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6.3f. Philosophy of Connectionism, Misc (Philosophy of Connectionism, Misc on PhilPapers)

See also:
Abrahamsen, Adele A. (1993). Cognizers' innards and connectionist nets: A holy alliance? Mind and Language 8 (4):520-530.   (Cited by 2 | Google | More links)
Aizawa, Kenneth (1999). Connectionist rules: A rejoinder to Horgan and Tienson's connectionism and the philosophy of psychology. Acta Analytica 22 (22):59-85.   (Cited by 3 | Google)
Bechtel, William P. (1985). Are the new PDP models of cognition cognitivist or associationist? Behaviorism 13:53-61.   (Google)
Bechtel, William P. & Abrahamson, A. (1990). Beyond the exclusively propositional era. Synthese 82 (2):223-53.   (Cited by 9 | Annotation | Google | More links)
Abstract:   Contemporary epistemology has assumed that knowledge is represented in sentences or propositions. However, a variety of extensions and alternatives to this view have been proposed in other areas of investigation. We review some of these proposals, focusing on (1) Ryle's notion of knowing how and Hanson's and Kuhn's accounts of theory-laden perception in science; (2) extensions of simple propositional representations in cognitive models and artificial intelligence; (3) the debate concerning imagistic versus propositional representations in cognitive psychology; (4) recent treatments of concepts and categorization which reject the notion of necessary and sufficient conditions; and (5) parallel distributed processing (connectionist) models of cognition. This last development is especially promising in providing a flexible, powerful means of representing information nonpropositionally, and carrying out at least simple forms of inference without rules. Central to several of the proposals is the notion that much of human cognition might consist in pattern recognition rather than manipulation of rules and propositions
Bechtel, William P. (1988). Connectionism and rules and representation systems: Are they compatible? Philosophical Psychology 1 (1):5-16.   (Cited by 43 | Annotation | Google)
Abstract: 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 repudiate the tradition of modelling cognition through systems of rules and representations. The proposal that seems most promising is that PDP-type structures might provide the underlying framework in which a rule and representation model might be implemented. To show how one might pursue such a strategy, I discuss recent research by Barsalou on the instability of concepts and show how that might be accounted for in a system whose microstructure had a PDP architecture. I also outline how adopting a multi-leveled view of the mind, where on one level the mind employed a PDP-type system and at another level constituted a rule processing system, would allow researchers to relocate some problems which seemed difficult to explain at one level, such as the capacity for concept learning, to another level where it could be handled in a straightforward manner
Bechtel, William P. (1987). Connectionism and the philosophy of mind. Southern Journal of Philosophy Supplement 26:17-41.   (Cited by 18 | Annotation | Google)
Bechtel, William P. & Abrahamsen, Adele A. (1992). Connectionism and the future of folk psychology. In Robert G. Burton (ed.), Minds: Natural and Artificial. SUNY Press.   (Cited by 3 | Google | More links)
Bechtel, William P. (1985). Contemporary connectionism: Are the new parallel distributed processing models cognitive or associationist? Behaviorism 13:53-61.   (Cited by 8 | Google)
Bechtel, William P. (1993). The case for connectionism. Philosophical Studies 71 (2):119-54.   (Cited by 5 | Google | More links)
Bechtel, William P. (1993). The path beyond first-order connectionism. Mind and Language 8 (4):531-539.   (Cited by 6 | Google | More links)
Bechtel, William P. (1986). What happens to accounts of mind-brain relations if we forgo an architecture of rules and representations? Philosophy of Science Association 1986.   (Annotation | Google)
Bechtel, William P. (1996). What should a connectionist philosophy of science look like? In The Churchlands and Their Critics. Oup.   (Cited by 5 | Google | More links)
Abstract: The reemergence of connectionism2 has profoundly altered the philosophy of mind. Paul Churchland has argued that it should equally transform the philosophy of science. He proposes that connectionism offers radical and useful new ways of understanding theories and explanations
Berkeley, Istvan S. N. (ms). A revisionist history of connectionism.   (Cited by 1 | Google)
