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  1. Jarmo J. Ahonen (1994). On Qualitative Modelling. AI and Society 8 (1):17-28.
    Fundamental assumptions behind qualitative modelling are critically considered and some inherent problems in that modelling approach are outlined. The problems outlined are due to the assumption that a sufficient set of symbols representing the fundamental features of the physical world exists. That assumption causes serious problems when modelling continuous systems. An alternative for intelligent system building for cases not suitable for qualitative modelling is proposed. The proposed alternative combines neural networks and quantitative modelling.
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  2. John A. Barnden & Kankanahalli Srinivas (1996). Quantification Without Variables in Connectionism. Minds and Machines 6 (2):173-201.
    Connectionist attention to variables has been too restricted in two ways. First, it has not exploited certain ways of doing without variables in the symbolic arena. One variable-avoidance method, that of logical combinators, is particularly well established there. Secondly, the attention has been largely restricted to variables in long-term rules embodied in connection weight patterns. However, short-lived bodies of information, such as sentence interpretations or inference products, may involve quantification. Therefore short-lived activation patterns may need to achieve the effect of (...)
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  3. William Bechtel (1993). Currents in Connectionism. Minds and Machines 3 (2):125-153.
    This paper reviews four significant advances on the feedforward architecture that has dominated discussions of connectionism. The first involves introducing modularity into networks by employing procedures whereby different networks learn to perform different components of a task, and a Gating Network determines which network is best equiped to respond to a given input. The second consists in the use of recurrent inputs whereby information from a previous cycle of processing is made available on later cycles. The third development involves developing (...)
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  4. William P. Bechtel (1994). Natural Deduction in Connectionist Systems. Synthese 101 (3):433-463.
    The relation between logic and thought has long been controversial, but has recently influenced theorizing about the nature of mental processes in cognitive science. One prominent tradition argues that to explain the systematicity of thought we must posit syntactically structured representations inside the cognitive system which can be operated upon by structure sensitive rules similar to those employed in systems of natural deduction. I have argued elsewhere that the systematicity of human thought might better be explained as resulting from the (...)
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  5. 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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  6. Margaret A. Boden (ed.) (1990). The Philosophy of AI. Oxford University Press.
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  7. Denny Borsboom & Ingmar Visser (2008). Semantic Cognition or Data Mining? Behavioral and Brain Sciences 31 (6):714-715.
    We argue that neural networks for semantic cognition, as proposed by Rogers & McClelland (R&M), do not acquire semantics and therefore cannot be the basis for a theory of semantic cognition. The reason is that the neural networks simply perform statistical categorization procedures, and these do not require any semantics for their successful operation. We conclude that this has severe consequences for the semantic cognition views of R&M.
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  8. Jeffrey S. Bowers (2000). Further Arguments in Support of Localist Coding in Connectionist Networks. Behavioral and Brain Sciences 23 (4):471-471.
    Two additional sources of evidence are provided in support of localist coding within connectionist networks. First, only models with localist codes can currently represent multiple pieces of information simultaneously or represent order among a set of items on-line. Second, recent priming data appear problematic for theories that rely on distributed representations. However, a faulty argument advanced by Page is also pointed out.
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  9. Keith Butler (1995). Representation and Computation in a Deflationary Assessment of Connectionist Cognitive Science. Synthese 104 (1):71-97.
    Connectionism provides hope for unifying work in neuroscience, computer science, and cognitive psychology. This promise has met with some resistance from Classical Computionalists, which may have inspired Connectionists to retaliate with bold, inflationary claims on behalf of Connectionist models. This paper demonstrates, by examining three intimately connected issues, that these inflationary claims made on behalf of Connectionism are wrong. This should not be construed as an attack on Connectionism, however, since the inflated claims made on its behalf have the look (...)
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  10. Francisco Calvo Garzón (2003). Connectionist Semantics and the Collateral Information Challenge. Mind and Language 18 (1):77-94.
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  11. Francisco Calvo Garzón (2000). A Connectionist Defence of the Inscrutability Thesis. Mind and Language 15 (5):465-480.
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  12. Francisco Calvo Garzón (2000). State Space Semantics and Conceptual Similarity: Reply to Churchland. Philosophical Psychology 13 (1):77-95.
    Jerry Fodor and Ernest Lepore [(1992) Holism: a shopper's guide, Oxford: Blackwell; (1996) in R. McCauley (Ed.) The Churchlands and their critics , Cambridge: Blackwell] have launched a powerful attack against Paul Churchland's connectionist theory of semantics--also known as state space semantics. In one part of their attack, Fodor and Lepore argue that the architectural and functional idiosyncrasies of connectionist networks preclude us from articulating a notion of conceptual similarity applicable to state space semantics. Aarre Laakso and Gary Cottrell [(1998) (...)
