Search results for 'symbol grounding' (try it on Scholar)

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  1.  57
    Mariarosaria Taddeo & Luciano Floridi (2008). A Praxical Solution of the Symbol Grounding Problem. Minds and Machines 17 (4):369-389.
    This article is the second step in our research into the Symbol Grounding Problem (SGP). In a previous work, we defined the main condition that must be satisfied by any strategy in order to provide a valid solution to the SGP, namely the zero semantic commitment condition (Z condition). We then showed that all the main strategies proposed so far fail to satisfy the Z condition, although they provide several important lessons to be followed by any new proposal. (...)
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  2.  57
    Dairon Rodríguez, Jorge Hermosillo & Bruno Lara (2012). Meaning in Artificial Agents: The Symbol Grounding Problem Revisited. Minds and Machines 22 (1):25-34.
    The Chinese room argument has presented a persistent headache in the search for Artificial Intelligence. Since it first appeared in the literature, various interpretations have been made, attempting to understand the problems posed by this thought experiment. Throughout all this time, some researchers in the Artificial Intelligence community have seen Symbol Grounding as proposed by Harnad as a solution to the Chinese room argument. The main thesis in this paper is that although related, these two issues present different (...)
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  3.  77
    Vincent C. Müller (2009). Symbol Grounding in Computational Systems: A Paradox of Intentions. Minds and Machines 19 (4):529-541.
    The paper presents a paradoxical feature of computational systems that suggests that computationalism cannot explain symbol grounding. If the mind is a digital computer, as computationalism claims, then it can be computing either over meaningful symbols or over meaningless symbols. If it is computing over meaningful symbols its functioning presupposes the existence of meaningful symbols in the system, i.e. it implies semantic nativism. If the mind is computing over meaningless symbols, no intentional cognitive processes are available prior to (...)
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  4.  47
    Vincent C. Müller (2015). Which Symbol Grounding Problem Should We Try to Solve? Journal of Experimental & Theoretical Artificial Intelligence 27 (1):73-78.
    Floridi and Taddeo propose a condition of “zero semantic commitment” for solutions to the grounding problem, and a solution to it. I argue briefly that their condition cannot be fulfilled, not even by their own solution. After a look at Luc Steels' very different competing suggestion, I suggest that we need to re-think what the problem is and what role the ‘goals’ in a system play in formulating the problem. On the basis of a proper understanding of computing, I (...)
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  5. Stevan Harnad (1990). The Symbol Grounding Problem. Philosophical Explorations 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 (...)
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  6.  62
    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 (...)
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  7.  84
    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 (...)
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  8.  18
    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 (...)
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  9.  25
    Evan Thompson (1997). Symbol Grounding: A Bridge From Artificial Life to Artificial Intelligence. Brain and Cognition 34 (1):48-71.
    This paper develops a bridge from AL issues about the symbol–matter relation to AI issues about symbol-grounding by focusing on the concepts of formality and syntactic interpretability. Using the DNA triplet-amino acid specification relation as a paradigm, it is argued that syntactic properties can be grounded as high-level features of the non-syntactic interactions in a physical dynamical system. This argu- ment provides the basis for a rebuttal of John Searle’s recent assertion that syntax is observer-relative (1990, 1992). (...)
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  10.  12
    Karl F. MacDorman (1998). Feature Learning, Multiresolution Analysis, and Symbol Grounding. Behavioral and Brain Sciences 21 (1):32-33.
    Cognitive theories based on a fixed feature set suffer from frame and symbol grounding problems. Flexible features and other empirically acquired constraints (e.g., analog-to-analog mappings) provide a framework for letting extrinsic relations influence symbol manipulation. By offering a biologically plausible basis for feature learning, nonorthogonal multiresolution analysis and dimensionality reduction, informed by functional constraints, may contribute to a solution to the symbol grounding problem.
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  11.  49
    Ron Sun (2000). Symbol Grounding: A New Look at an Old Idea. Philosophical Psychology 13 (2):149-172.