Abstract: According to the standard (recent) history of connectionism (see for example the accounts offered by Hecht-Nielsen (1990: pp. 14-19) and Dreyfus and Dreyfus (1988), or Papert's (1988: pp. 3-4) somewhat whimsical description), in the early days of Classical Computational Theory of Mind (CCTM) based AI research, there was also another allegedly distinct approach, one based upon network models. The work on network models seems to fall broadly within the scope of the term 'connectionist' (see Aizawa 1992), although the term had yet to be coined at the time. These two approaches were "two daughter sciences" according to Papert (1988: p. 3). The fundamental difference between these two 'daughters', lay (according to Dreyfus and Dreyfus (1988: p. 16)) in what they took to be the paradigm of intelligence. Whereas the early connectionists took learning to be fundamental, the traditional school concentrated upon problem solving
Berkeley, István S. N. (online). Some myths of connectionism.   (Cited by 1 | Google)
Abstract: Since the emergence of what Fodor and Pylyshyn (1988) call 'new connectionism', there can be little doubt that connectionist research has become a significant topic for discussion in the Philosophy of Cognitive Science and the Philosophy of Mind. In addition to the numerous papers on the topic in philosophical journals, almost every recent book in these areas contain at least a brief reference to, or discussion of, the issues raised by connectionist research (see Sterelny 1990, Searle, 1992, and O Nualláin, 1995, for example). Other texts have focused almost exclusively upon connectionist issues (see Clark, 1993, Bechtel and Abrahamsen, 1991 and Lloyd, 1989, for example). Regrettably the discussions of connectionism found in the philosophical literature suffer from a number of deficiencies. My purpose in this paper is to highlight one particular problem and attempt to take a few steps to remedy the situation
Berkeley, Istvan S. N. (online). What is connectionism?   (Google)
Abstract: Connectionism is a style of modeling based upon networks of interconnected simple processing devices. This style of modeling goes by a number of other names too. Connectionist models are also sometimes referred to as 'Parallel Distributed Processing' (or PDP for short) models or networks.1 Connectionist systems are also sometimes referred to as 'neural networks' (abbreviated to NNs) or 'artificial neural networks' (abbreviated to ANNs). Although there may be some rhetorical appeal to this neural nomenclature, it is in fact misleading as connectionist networks are commonly significantly dissimilar to neurological systems. For this reason, I will avoid using this terminology, other than in direct quotations. Instead, I will follow the practice I have adopted above and use 'connectionist' as my primary term for systems of this kind
Bickle, John (1995). Connectionism, reduction, and multiple realizability. Behavior and Philosophy 23 (2):29-39.   (Cited by 3 | Google)
Blackmore, Susan J. (2003). The case of the mysterious mind: Review of Radiant Cool, by Dan Lloyd. New Scientist 13:36-39.   (Cited by 3 | Google | More links)
Bradshaw, Denny E. (1991). Connectionism and the specter of representationalism. In Terence E. Horgan & John L. Tienson (eds.), Connectionism and the Philosophy of Mind. Kluwer.   (Cited by 4 | Annotation | Google)
Christie, Drew (1993). Comments on Bechtel's The Case for Connectionism. Philosophical Studies 71 (2):155-162.   (Cited by 1 | Google | More links)
Churchland, Patricia S. & Sejnowski, Terrence J. (1989). Neural representation and neural computation. In L. Nadel (ed.), Neural Connections, Mental Computations. MIT Press.   (Cited by 78 | Annotation | Google | More links)
Churchland, Paul M. (1989). On the nature of explanation: A PDP approach. In A Neurocomputational Perspective. MIT Press.   (Cited by 9 | Annotation | Google | More links)
Churchland, Paul M. (1989). On the nature of theories: A neurocomputational perspective. Minnesota Studies in the Philosophy of Science 14.   (Cited by 22 | Annotation | Google)
Clark, Andy (1990). Connectionism, competence and explanation. British Journal for the Philosophy of Science 41 (June):195-222.   (Cited by 25 | Annotation | Google | More links)
Abstract: 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 equivalence class of algorithms/processing strategies so that the common properties highlighted by the chosen class may feature in psychologically interesting accounts. A question arises concerning the type of relation a theorist might expect to hold between such a competence model and a psychologically real processing strategy. Classical work in cognitive science expects the actual processing to depend on explicit or tacit knowledge of the competence theory. Connectionist work, for reasons to be explained, represents a departure from this norm. But the precise way in which a connectionist approach may disturb the satisfying classical symmetry of competence and processing has yet to be properly specified. A standard ?Newtonian? connectionist account, due to Paul Smolensky, is discussed and contrasted with a somewhat different ?rogue? account. A standard connectionist understanding has it that a classical competence theory describes an idealized subset of a network's behaviour. But the network's behaviour is not to be explained by its embodying explicit or tacit knowledge of the information laid out in the competence theory. A rogue model, by contrast, posits either two systems, or two aspects of a single system, such that one system does indeed embody the knowledge laid out in the competence theory
