Search results for 'Connectionism' (try it on Scholar)

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  1. Martin Davies (1991). Concepts, Connectionism, and the Language of Thought. In W Ramsey, Stephen P. Stich & D. Rumelhart (eds.), Philosophy and Connectionist Theory. Hillsdale, NJ: Lawrence Erlbaum Associates. 485-503.score: 27.0
    The aim of this paper is to demonstrate a _prima facie_ tension between our commonsense conception of ourselves as thinkers and the connectionist programme for modelling cognitive processes. The language of thought hypothesis plays a pivotal role. The connectionist paradigm is opposed to the language of thought; and there is an argument for the language of thought that draws on features of the commonsense scheme of thoughts, concepts, and inference. Most of the paper (Sections 3-7) is taken up with the (...)
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  2. John Sutton (1998). Philosophy and Memory Traces: Descartes to Connectionism. Cambridge University Press.score: 24.0
    Philosophy and Memory Traces defends two theories of autobiographical memory. One is a bewildering historical view of memories as dynamic patterns in fleeting animal spirits, nervous fluids which rummaged through the pores of brain and body. The other is new connectionism, in which memories are 'stored' only superpositionally, and reconstructed rather than reproduced. Both models, argues John Sutton, depart from static archival metaphors by employing distributed representation, which brings interference and confusion between memory traces. Both raise urgent issues about (...)
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  3. David J. Chalmers (1993). Connectionism and Compositionality: Why Fodor and Pylyshyn Were Wrong. Philosophical Psychology 6 (3):305-319.score: 24.0
    This paper offers both a theoretical and an experimental perspective on the relationship between connectionist and Classical (symbol-processing) models. Firstly, a serious flaw in Fodor and Pylyshyn’s argument against connectionism is pointed out: if, in fact, a part of their argument is valid, then it establishes a conclusion quite different from that which they intend, a conclusion which is demonstrably false. The source of this flaw is traced to an underestimation of the differences between localist and distributed representation. It (...)
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  4. William Ramsey, Stephen P. Stich & J. Garon (1991). Connectionism, Eliminativism, and the Future of Folk Psychology. In William Ramsey, Stephen P. Stich & D. Rumelhart (eds.), Philosophy and Connectionist Theory. Lawrence Erlbaum. 499-533.score: 24.0
  5. Paul Smolensky (1988). On the Proper Treatment of Connectionism. Behavioral and Brain Sciences 11 (1):1-23.score: 24.0
    A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...)
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  6. Michael V. Antony (1991). Fodor and Pylyshyn on Connectionism. Minds and Machines 1 (3):321-41.score: 24.0
    Fodor and Pylyshyn (1988) have argued that the cognitive architecture is not Connectionist. Their argument takes the following form: (1) the cognitive architecture is Classical; (2) Classicalism and Connectionism are incompatible; (3) therefore the cognitive architecture is not Connectionist. In this essay I argue that Fodor and Pylyshyn's defenses of (1) and (2) are inadequate. Their argument for (1), based on their claim that Classicalism best explains the systematicity of cognitive capacities, is an invalid instance of inference to the (...)
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  7. Murat Aydede (1997). Language of Thought: The Connectionist Contribution. Minds and Machines 7 (1):57-101.score: 24.0
    Fodor and Pylyshyn's critique of connectionism has posed a challenge to connectionists: Adequately explain such nomological regularities as systematicity and productivity without postulating a "language of thought" (LOT). Some connectionists like Smolensky took the challenge very seriously, and attempted to meet it by developing models that were supposed to be non-classical. At the core of these attempts lies the claim that connectionist models can provide a representational system with a combinatorial syntax and processes sensitive to syntactic structure. They are (...)
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  8. John Bickle (1993). Connectionism, Eliminativism, and the Semantic View of Theories. Erkenntnis 39 (3):359-382.score: 24.0
    Recently some philosophers have urged that connectionist artificial intelligence is (potentially) eliminative for the propositional attitudes of folk psychology. At the same time, however, these philosophers have also insisted that since philosophy of science has failed to provide criteria distinguishing ontologically retentive from eliminative theory changes, the resulting eliminativism is not principled. Application of some resources developed within the semantic view of scientific theories, particularly recent formal work on the theory reduction relation, reveals these philosophers to be wrong in this (...)