    Symbols should be grounded, as has been argued before. But we insist that they should be grounded not only in subsymbolic activities, but also in the interaction between the agent and the world. The point is that concepts are not formed in isolation (from the world), in abstraction, or "objectively." They are formed in relation to the experience of agents, through their perceptual/motor apparatuses, in their world and linked to their goals and actions. This paper takes a detailed look at (...)
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  12.  77
    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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  13.  14
    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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  14.  60
    Angelo Cangelosi, Alberto Greco & Stevan Harnad (2002). Symbol Grounding and the Symbolic Theft Hypothesis. In A. Cangelosi & D. Parisi (eds.), Simulating the Evolution of Language. Springer-Verlag 191--210.
    Scholars studying the origins and evolution of language are also interested in the general issue of the evolution of cognition. Language is not an isolated capability of the individual, but has intrinsic relationships with many other behavioral, cognitive, and social abilities. By understanding the mechanisms underlying the evolution of linguistic abilities, it is possible to understand the evolution of cognitive abilities. Cognitivism, one of the current approaches in psychology and cognitive science, proposes that symbol systems capture mental phenomena, and (...)
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  15.  15
    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.
  16.  6
    Stephen J. Cowley (2007). How Human Infants Deal with Symbol Grounding. Interaction Studies 8 (1):83-104.
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  17.  6
    Stevan Harnad (2003). SymbolGrounding Problem. In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group
  18.  13
    W. K. Yeap (1993). On Symbol Grounding. Idealistic Studies 23 (2-3):179-185.
  19. T. Belpaeme, S. Cowley & K. F. MacDorman (2007). Symbol Grounding: Special Issue Of. Interaction Studies 8 (1).
     
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  20.  10
    Paul Vogt & Federico Divina (2007). Social Symbol Grounding and Language Evolution. Interaction Studies 8 (1):31-52.
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  21.  15
    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.
  22.  17
    Tony Belpaeme & Stephen J. Cowley (2007). Extending Symbol Grounding. Interaction Studies 8 (1):1-16.
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  23.  7
    W. K. Yeap (1993). On Symbol Grounding. Idealistic Studies 23 (2/3):179-185.
  24.  6
    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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  25. Tony Belpaeme & Stephen J. Cowley (2007). Foreword: Extending Symbol Grounding. Interaction Studiesinteraction Studies Social Behaviour and Communication in Biological and Artificial Systems 8 (1):1-6.
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  26. Stephen J. Cowley (2007). How Human Infants Deal with Symbol Grounding. Interaction Studiesinteraction Studies Social Behaviour and Communication in Biological and Artificial Systems 8 (1):83-104.
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  27. Robert C. Cummins (1996). Why There is No Symbol Grounding Problem? In Representations, Targets, and Attitudes. MIT Press
     
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  28. Christopher Viger (2007). The Acquired Language of Thought Hypothesis: A Theory of Symbol Grounding. Interaction Studiesinteraction Studies Social Behaviour and Communication in Biological and Artificial Systems 8 (1):125-142.
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  29. Paul Vogt & Federico Divina (2007). Social Symbol Grounding and Language Evolution. Interaction Studiesinteraction Studies Social Behaviour and Communication in Biological and Artificial Systems 8 (1):31-52.
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  30.  74
    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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  31.  11
    Luc Steels & Tony Belpaeme (2005). Coordinating Perceptually Grounded Categories Through Language: A Case Study for Colour. Behavioral and Brain Sciences 28 (4):469-489.
    This article proposes a number of models to examine through which mechanisms a population of autonomous agents could arrive at a repertoire of perceptually grounded categories that is sufficiently shared to allow successful communication. The models are inspired by the main approaches to human categorisation being discussed in the literature: nativism, empiricism, and culturalism. Colour is taken as a case study. Although we take no stance on which position is to be accepted as final truth with respect to human categorisation (...)
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  32.  11
    Angelo Cangelosi & Thomas Riga (2006). An Embodied Model for Sensorimotor Grounding and Grounding Transfer: Experiments With Epigenetic Robots. Cognitive Science 30 (4):673-689.