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  13. F. P. Cilliers (1991). Rules and Relations: Some Connectionist Implications for Cognitive Science and Language. South African Journal of Philosophy 49 (May):49-55.
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  14. Andy Clark, Connectionism, Nonconceptual Content, and Representational Redescription.
  15. Andy Clark (1993). Associative Engines: Connectionism, Concepts, and Representational Change. MIT Press.
    As Ruben notes, the macrostrategy can allow that the distinction may also be drawn at some micro level, but it insists that descent to the micro level is ...
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  16. Andy Clark & Annette Karmiloff-Smith (1994). The Cognizer's Innards: A Psychological and Philosophical Perspective on the Development of Thought. Mind and Language 8 (4):487-519.
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  17. Andy Clark & Chris Thornton, Trading Spaces: Connectionism and the Limits of Uninformed Learning.
    It is widely appreciated that the difficulty of a particluar computation varies according to how the input data are presented. What is less understood is the effect of this computation/representation tradeoff within familiar learning paradigms. We argue that existing learning algoritms are often poorly equipped to solve problems involving a certain type of important and widespread regularity, which we call 'type-2' regularity. The solution in these cases is to trade achieved representation against computational search. We investigate several ways in which (...)
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  18. Andy Clark & S. Thornton (1997). Trading Spaces: Computation, Representation, and the Limits of Uninformed Learning. Behavioral and Brain Sciences 20 (1):57-66.
    Some regularities enjoy only an attenuated existence in a body of training data. These are regularities whose statistical visibility depends on some systematic recoding of the data. The space of possible recodings is, however, infinitely large type-2 problems. they are standardly solved! This presents a puzzle. How, given the statistical intractability of these type-2 cases, does nature turn the trick? One answer, which we do not pursue, is to suppose that evolution gifts us with exactly the right set of recoding (...)
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  19. Robert C. Cummins (1991). The Role of Representation in Connectionist Explanation of Cognitive Capacities. In William Ramsey, Stephen P. Stich & D. Rumelhart (eds.), Philosophy and Connectionist Theory. Lawrence Erlbaum. 91--114.
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  20. Adrian Cussins (1990). The Connectionist Construction of Concepts. In Margaret A. Boden (ed.), The Philosophy of Ai. Oxford University Press.
    The character of computational modelling of cognition depends on an underlying theory of representation. Classical cognitive science has exploited the syntax/semantics theory of representation that derives from logic. But this has had the consequence that the kind of psychological explanation supported by classical cognitive science is
    _conceptualist_:
    psychological phenomena are modelled in terms of relations that hold between concepts, and between the sensors/effectors and concepts. This kind of explanation is inappropriate for the Proper Treatment of Connectionism (Smolensky 1988).
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  21. G. Dorffner (ed.) (1990). Konnektionismus in Artificial Intelligence Und Kognitionsforschung. Berlin: Springer-Verlag.
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  22. Chris Eliasmith, Structure Without Symbols: Providing a Distributed Account of High-Level Cognition.
    There has been a long-standing debate between symbolicists and connectionists concerning the nature of representation used by human cognizers. In general, symbolicist commitments have allowed them to provide superior models of high-level cognitive function. In contrast, connectionist distributed representations are preferred for providing a description of low-level cognition. The development of Holographic Reduced Representations (HRRs) has opened the possibility of one representational medium unifying both low-level and high-level descriptions of cognition. This paper describes the relative strengths and weaknesses of symbolic (...)
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  23. Norman Fenton, Martin Neil & David A. Lagnado (2013). A General Structure for Legal Arguments About Evidence Using Bayesian Networks. Cognitive Science 37 (1):61-102.
    A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments, there is no systematic, repeatable method for modeling legal arguments as BNs. Hence, where (...)
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  24. Francisco Calvo Garzon (2000). A Connectionist Defence of the Inscrutability Thesis. Mind and Language 15 (5):465-480.
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  25. Christopher Gauker (2007). A Critique of the Similarity Space Theory of Concepts. Mind and Language 22 (4):317–345.
    A similarity space is a hyperspace in which the dimensions represent various dimensions on which objects may differ. The similarity space theory of concepts is the thesis that concepts are regions of similarity spaces that are somehow realized in the brain. Proponents of such a theory of concepts include Paul Churchland and Peter Gärdenfors. This paper argues that the similarity space theory of concepts is mistaken because regions of similarity spaces cannot serve as the components of judgments. It emerges that (...)
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  26. Grant R. Gillett (1989). Representations and Cognitive Science. Inquiry 32 (September):261-77.