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  9. Mark Rowlands (1994). Connectionism and the Language of Thought. British Journal for the Philosophy of Science 45 (2):485-503.score: 24.0
    In an influential critique, Jerry Fodor and Zenon Pylyshyn point to the existence of a potentially devastating dilemma for connectionism (Fodor and Pylyshyn [1988]). Either connectionist models consist in mere associations of unstructured representations, or they consist in processes involving complex representations. If the former, connectionism is mere associationism, and will not be capable of accounting for very much of cognition. If the latter, then connectionist models concern only the implementation of cognitive processes, and are, therefore, not informative (...)
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  10. Martin Davies (1989). Connectionism, Modularity and Tacit Knowledge. British Journal for the Philosophy of Science 40 (December):541-55.score: 24.0
    In this paper, I define tacit knowledge as a kind of causal-explanatory structure, mirroring the derivational structure in the theory that is tacitly known. On this definition, tacit knowledge does not have to be explicitly represented. I then take the notion of a modular theory, and project the idea of modularity to several different levels of description: in particular, to the processing level and the neurophysiological level. The fundamental description of a connectionist network lies at a level between the processing (...)
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  11. Michael R. W. Dawson & Corinne Zimmerman (2003). Interpreting the Internal Structure of a Connectionist Model of the Balance Scale Task. Brain and Mind 4 (2):129-149.score: 24.0
    One new tradition that has emerged from early research on autonomous robots is embodied cognitive science. This paper describes the relationship between embodied cognitive science and a related tradition, synthetic psychology. It is argued that while both are synthetic, embodied cognitive science is antirepresentational while synthetic psychology still appeals to representations. It is further argued that modern connectionism offers a medium for conducting synthetic psychology, provided that researchers analyze the internal representations that their networks develop. The paper then provides (...)
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  12. M. Forster & Eric Saidel (1994). Connectionism and the Fate of Folk Psychology. Philosophical Psychology 7 (4):437-52.score: 24.0
    Abstract Ramsey, Stick and Garon (1991) argue that if the correct theory of mind is some parallel distributed processing theory, then folk psychology must be false. Their idea is that if the nodes and connections that encode one representation are causally active then all representations encoded by the same set of nodes and connections are also causally active. We present a clear, and concrete, counterexample to RSG's argument. In conclusion, we suggest that folk psychology and connectionism are best understood (...)
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  13. Kenneth Aizawa (1994). Representations Without Rules, Connectionism, and the Syntactic Argument. Synthese 101 (3):465-92.score: 24.0
    Terry Horgan and John Tienson have suggested that connectionism might provide a framework within which to articulate a theory of cognition according to which there are mental representations without rules (RWR) (Horgan and Tienson 1988, 1989, 1991, 1992). In essence, RWR states that cognition involves representations in a language of thought, but that these representations are not manipulated by the sort of rules that have traditionally been posited. In the development of RWR, Horgan and Tienson attempt to forestall a (...)
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  14. William P. Bechtel (1994). Natural Deduction in Connectionist Systems. Synthese 101 (3):433-463.score: 24.0
    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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  15. William Ramsey & Stephen P. Stich (1990). Connectionism and Three Levels of Nativism. Synthese 82 (2):177-205.score: 24.0
    Along with the increasing popularity of connectionist language models has come a number of provocative suggestions about the challenge these models present to Chomsky's arguments for nativism. The aim of this paper is to assess these claims. We begin by reconstructing Chomsky's argument from the poverty of the stimulus and arguing that it is best understood as three related arguments, with increasingly strong conclusions. Next, we provide a brief introduction to connectionism and give a quick survey of recent efforts (...)
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  16. W. F. G. Haselager & J. F. H. Van Rappard (1998). Connectionism, Systematicity, and the Frame Problem. Minds and Machines 8 (2):161-179.score: 24.0
    This paper investigates connectionism's potential to solve the frame problem. The frame problem arises in the context of modelling the human ability to see the relevant consequences of events in a situation. It has been claimed to be unsolvable for classical cognitive science, but easily manageable for connectionism. We will focus on a representational approach to the frame problem which advocates the use of intrinsic representations. We argue that although connectionism's distributed representations may look promising from this (...)
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  17. Eric Lormand (1991). Classical and Connectionist Models. Dissertation, Mitscore: 24.0
    Much of the philosophical interest of cognitive science stems from its potential relevance to the mind/body problem. The mind/body problem concerns whether both mental and physical phenomena exist, and if so, whether they are distinct. In this chapter I want to portray the classical and connectionist frameworks in cognitive science as potential sources of evidence for or against a particular strategy for solving the mind/body problem. It is not my aim to offer a full assessment of these two frameworks in (...)