Clark, Andy (1995). Connectionist minds. In Connectionism: Debates on Psychological Explanation. Cambridge: Blackwell.   (Cited by 10 | Google)
Clark, Andy (1989). Microcognition. MIT Press.   (Cited by 300 | Annotation | Google | More links)
Clark, Andy (1989). Microfunctionalism: Connectionism and the Scientific Explanation of Mental States. In A. Clark (ed.), Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing. MIT Press.   (Google | More links)
Abstract: This is an amended version of material that first appeared in A. Clark, Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing (MIT Press, Cambridge, MA, 1989), Ch. 1, 2, and 6. It appears in German translation in Metzinger,T (Ed) DAS LEIB-SEELE-PROBLEM IN DER ZWEITEN HELFTE DES 20 JAHRHUNDERTS (Frankfurt am Main: Suhrkamp. 1999)
Clark, Andy (1991). Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing. Cambridge: MIT Press.   (Cited by 224 | Google | More links)
Clark, Andy & Eliasmith, Chris (2002). Philosophical issues in brain theory and connectionism. In Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press.   (Cited by 7 | Google | More links)
Collier, Mark (1999). Filling the Gaps: Hume and Connectionism on the Continued Existence of Unperceived Objects". Hume Studies 25 (1 and 2):155-170.   (Google)
Copeland, Jack (1996). On Alan Turing's anticipation of connectionism. Synthese 108 (3):361-377.   (Cited by 20 | Google | More links)
Abstract:   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 network and the training process by means of a computer program. We outline Turing's connectionist project of 1948
Cummins, Robert E. (1995). Connectionist and the rationale constraint on cognitive explanations. Philosophical Perspectives 9:105-25.   (Cited by 3 | Google | More links)
Cummins, Robert E. & Schwarz, Georg (1991). Connectionism, computation, and cognition. In Terence E. Horgan & John L. Tienson (eds.), Connectionism and the Philosophy of Mind. Kluwer.   (Cited by 55 | Annotation | Google)
Cummins, Robert E. & Schwarz, Georg (1987). Radical connectionism. Southern Journal of Philosophy Supplement 26:43-61.   (Cited by 8 | Annotation | Google)
Davies, Martin (1989). Connectionism, modularity and tacit knowledge. British Journal for the Philosophy of Science 40 (December):541-55.   (Cited by 11 | Annotation | Google | More links)
Abstract: 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 level and the physiological level. At this level, connectionism involves a characteristic departure from modularity, and a correlative absence of syntactic structure. This is linked to the fact that tacit knowledge descriptions of networks are only approximately true. A consequence is that strict causal systematicity in cognitive processes poses a problem for the connectionist programme
Duran, Jane & Doell, Ruth (1993). Naturalized epistemology, connectionism and biology. Dialectica 47 (4):327-336.   (Google)
García-Carpintero, Manuel (1995). The philosophical import of connectionism: A critical notice of Andy Clark's associative engines. Mind and Language 10 (4):370-401.   (Cited by 1 | Google)
Globus, Gordon G. (1992). Derrida and connectionism: Differance in neural nets. Philosophical Psychology 5 (2):183-97.   (Cited by 2 | Google)
Abstract: 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. The 'deconstructed brain' neither processes information nor computes functions but is spontaneously economical
Hadley, Robert F. (1999). Connectionism and novel combinations of skills: Implications for cognitive architecture. Minds and Machines 9 (2):197-221.   (Cited by 11 | Google | More links)
Abstract:   In the late 1980s, there were many who heralded the emergence of connectionism as a new paradigm – one which would eventually displace the classically symbolic methods then dominant in AI and Cognitive Science. At present, there remain influential connectionists who continue to defend connectionism as a more realistic paradigm for modeling cognition, at all levels of abstraction, than the classical methods of AI. Not infrequently, one encounters arguments along these lines: given what we know about neurophysiology, it is just not plausible to suppose that our brains are digital computers. Thus, they could not support a classical architecture. I argue here for a middle ground between connectionism and classicism. I assume, for argument's sake, that some form(s) of connectionism can provide reasonably