    ?Representation? is a concept which occurs both in cognitive science and philosophy. It has common features in both settings in that it concerns the explanation of behaviour in terms of the way the subject categorizes and systematizes responses to its environment. The prevailing model sees representations as causally structured entities correlated on the one hand with elements in a natural language and on the other with clearly identifiable items in the world. This leads to an analysis of representation and cognition (...)
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  27. T. Goschke & Dirk Koppelberg (1991). The Concept of Representation and the Representation of Concepts in Connectionist Models. In William Ramsey, Stephen P. Stich & D. Rumelhart (eds.), Philosophy and Connectionist Theory. Lawrence Erlbaum. 129--161.
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  28. T. Goschke & Dirk Koppelberg (1990). Connectionism and the Semantic Content of Internal Representation. Review of International Philosophy 44 (172):87-103.
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  29. Robert F. Hadley (2004). On the Proper Treatment of Semantic Systematicity. Minds and Machines 14 (2):145-172.
    The past decade has witnessed the emergence of a novel stance on semantic representation, and its relationship to context sensitivity. Connectionist-minded philosophers, including Clark and van Gelder, have espoused the merits of viewing hidden-layer, context-sensitive representations as possessing semantic content, where this content is partially revealed via the representations'' position in vector space. In recent work, Bodén and Niklasson have incorporated a variant of this view of semantics within their conception of semantic systematicity. Moreover, Bodén and Niklasson contend that they (...)
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  30. Stevan Harnad (1993). Grounding Symbols in the Analog World with Neural Nets. Philosophical Explorations 2 (1):12-78.
    Harnad's main argument can be roughly summarised as follows: due to Searle's Chinese Room argument, symbol systems by themselves are insufficient to exhibit cognition, because the symbols are not grounded in the real world, hence without meaning. However, a symbol system that is connected to the real world through transducers receiving sensory data, with neural nets translating these data into sensory categories, would not be subject to the Chinese Room argument. Harnad's article is not only the starting point for the (...)
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  31. W. F. G. Haselager (1999). On the Potential of Non-Classical Constituency. Acta Analytica 22 (22):23-42.
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  32. Gary Hatfield (1991). Representation and Rule-Instantiation in Connectionist Systems. In Terence E. Horgan & John L. Tienson (eds.), Connectionism and the Philosophy of Mind. Kluwer.
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  33. Gary Hatfield (1991). Representation in Perception and Cognition: Connectionist Affordances. In William Ramsey, Stephen P. Stich & D. Rumelhart (eds.), Philosophy and Connectionist Theory. Lawrence Erlbaum. 163--95.
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  34. Daniel M. Haybron (2000). The Causal and Explanatory Role of Information Stored in Connectionist Networks. Minds and Machines 10 (3):361-380.
    In this paper I defend the propriety of explaining the behavior of distributed connectionist networks by appeal to selected data stored therein. In particular, I argue that if there is a problem with such explanations, it is a consequence of the fact that information storage in networks is superpositional, and not because it is distributed. I then develop a ``proto-account'''' of causation for networks, based on an account of Andy Clark''s, that shows even superpositionality does not undermine information-based explanation. Finally, (...)
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  35. Jürgen Hollatz (1999). Analogy Making in Legal Reasoning with Neural Networks and Fuzzy Logic. Artificial Intelligence and Law 7 (2-3):289-301.
    Analogy making from examples is a central task in intelligent system behavior. A lot of real world problems involve analogy making and generalization. Research investigates these questions by building computer models of human thinking concepts. These concepts can be divided into high level approaches as used in cognitive science and low level models as used in neural networks. Applications range over the spectrum of recognition, categorization and analogy reasoning. A major part of legal reasoning could be formally interpreted as an (...)
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  36. John E. Hummel (2000). Localism as a First Step Toward Symbolic Representation. Behavioral and Brain Sciences 23 (4):480-481.
    Page argues convincingly for several important properties of localist representations in connectionist models of cognition. I argue that another important property of localist representations is that they serve as the starting point for connectionist representations of symbolic (relational) structures because they express meaningful properties independent of one another and their relations.
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  37. Dan Hunter (1999). Out of Their Minds: Legal Theory in Neural Networks. [REVIEW] Artificial Intelligence and Law 7 (2-3):129-151.
    This paper examines the use of connectionism (neural networks) in modelling legal reasoning. I discuss how the implementations of neural networks have failed to account for legal theoretical perspectives on adjudication. I criticise the use of neural networks in law, not because connectionism is inherently unsuitable in law, but rather because it has been done so poorly to date. The paper reviews a number of legal theories which provide a grounding for the use of neural networks in law. It then (...)
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  38. Daniel D. Hutto (2011). Representation Reconsidered. [REVIEW] Philosophical Psychology 24 (1):135-139.