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  18. L. Shastri & V. Ajjanagadde (1993). From Simple Associations to Systematic Reasoning: A Connectionist Representation of Rules, Variables, and Dynamic Binding Using Temporal Synchrony. Behavioral and Brain Sciences 16 (3):417-51.score: 24.0
    Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large (...)
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  19. Stephen P. Stich & Ted A. Warfield (1995). Reply to Clark and Smolensky: Do Connectionist Minds Have Beliefs? In C. Macdonald & Graham F. Macdonald (eds.), Connectionism: Debates on Psychological Explanation. Blackwell.score: 24.0
  20. Daniel M. Haybron (2000). The Causal and Explanatory Role of Information Stored in Connectionist Networks. Minds and Machines 10 (3):361-380.score: 24.0
    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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  21. Jonathan Opie & Gerard O'Brien (1999). A Connectionist Theory of Phenomenal Experience. Behavioral and Brain Sciences 22 (1):127-148.score: 24.0
    When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many of them have been doing recently, there are two fundamentally distinct approaches available. Either consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or it is to be explained in terms of the computational processes defined over these vehicles. We call versions of these two approaches _vehicle_ and _process_ theories of consciousness, respectively. However, while there may be space (...)
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  22. Gerard O'Brien (1989). Connectionism, Analogicity and Mental Content. Acta Analytica 22 (22):111-31.score: 24.0
    In Connectionism and the Philosophy of Psychology, Horgan and Tienson (1996) argue that cognitive processes, pace classicism, are not governed by exceptionless, “representation-level” rules; they are instead the work of defeasible cognitive tendencies subserved by the non-linear dynamics of the brain’s neural networks. Many theorists are sympathetic with the dynamical characterisation of connectionism and the general (re)conception of cognition that it affords. But in all the excitement surrounding the connectionist revolution in cognitive science, it has largely gone unnoticed (...)
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  23. Kim Plunkett (2001). Connectionism Today. Synthese 129 (2):185-194.score: 24.0
    Connectionist networks have been used to model a wide range of cognitivephenomena, including developmental, neuropsychological and normal adultbehaviours. They have offered radical alternatives to traditional accounts ofwell-established facts about cognition. The primary source of the success ofthese models is their sensitivity to statistical regularities in their trainingenvironment. This paper provides a brief description of the connectionisttoolbox and how this has developed over the past 2 decades, with particularreference to the problem of reading aloud.
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  24. William Ramsey, Stephen P. Stich & D. M. Rumelhart (eds.) (1991). Philosophy and Connectionist Theory. Lawrence Erlbaum.score: 24.0
    The philosophy of cognitive science has recently become one of the most exciting and fastest growing domains of philosophical inquiry and analysis. Until the early 1980s, nearly all of the models developed treated cognitive processes -- like problem solving, language comprehension, memory, and higher visual processing -- as rule-governed symbol manipulation. However, this situation has changed dramatically over the last half dozen years. In that period there has been an enormous shift of attention toward connectionist models of cognition that are (...)
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  25. Terence E. Horgan (1997). Connectionism and the Philosophical Foundations of Cognitive Science. Metaphilosophy 28 (1-2):1-30.score: 24.0
    This is an overview of recent philosophical discussion about connectionism and the foundations of cognitive science. Connectionist modeling in cognitive science is described. Three broad conceptions of the mind are characterized, and their comparative strengths and weaknesses are discussed: (1) the classical computational conception in cognitive science; (2) a popular foundational interpretation of connectionism that John Tienson and I call “non-sentential computationalism”; and (3) an alternative interpretation of connectionism we call “dynamical cognition.” Also discussed are two recent (...)
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  26. Bryon Cunningham (2001). Capturing Qualia: Higher-Order Concepts and Connectionism. Philosophical Psychology 14 (1):29-41.score: 24.0
    Antireductionist philosophers have argued for higher-order classifications of qualia that locate consciousness outside the scope of conventional scientific explanations, viz., by classifying qualia as intrinsic, basic, or subjective properties, antireductionists distinguish qualia from extrinsic, complex, and objective properties, and thereby distinguish conscious mental states from the possible explananda of functionalist or physicalist explanations. I argue that, in important respects, qualia are intrinsic, basic, and subjective properties of conscious mental states, and that, contrary to antireductionists' suggestions, these (...)