approximate models – at least for lower-level cognitive processes. Given this assumption, I argue on theoretical and empirical grounds that most human mental skills must reside in separate connectionist modules or sub-networks. Ultimately, it is argued that the basic tenets of connectionism, in conjunction with the fact that humans often employ novel combinations of skill modules in rule following and problem solving, lead to the plausible conclusion that, in certain domains, high level cognition requires some form of classical architecture. During the course of argument, it emerges that only an architecture with classical structure could support the novel patterns of information flow and interaction that would exist among the relevant set of modules. Such a classical architecture might very well reside in the abstract levels of a hybrid system whose lower-level modules are purely connectionist
Hatfield, Gary (1990). Gibsonian representations and connectionist symbol-processing: Prospects for unification. Psychological Research 52:243-52.   (Cited by 5 | Annotation | Google)
Horgan, Terence E. & Tienson, John L. (1999). Authors' replies. Acta Analytica 22 (22):275-287.   (Google)
Horgan, Dianne D. & Hacker, Douglas J. (1999). Beginning a theoretician-practitioner dialogue about connectionism. Acta Analytica 22 (22):261-273.   (Google)
Horgan, Terence E. & Tienson, John L. (eds.) (1991). Connectionism and the Philosophy of Mind. Kluwer.   (Cited by 30 | Google)
Abstract: "A third of the papers in this volume originated at the 1987 Spindel Conference ... at Memphis State University"--Pref.
Horgan, Terence E. & Tienson, John L. (1996). Connectionism and the Philosophy of Psychology. MIT Press.   (Cited by 123 | Google)
Abstract: In Connectionism and the Philosophy of Psychology, Horgan and Tienson articulate and defend a new view of cognition.
Horgan, Terence E. (1997). Connectionism and the philosophical foundations of cognitive science. Metaphilosophy 28 (1-2):1-30.   (Cited by 5 | Google | More links)
Horgan, Terence E. (1997). Modelling the noncomputational mind: Reply to Litch. Philosophical Psychology 10 (3):365-371.   (Google)
Abstract: I explain why, within the nonclassical framework for cognitive science we describe in the book, cognitive-state transitions can fail to be tractably computable even if they are subserved by a discrete dynamical system whose mathematical-state transitions are tractably computable. I distinguish two ways that cognitive processing might conform to programmable rules in which all operations that apply to representation-level structure are primitive, and two corresponding constraints on models of cognition. Although Litch is correct in maintaining that classical cognitive science is not committed to the first constraint, it is committed to the second. This fact constitutes an illuminating gloss on our claim that one foundational assumption of classicism is that human cognition conforms to programmable, representation-level, rules
Horgan, Terence E. (1999). Short prcis of connectionism and the philosophy of psychology. Acta Analytica 22 (22):9-21.   (Cited by 4 | Google)
Humphreys, Glyn W. (1986). Information-processing systems which embody computational rules: The connectionist approach. Mind and Language 1:201-12.   (Cited by 2 | Google)
Kirsh, David (1992). PDP Learnability and Innate Knowledge of Language. In S. Davis (ed.), Connectionism: Theory and practice (Volume III of The Vancouver Studies in Cognitive Science. Oxford University press.   (Google)
Abstract: It is sometimes argued that if PDP networks can be trained to make correct judgements of grammaticality we have an existence proof that there is enough information in the stimulus to permit learning grammar by inductive means alone. This seems inconsistent superficially with Gold's theorem and at a deeper level with the fact that networks are designed on the basis of assumptions about the domain of the function to be learned. To clarify the issue I consider what we should learn from Gold's theorem, then go on to inquire into what it means to say that knowledge is domain specific. I first try sharpening the intuitive notion of domain specific knowledge by reviewing the alleged difference between processing limitatons due to shartage of resources vs shortages of knowledge. After rejecting different formulations of this idea, I suggest that a model is language specific if it transparently refer to entities and facts about language as opposed to entities and facts of more general mathematical domains. This is a useful but not necessary condition. I then suggest that a theory is domain specific if it belongs to a model family which is attuned in a law-like way to domain regularities. This leads to a comparison of PDP and parameter setting models of language learning. I conclude with a novel version of the poverty of stimulus argument.
