Generation Comput 24(3):209–222, 2006 b ). Gärdenfors’ theory offers a way of bridging the traditional divide between symbolic and sub-symbolic representations, as well as the gap between representational formalism and meaning as perceived by human minds. We (...)
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