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  27. Marcello Guarini (2001). A Defence of Connectionism Against the "Syntactic" Argument. Synthese 128 (3):287-317.score: 24.0
    In "Representations without Rules, Connectionism and the Syntactic Argument'', Kenneth Aizawa argues against the view that connectionist nets can be understood as processing representations without the use of representation-level rules, and he provides a positive characterization of how to interpret connectionist nets as following representation-level rules. He takes Terry Horgan and John Tienson to be the targets of his critique. The present paper marshals functional and methodological considerations, gleaned from the practice of cognitive modelling, to argue against Aizawa's characterization (...)
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  28. Brian P. McLaughlin & F. Warfield (1994). The Allure of Connectionism Reexamined. Synthese 101 (3):365-400.score: 24.0
    There is currently a debate over whether cognitive architecture is classical or connectionist in nature. One finds the following three comparisons between classical architecture and connectionist architecture made in the pro-connectionist literature in this debate: (1) connectionist architecture is neurally plausible and classical architecture is not; (2) connectionist architecture is far better suited to model pattern recognition capacities than is classical architecture; and (3) connectionist architecture is far better suited to model the acquisition of pattern recognition capacities by learning than (...)
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  29. Keith Butler (1995). Representation and Computation in a Deflationary Assessment of Connectionist Cognitive Science. Synthese 104 (1):71-97.score: 24.0
    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 (...)
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  30. Mike Oaksford, Nick Chater & Keith Stenning (1990). Connectionism, Classical Cognitive Science and Experimental Psychology. AI and Society 4 (1):73-90.score: 24.0
    Classical symbolic computational models of cognition are at variance with the empirical findings in the cognitive psychology of memory and inference. Standard symbolic computers are well suited to remembering arbitrary lists of symbols and performing logical inferences. In contrast, human performance on such tasks is extremely limited. Standard models donot easily capture content addressable memory or context sensitive defeasible inference, which are natural and effortless for people. We argue that Connectionism provides a more natural framework in which to model (...)
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  31. Andy Clark (1990). Connectionism, Competence and Explanation. British Journal for the Philosophy of Science 41 (June):195-222.score: 24.0
    A competence model describes the abstract structure of a solution to some problem. or class of problems, facing the would-be intelligent system. Competence models can be quite derailed, specifying far more than merely the function to be computed. But for all that, they are pitched at some level of abstraction from the details of any particular algorithm or processing strategy which may be said to realize the competence. Indeed, it is the point and virtue of such models to specify some (...)
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  32. James W. Garson (2003). Simulation and Connectionism: What is the Connection? Philosophical Psychology 16 (4):499-515.score: 24.0
    Simulation has emerged as an increasingly popular account of folk psychological (FP) talents at mind-reading: predicting and explaining human mental states. Where its rival (the theory-theory) postulates that these abilities are explained by mastery of laws describing the connections between beliefs, desires, and action, simulation theory proposes that we mind-read by "putting ourselves in another's shoes." This paper concerns connectionist architecture and the debate between simulation theory (ST) and the theory-theory (TT). It is only natural to associate TT with classical (...)
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  33. Paul R. Thagard (1989). Connectionism and Epistemology: Goldman on Winner-Take-All Networks. Philosophia 19 (2-3):189-196.score: 24.0
    This paper examines Alvin Goldman's discussion of acceptance and uncertainty in chapter 15 of his book, Epistemology and Cognition. Goldman discusses how acceptance and rejection of beliefs might be understood in terms of "winner-take-all" connectionist networks. The paper answers some of the questions he raises in his epistemic evaluation of connectionist programs. The major tool for doing this is a connectionist model of explanatory coherence judgments (Thagard, Behavioral and Brain Sciences, 1989). Finally, there is a discussion of problems for Goldman's (...)
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  34. Hugh Clapin (1991). Connectionism Isn't Magic. Minds and Machines 1 (2):167-84.score: 24.0
    Ramsey, Stich and Garon's recent paper Connectionism, Eliminativism, and the Future of Folk Psychology claims a certain style of connectionism to be the final nail in the coffin of folk psychology. I argue that their paper fails to show this, and that the style of connectionism they illustrate can in fact supplement, rather than compete with, the claims of a theory of cognition based in folk psychology's ontology. Ramsey, Stich and Garon's argument relies on the lack of (...)