Laakso, Aarre & Cottrell, Garrison W. (2006). Churchland on connectionism. In Brian L. Keeley (ed.), Paul Churchland. Cambridge: Cambridge University Press.   (Google)
Legg, C. R. (1988). Connectionism and physiological psychology: A marriage made in heaven? Philosophical Psychology 1:263-78.   (Google)
Litch, Mary (1997). Computation, connectionism and modelling the mind. Philosophical Psychology 10 (3):357-364.   (Google)
Abstract: 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 one presupposes: non-learning connectionist-based systems as simulated on digital computers are consistent with the computational model of the mind, whereas connectionist-based systems (/dynamical systems) qua analog systems are not
Litch, Mary (1999). Learning connectionist networks and the philosophy of psychology. Acta Analytica 22 (22):87-110.   (Google)
Lloyd, Dan (1994). Connectionist hysteria: Reducing a Freudian case study to a network model. Philosophy, Psychiatry, and Psychology 1 (2):69-88.   (Cited by 10 | Google)
Lloyd, Dan (1989). Parallel distributed processing and cognition: Only connect? In Simple Minds. MIT Press.   (Annotation | Google)
Lycan, William G. (1991). Homuncular functionalism meets PDP. In William Ramsey, Stephen P. Stich & D. Rumelhart (eds.), Philosophy and Connectionist Theory. Lawrence Erlbaum.   (Cited by 7 | Annotation | Google)
Macdonald, C. (ed.) (1995). Connectionism: Debates on Psychological Explanation. Blackwell.   (Cited by 36 | Google)
McLaughlin, Brian P. (1987). Tye on connectionism. Southern Journal of Philosophy (Suppl.) 185:185-193.   (Cited by 2 | Google)
Mills, Stephen L. (1993). Wittgenstein and connectionism: A significant complementarity? Philosophy 34:137-157.   (Cited by 4 | Google)
Miscevic, Nenad (1994). Connectionism and epistemic value. Acta Analytica 12 (12):19-37.   (Google)
Nenon, Thomas J. (1994). Connectionism and phenomenology. In Phenomenology of the Cultural Disciplines. Dordrecht: Kluwer.   (Google)
Niklasson, L. F. & van Gelder, Tim (online). Can connectionist models exhibit non-classical structure sensitivity?   (Cited by 30 | Google | More links)
Abstract: Department of Computer Science Philosophy Program, Research School of Social Sciences University of Skövde, S-54128, SWEDEN Australian National University, Canberra ACT 0200
O'Brien, Gerard & Opie, Jonathan (2002). Radical connectionism: Thinking with (not in) language. Language and Communication 22 (3):313-329.   (Cited by 12 | Google | More links)
Abstract: In this paper we defend a position we call radical connectionism. Radical connectionism claims that cognition _never_ implicates an internal symbolic medium, not even when natural language plays a part in our thought processes. On the face of it, such a position renders the human capacity for abstract thought quite mysterious. However, we argue that connectionism is committed to an analog conception of neural computation, and that representation of the abstract is no more problematic for a system of analog vehicles than for a symbol system. Natural language is therefore not required as a representational medium for abstract thought. Since natural language is arguably not a representational medium _at all_, but a conventionally governed scheme of communicative signals, we suggest that the role of internalised (i.e., self- directed) language is best conceived in terms of the coordination and control of cognitive activities within the brain
Piccinini, Gualtiero (2007). Connectionist computation. In Gualtiero Piccinini (ed.), Proceedings of the 2007 International Joint Conference on Neural Networks.   (Google)
Abstract: The following three theses are inconsistent: (1) (Paradigmatic) connectionist systems perform computations. (2) Performing computations requires executing programs. (3) Connectionist systems do not execute programs. Many authors embrace (2). This leads them to a dilemma: either connectionist systems execute programs or they don't compute. Accordingly, some authors attempt to deny (1), while others attempt to deny (3). But as I will argue, there are compelling reasons to accept both (1) and (3). So, we should replace (2) with a more satisfactory account of computation. Once we do, we can see more clearly what is peculiar to connectionist computation.