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  33.  30
    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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  34.  52
    Graham White (2011). Bootstrapping Normativity. Philosophy and Technology 24 (1):35-53.
    We compare the role of Cartesian assumptions in the symbol grounding problem and in the Myth of the Given: We argue that the Sellars–McDowell critique of the Myth of the Given and, in particular, its use of the concept of normativity can provide useful resources for responding to the symbol grounding problem. We also describe the concepts of normativity at work in computer science and cognitive science: We argue that normative concepts are pervasive in the sciences (...)
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  35.  44
    Graham White (2011). Descartes Among the Robots. Minds and Machines 21 (2):179-202.
    We consider the symbol grounding problem, and apply to it philosophical arguments against Cartesianism developed by Sellars and McDowell: the problematic issue is the dichotomy between inside and outside which the definition of a physical symbol system presupposes. Surprisingly, one can question this dichotomy and still do symbolic computation: a detailed examination of the hardware and software of serial ports shows this.
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  36.  21
    Serge Thill, Sebastian Padó & Tom Ziemke (2014). On the Importance of a Rich Embodiment in the Grounding of Concepts: Perspectives From Embodied Cognitive Science and Computational Linguistics. Topics in Cognitive Science 6 (3):545-558.
    The recent trend in cognitive robotics experiments on language learning, symbol grounding, and related issues necessarily entails a reduction of sensorimotor aspects from those provided by a human body to those that can be realized in machines, limiting robotic models of symbol grounding in this respect. Here, we argue that there is a need for modeling work in this domain to explicitly take into account the richer human embodiment even for concrete concepts that prima facie relate (...)
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  37.  88
    Stevan Harnad (1991). Other Bodies, Other Minds: A Machine Incarnation of an Old Philosophical Problem. [REVIEW] Minds and Machines 1 (1):43-54.
    Explaining the mind by building machines with minds runs into the other-minds problem: How can we tell whether any body other than our own has a mind when the only way to know is by being the other body? In practice we all use some form of Turing Test: If it can do everything a body with a mind can do such that we can't tell them apart, we have no basis for doubting it (...)
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  38. Stevan Harnad (2011). Lunch Uncertain [Review Of: Floridi, Luciano (2011) The Philosophy of Information (Oxford)]. [REVIEW] Times Literary Supplement 5664 (22-23).
    The usual way to try to ground knowing according to contemporary theory of knowledge is: We know something if (1) it’s true, (2) we believe it, and (3) we believe it for the “right” reasons. Floridi proposes a better way. His grounding is based partly on probability theory, and partly on a question/answer network of verbal and behavioural interactions evolving in time. This is rather like modeling the data-exchange between a data-seeker who needs to know which button to press (...)
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  39.  95
    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 (...)
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  40.  9
    Donald Favareau (2015). Symbols Are Grounded Not in Things, but in Scaffolded Relations and Their Semiotic Constraints. Biosemiotics 8 (2):235-255.
    As the accompanying articles in the Special Issue on Semiotic Scaffolding will attest, my colleagues in biosemiotics have done an exemplary job in showing us how to think about the critically generative role that semiotic scaffolding plays “vertically” – i.e., in evolutionary and developmental terms – by “allowing access to the upper floors” of biological complexity, cognition and evolution.In addition to such diachronic considerations of semiotic scaffolding, I wish to offer here a consideration of semiotic scaffolding’s synchronic power, as well (...)
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  41.  63
    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 (...)
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  42.  90
    Arthur M. Glenberg (1997). What Memory is For. Behavioral and Brain Sciences 20 (1):1-19.
    I address the commentators' calls for clarification of theoretical terms, discussion of similarities to other proposals, and extension of the ideas. In doing so, I keep the focus on the purpose of memory: enabling the organism to make sense of its environment so that it can take action appropriate to constraints resulting from the physical, personal, social, and cultural situations.
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  43. Fred Adams (2010). Embodied Cognition. Phenomenology and the Cognitive Sciences 9 (4):619-628.