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  35. Robert F. Hadley & M. B. Hayward (1997). Strong Semantic Systematicity From Hebbian Connectionist Learning. Minds and Machines 7 (1):1-55.score: 24.0
    Fodor's and Pylyshyn's stand on systematicity in thought and language has been debated and criticized. Van Gelder and Niklasson, among others, have argued that Fodor and Pylyshyn offer no precise definition of systematicity. However, our concern here is with a learning based formulation of that concept. In particular, Hadley has proposed that a network exhibits strong semantic systematicity when, as a result of training, it can assign appropriate meaning representations to novel sentences (both simple and embedded) which contain words in (...)
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  36. Martin Roth (2005). Program Execution in Connectionist Networks. Mind and Language 20 (4):448-467.score: 24.0
    Recently, connectionist models have been developed that seem to exhibit structuresensitive cognitive capacities without executing a program. This paper examines one such model and argues that it does execute a program. The argument proceeds by showing that what is essential to running a program is preserving the functional structure of the program. It has generally been assumed that this can only be done by systems possessing a certain temporalcausal organization. However, counterfactualpreserving functional architecture can be instantiated in other ways, for (...)
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  37. Jack Copeland (1996). On Alan Turing's Anticipation of Connectionism. Synthese 108 (3):361-377.score: 24.0
    It is not widely realised that Turing was probably the first person to consider building computing machines out of simple, neuron-like elements connected together into networks in a largely random manner. Turing called his networks unorganised machines. By the application of what he described as appropriate interference, mimicking education an unorganised machine can be trained to perform any task that a Turing machine can carry out, provided the number of neurons is sufficient. Turing proposed simulating both the behaviour of the (...)
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  38. Scott R. Sehon (1998). Connectionism and the Causal Theory of Action Explanation. Philosophical Psychology 11 (4):511-532.score: 24.0
    It is widely assumed that common sense psychological explanations of human action are a species of causal explanation. I argue against this construal, drawing on Ramsey et al.'s paper, “Connectionism, eliminativism, and the future of folk psychology”. I argue that if certain connec-tionist models are correct, then mental states cannot be identified with functionally discrete causes of behavior, and I respond to some recent attempts to deny this claim. However, I further contend that our common sense psychological practices are (...)
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  39. S. Kaplan, M. Weaver & Robert M. French (1990). Active Symbols and Internal Models: Towards a Cognitive Connectionism. [REVIEW] AI and Society 4 (1):51-71.score: 24.0
    In the first section of the article, we examine some recent criticisms of the connectionist enterprise: first, that connectionist models are fundamentally behaviorist in nature (and, therefore, non-cognitive), and second that connectionist models are fundamentally associationist in nature (and, therefore, cognitively weak). We argue that, for a limited class of connectionist models (feed-forward, pattern-associator models), the first criticism is unavoidable. With respect to the second criticism, we propose that connectionist modelsare fundamentally associationist but that this is appropriate for building models (...)
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  40. John Hawthorne (1989). On the Compatibility of Connectionist and Classical Models. Philosophical Psychology 2 (1):5-16.score: 24.0
    This paper presents considerations in favour of the view that traditional (classical) architectures can be seen as emergent features of connectionist networks with distributed representation. A recent paper by William Bechtel (1988) which argues for a similar conclusion is unsatisfactory in that it fails to consider whether the compositional syntax and semantics attributed to mental representations by classical models can emerge within a connectionist network. The compatibility of the two paradigms hinges largely, I suggest, on how this question is answered. (...)
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  41. Mary Litch (1997). Computation, Connectionism and Modelling the Mind. Philosophical Psychology 10 (3):357-364.score: 24.0
    Any analysis of the concept of computation as it occurs in the context of a discussion of the computational model of the mind must be consonant with the philosophic burden traditionally carried by that concept as providing a bridge between a physical and a psychological description of an agent. With this analysis in hand, one may ask the question: are connectionist-based systems consistent with the computational model of the mind? The answer depends upon which of several versions of connectionism (...)
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  42. Gerard O'Brien & Jonathan Opie (1999). A Connectionist Theory of Phenomenal Experience. Behavioral and Brain Sciences 22 (1):127-48.score: 24.0
    When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many of them have been doing recently, there are two fundamentally distinct approaches available. Either consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or it is to be explained in terms of the computational processes defined over these vehicles. We call versions of these two approaches vehicle and process theories of consciousness, respectively. However, while there may be space (...)
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  43. R. H. Phaf & G. Wolters (1997). A Constructivist and Connectionist View on Conscious and Nonconscious Processes. Philosophical Psychology 10 (3):287-307.score: 24.0
    Recent experimental findings reveal dissociations of conscious and nonconscious performance in many fields of psychological research, suggesting that conscious and nonconscious effects result from qualitatively different processes. A connectionist view of these processes is put forward in which consciousness is the consequence of construction processes taking place in three types of working memory in a specific type of recurrent neural network. The recurrences arise by feeding back output to the input of a central (representational) network. They are assumed to be (...)