Place, Ullin T. (1999). Connectionism and the problem of consciousness. Acta Analytica 22 (22):197-226.   (Google)
Plunkett, Kim (2001). Connectionism today. Synthese 129 (2):185-194.   (Cited by 2 | Google | More links)
Abstract:   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
Ramsey, William & Stich, Stephen P. (1990). Connectionism and three levels of nativism. Synthese 82 (2):177-205.   (Cited by 14 | Annotation | Google | More links)
Abstract:   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 to develop networks that model various aspects of human linguistic behavior. Finally, we explore the implications of this research for Chomsky's arguments. Our claim is that the relation between connectionism and Chomsky's views on innate knowledge is more complicated than many have assumed, and that even if these models enjoy considerable success the threat they pose for linguistic nativism is small
Ramsey, William; Stich, Stephen P. & Rumelhart, D. M. (eds.) (1991). Philosophy and Connectionist Theory. Lawrence Erlbaum.   (Cited by 46 | Google)
Abstract: 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 inspired by the network-like architecture of the brain. Because of their unique architecture and style of processing, connectionist systems are generally regarded as radically different from the more traditional symbol manipulation models. This collection was designed to provide philosophers who have been working in the area of cognitive science with a forum for expressing their views on these recent developments. Because the symbol-manipulating paradigm has been so important to the work of contemporary philosophers, many have watched the emergence of connectionism with considerable interest. The contributors take very different stands toward connectionism, but all agree that the potential exists for a radical shift in the way many philosophers think of various aspects of cognition. Exploring this potential and other philosophical dimensions of connectionist research is the aim of this volume
Rosenberg, Jay F. (1989). Connectionism and cognition. Bielefeld Report.   (Cited by 7 | Annotation | Google)
Sehon, Scott R. (1998). Connectionism and the causal theory of action explanation. Philosophical Psychology 11 (4):511-532.   (Cited by 2 | Google)
Abstract: 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 not committed to the falsity of such connectionist models. The paper concludes that common sense psychology is not committed to the identification of mental states with functionally discrete causes of behavior, and hence that common sense psychology is not committed to the causal account of action explanation
Shanon, Benny (1992). Are connectionist models cognitive? Philosophical Psychology 5 (3):235-255.   (Cited by 5 | Annotation | Google)
Abstract: In their critique of connectionist models Fodor and Pylyshyn (1988) dismiss such models as not being cognitive or psychological. Evaluating Fodor and Pylyshyn's critique requires examining what is required in characterizating models as 'cognitive'. The present discussion examines the various senses of this term. It argues the answer to the title question seems to vary with these different senses. Indeed, by one sense of the term, neither representa-tionalism nor connectionism is cognitive. General ramifications of such an appraisal are discussed and alternative avenues for cognitive research are suggested
Smith, Barry (1997). The connectionist mind: A study of Hayekian psychology. In Stephen F. Frowen (ed.), Hayek: Economist and Social Philosopher: A Critical Retrospect. St. Martin's Press.   (Cited by 16 | Google | More links)
Abstract: Introduction I shall begin my remarks with some discussion of recent work in cognitive science, and the participants in this meeting might find it useful to note that I might equally well have chosen as title of my paper something like 'Artificial Intelligence and the Free Market Order'. They might care to note also that I am, as far as the achievements and goals of research in artificial intelligence are concerned, something of a sceptic. My appeal to cognitive science in what follows is designed to serve clarificatory ends, and to raise new questions, of a sort which will become clear as the paper progresses
Stark, Herman E. (1994). Connectionism and the form of rational norms. Acta Analytica 12 (12):39-53.   (Cited by 2 | Google)
Sterelny, Kim (1990). Connectionism. In The Representational Theory of Mind. Blackwell.   (Google)
Thagard, Paul R. (1989). Connectionism and epistemology: Goldman on Winner-take-all networks. Philosophia 19 (2-3):189-196.   (Cited by 1 | Google | More links)
Tienson, John L. (1987). Introduction to connectionism. Southern Journal of Philosophy (Suppl.) 1:1-16.   (Cited by 15 | Google)
van Gelder, Tim (1993). Connectionism and the mind-body problem: Exposing the distinction between mind and cognition. Artificial Intelligence Review 7:355-369.   (Google)