    Embodied cognition is sweeping the planet. On a non-embodied approach, the sensory system informs the cognitive system and the motor system does the cognitive system’s bidding. There are causal relations between the systems but the sensory and motor systems are not constitutive of cognition. For embodied views, the relation to the sensori-motor system to cognition is constitutive, not just causal. This paper examines some recent empirical evidence used to support the view that cognition is embodied and raises questions about some (...)
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  44. Julian Kiverstein (2012). The Meaning of Embodiment. Topics in Cognitive Science 4 (4):740-758.
    There is substantial disagreement among philosophers of embodied cognitive science about the meaning of embodiment. In what follows, I describe three different views that can be found in the current literature. I show how this debate centers around the question of whether the science of embodied cognition can retain the computer theory of mind. One view, which I will label body functionalism, takes the body to play the functional role of linking external resources for problem solving with internal biological machinery. (...)
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  45. Vincent C. Müller (2007). Is There a Future for AI Without Representation? Minds and Machines 17 (1):101-115.
    This paper investigates the prospects of Rodney Brooks’ proposal for AI without representation. It turns out that the supposedly characteristic features of “new AI” (embodiment, situatedness, absence of reasoning, and absence of representation) are all present in conventional systems: “New AI” is just like old AI. Brooks proposal boils down to the architectural rejection of central control in intelligent agents—Which, however, turns out to be crucial. Some of more recent cognitive science suggests that we might do well to dispose of (...)
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  46.  32
    Julio Santiago, Marc Ouellet, Antonio Román & Javier Valenzuela (2012). Attentional Factors in Conceptual Congruency. Cognitive Science 36 (6):1051-1077.
    Conceptual congruency effects are biases induced by an irrelevant conceptual dimension of a task (e.g., location in vertical space) on the processing of another, relevant dimension (e.g., judging words’ emotional evaluation). Such effects are a central empirical pillar for recent views about how the mind/brain represents concepts. In the present paper, we show how attentional cueing (both exogenous and endogenous) to each conceptual dimension succeeds in modifying both the manifestation and the symmetry of the effect. The theoretical implications of this (...)
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  47.  16
    S. Harnad (2000). Minds, Machines and Turing. Journal of Logic, Language and Information 9 (4):425-445.
    Turing's celebrated 1950 paper proposes a very generalmethodological criterion for modelling mental function: total functionalequivalence and indistinguishability. His criterion gives rise to ahierarchy of Turing Tests, from subtotal (toy) fragments of ourfunctions (t1), to total symbolic (pen-pal) function (T2 – the standardTuring Test), to total external sensorimotor (robotic) function (T3), tototal internal microfunction (T4), to total indistinguishability inevery empirically discernible respect (T5). This is areverse-engineering hierarchy of (decreasing) empiricalunderdetermination of the theory by the data. Level t1 is clearly toounderdetermined, T2 (...)
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  48.  54
    Fred Adams (2010). Information and Knowledge À la Floridi. Metaphilosophy 41 (3):331-344.
    Abstract: Luciano Floridi has impressively applied the concept of information to problems in semantics and epistemology, among other areas. In this essay, I briefly review two areas where I think one may usefully raise questions about some of Floridi's conclusions. One area is in the project to naturalize semantics and Floridi's use of the derived versus nonderived notion of semantic content. The other area is in the logic of information and knowledge and whether knowledge based on information necessarily supports closure, (...)
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  49.  70
    Bruce J. MacLennan (1993). Grounding Analog Computers. Philosophical Explorations 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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  50.  8
    Angelo Cangelosi (2006). The Grounding and Sharing of Symbols. Pragmatics and Cognition 14 (2):275-286.
    The double function of language, as a social/communicative means, and as an individual/cognitive capability, derives from its fundamental property that allows us to internally re-represent the world we live in. This is possible through the mechanism of symbol grounding, i.e., the ability to associate entities and states in the external and internal world with internal categorical representations. The symbol grounding mechanism, as language, has both an individual and a social component. The individual component, called the “Physical (...)
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