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  44. Andy Clark (1993). Superpositional Connectionism: A Reply to Marinov. [REVIEW] Minds and Machines 3 (3):271-81.score: 24.0
    Marinov''s critique I argue, is vitiated by its failure to recognize the distinctive role of superposition within the distributed connectionist paradigm. The use of so-called subsymbolic distributed encodings alone is not, I agree, enough to justify treating distributed connectionism as a distinctive approach. It has always been clear that microfeatural decomposition is both possible and actual within the confines of recognizably classical approaches. When such approaches also involve statistically-driven learning algorithms — as in the case of ID3 — the (...)
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  45. William P. Bechtel (1988). Connectionism and Rules and Representation Systems: Are They Compatible? Philosophical Psychology 1 (1):5-16.score: 24.0
    The introduction of connectionist or parallel distributed processing (PDP) systems to model cognitive functions has raised the question of the possible relations between these models and traditional information processing models which employ rules to manipulate representations. After presenting a brief account of PDP models and two ways in which they are commonly interpreted by those seeking to use them to explain cognitive functions, I present two ways one might relate these models to traditional information processing models and so not totally (...)
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  46. Keith Butler (1993). Connectionism, Classical Cognitivism, and the Relation Between Cognitive and Implementational Levels of Analysis. Philosophical Psychology 6 (3):321-33.score: 24.0
    This paper discusses the relation between cognitive and implementational levels of analysis. Chalmers (1990, 1993) argues that a connectionist implementation of a classical cognitive architecture possesses a compositional semantics, and therefore undercuts Fodor and Pylyshyn's (1988) argument that connectionist networks cannot possess a compositional semantics. I argue that Chalmers argument misconstrues the relation between cognitive and implementational levels of analysis. This paper clarifies the distinction, and shows that while Fodor and Pylyshyn's argument survives Chalmers' critique, it cannot be used to (...)
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  47. Gordon G. Globus (1992). Derrida and Connectionism: Differance in Neural Nets. Philosophical Psychology 5 (2):183-97.score: 24.0
    A possible relation between Derrida's deconstruction of metaphysics and connectionism is explored by considering diff rance in neural nets terms. First diff rance , as the crossing of Saussurian difference and Freudian deferral, is modeled and then the fuller 'sheaf of diff rance is taken up. The metaphysically conceived brain has two versions: in the traditional computational version the brain processes information like a computer and in the connectionist version the brain computes input vector to output vector transformations non-symbolically. (...)
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  48. Robert F. Hadley (1993). Connectionism, Explicit Rules, and Symbolic Manipulation. Minds and Machines 3 (2):183-200.score: 24.0
    At present, the prevailing Connectionist methodology forrepresenting rules is toimplicitly embody rules in neurally-wired networks. That is, the methodology adopts the stance that rules must either be hard-wired or trained into neural structures, rather than represented via explicit symbolic structures. Even recent attempts to implementproduction systems within connectionist networks have assumed that condition-action rules (or rule schema) are to be embodied in thestructure of individual networks. Such networks must be grown or trained over a significant span of time. However, arguments (...)
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  49. David DeMoss (2007). The Connectionist Self in Action. Mind and Society 6 (1):19-33.score: 24.0
    ObjectiveTo demonstrate that the human brain, as a connectionist system, has the capacity to become a free, rational, moral, agent—that is, the capacity to become a self—and that the brain becomes a self by engaging second-order reflection in the hermeneutical task of constructing narratives that rationalise action. StructureSection 2 explains the connectionist brain and its relevant capacities: to categorise, to develop goal-directed dispositions, to problem-solve what it should do, and to second-order reflect. Section 3 argues that the connectionist brain constitutes (...)
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  50. Mike Page (2000). Connectionist Modelling in Psychology: A Localist Manifesto. Behavioral and Brain Sciences 23 (4):443-467.score: 24.0
    Over the last decade, fully distributed models have become dominant in connectionist psychological modelling, whereas the virtues of localist models have been underestimated. This target article illustrates some of the benefits of localist modelling. Localist models are characterized by the presence of localist representations rather than the absence of distributed representations. A generalized localist model is proposed that exhibits many of the properties of fully distributed models. It can be applied to a number of problems that are difficult for fully (...)
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