  39. Brian L. Keeley (2006). Paul Churchland. Cambridge: Cambridge University Press.
    This collection offers an introduction to Churchland's work, as well as a critique of some of his most famous philosophical positions.
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  40. Stephen M. Kosslyn & Gary Hatfield (1984). Representation Without Symbol Systems. Social Research 51:1019-1045.
  41. Emilio Kropff & Alessandro Treves (2008). Semantic Cognition: Distributed, but Then Attractive. Behavioral and Brain Sciences 31 (6):718-719.
    The parallel distributed processing (PDP) perspective brings forward the important point that all semantic phenomena are based on analog underlying mechanisms, involving the weighted summation of multiple inputs by individual neurons. It falls short of indicating, however, how the essentially discrete nature of semantic processing may emerge at the cognitive level. Bridging this gap probably requires attractor networks.
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  42. Aarre Laakso & Garrison W. Cottrell (2000). Content and Cluster Analysis: Assessing Representational Similarity in Neural Systems. Philosophical Psychology 13 (1):47-76.
    If connectionism is to be an adequate theory of mind, we must have a theory of representation for neural networks that allows for individual differences in weighting and architecture while preserving sameness, or at least similarity, of content. In this paper we propose a procedure for measuring sameness of content of neural representations. We argue that the correct way to compare neural representations is through analysis of the distances between neural activations, and we present a method for doing so. We (...)
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  43. Eric Lormand, Connectionist Content.
    If the arguments of chapter 1 are correct, associationist connectionist models (such as ultralocal ones) yield the clearest alternatives to the LOT hypothesis. While it may be that such models cannot provide a general account of cognition, they may account for important aspects of cognition, such as low-level perception (e.g., with the interactive activation model of reading) or the mechanisms which distinguish experts from novices at a given skill (e.g., with dependency-network models). Since these models stand a fighting chance of (...)
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  44. Jack C. Lyons (2014). The Epistemological Import of Morphological Content. Philosophical Studies 169 (3):537-547.
    Morphological content (MC) is content that is implicit in the standing structure of the cognitive system. Henderson and Horgan claim that MC plays a distinctive epistemological role unrecognized by traditional epistemic theories. I consider the possibilities that MC plays this role either in central cognition or in peripheral modules. I argue that the peripheral MC does not play an interesting epistemological role and that the central MC is already recognized by traditional theories.
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  45. B. Maclennan (2003). Transcending Turing Computability. Minds and Machines 13 (1):3-22.
    It has been argued that neural networks and other forms of analog computation may transcend the limits of Turing-machine computation; proofs have been offered on both sides, subject to differing assumptions. In this article I argue that the important comparisons between the two models of computation are not so much mathematical as epistemological. The Turing-machine model makes assumptions about information representation and processing that are badly matched to the realities of natural computation (information representation and processing in or inspired by (...)
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  46. Pete Mandik (2003). Varieties of Representation in Evolved and Embodied Neural Networks. Biology and Philosophy 18 (1):95-130.
    In this paper I discuss one of the key issuesin the philosophy of neuroscience:neurosemantics. The project of neurosemanticsinvolves explaining what it means for states ofneurons and neural systems to haverepresentational contents. Neurosemantics thusinvolves issues of common concern between thephilosophy of neuroscience and philosophy ofmind. I discuss a problem that arises foraccounts of representational content that Icall ``the economy problem'': the problem ofshowing that a candidate theory of mentalrepresentation can bear the work requiredwithin in the causal economy of a mind and (...)
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  47. Gary F. Marcus & Frank C. Keil (2008). Concepts, Correlations, and Some Challenges for Connectionist Cognition. Behavioral and Brain Sciences 31 (6):722-723.
    Rogers & McClelland's (R&M's) précis represents an important effort to address key issues in concepts and categorization, but few of the simulations deliver what is promised. We argue that the models are seriously underconstrained, importantly incomplete, and psychologically implausible; more broadly, R&M dwell too heavily on the apparent successes without comparable concern for limitations already noted in the literature.
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  48. Olga Markic (1995). Finding the Right Level for Connectionist Representations (a Critical Note on Ramsey's Paper). Acta Analytica 14 (14):27-35.
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  49. Brian P. McLaughlin (2009). Systematicity Redux. Synthese 170 (2):251 - 274.
    One of the main challenges that Jerry Fodor and Zenon Pylyshyn (Cognition 28:3–71, 1988) posed for any connectionist theory of cognitive architecture is to explain the systematicity of thought without implementing a Language of Thought (LOT) architecture. The systematicity challenge presents a dilemma: if connectionism cannot explain the systematicity of thought, then it fails to offer an adequate theory of cognitive architecture; and if it explains the systematicity of thought by implementing a LOT architecture, then it fails to offer 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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