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Philosophy of artificial intelligence :: Philosophy of connectionism :: Philosophy of connectionism, misc.

• William Bechtel (1996). What should a connectionist philosophy of science look like?   (Abstract & more)
  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
  Additional links for this entry:
  http://mechanism.ucsd.edu/~bill/research/CHURCHLA.pdf
  http://citeseer.ist.psu.edu/67036.html
  
• Istvan Berkeley (ms). A revisionist history of connectionism.   (Abstract & more)
  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
• Istvan Berkeley (online). What is connectionism?   (Abstract & more)
  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
• István Berkeley (online). Some myths of connectionism.   (Abstract & more)
  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
• Susan Blackmore (2003). The case of the mysterious mind: Review of Radiant Cool, by Dan Lloyd.   (More)
  Additional links for this entry:
  http://www.susanblackmore.co.uk/journalism/NSradiantcool03.htm
  
• Patricia Churchland & Terrence Sejnowski (1989). Neural representation and neural computation.   (More)
  Additional links for this entry:
  http://links.jstor.org/sici?sici=1520-8583(1990)4<343:NRANC>2.0.CO;2-P
  http://www.jstor.org/stable/pdfplus/2214198.pdf
  http://www.jstor.org/sici?sici=1520-8583(1990)4<343:NRANC>2.0.CO;2-P
  
• Paul Churchland (1989). On the nature of explanation: A PDP approach.   (More)
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=114408
  http://books.google.com/books?hl=en&lr=&id=LqszY4kBw98C&oi=fnd&pg=PA75&ots=qXgwkOTy04&sig=yMyj0EhkKs802wSnrOjoUyDdztg
  http://adsabs.harvard.edu/abs/1990PhyD...42..281C
  
• Andy Clark (1990). Connectionism, competence and explanation.   (Abstract & more)
  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
  Additional links for this entry:
  http://www.era.lib.ed.ac.uk/handle/1842/1326?mode=simple
  http://www.jstor.org/sici?sici=0007-0882(199006)41:2<195:CCAE>2.0.CO;2-U
  http://bjps.oxfordjournals.org/cgi/content/abstract/41/2/195
  http://bjps.oxfordjournals.org/cgi/reprint/41/2/195
  http://links.jstor.org/sici?sici=0007-0882(199006)41:2<195:CCAE>2.0.CO;2-U
  http://www.jstor.org/stable/pdfplus/687772.pdf
  
• Andy Clark (1989). Microfunctionalism: Connectionism and the Scientific Explanation of Mental States.   (Abstract & more)
  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)
  Additional links for this entry:
  http://www.era.lib.ed.ac.uk/handle/1842/1332
  https://www.era.lib.ed.ac.uk/handle/1842/1332
  http://www.cogs.indiana.edu/andy/microfx.pdf
  http://www.era.lib.ed.ac.uk/handle/1842/1332?mode=simple
  http://www.philosophy.ed.ac.uk/staff/clark/pubs/microfx.pdf
  
• Andy Clark & Chris Eliasmith (2002). Philosophical issues in brain theory and connectionism.   (More)
  Additional links for this entry:
  http://watarts.uwaterloo.ca/~celiasmi/Papers/withclark.html
  
• Jack Copeland (1996). On Alan Turing's anticipation of connectionism.   (Abstract & more)
  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
  Additional links for this entry:
  http://citeseer.ist.psu.edu/389627.html
  http://www.phil.canterbury.ac.nz/personal_pages/jack_copeland/pub/turing3.pdf
  http://www.springerlink.com/content/v3571g00l3504874/fulltext.pdf
  http://www.springerlink.com/index/V3571G00L3504874.pdf
  
• Robert Hadley (1999). Connectionism and novel combinations of skills: Implications for cognitive architecture.   (Abstract & more)
  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
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=596712.596817
  http://www.kluweronline.com/article.asp?PIPS=206285&PDF=1
  http://www.springerlink.com/content/l64416n38j41q278/fulltext.pdf
  http://www.springerlink.com/index/L64416N38J41Q278.pdf
  http://www.ingentaconnect.com/content/klu/mind/1999/00000009/00000002/00206285
  
• L. F. Niklasson & Tim van Gelder (online). Can connectionist models exhibit non-classical structure sensitivity?   (Abstract & more)
  Abstract: Department of Computer Science Philosophy Program, Research School of Social Sciences University of Skövde, S-54128, SWEDEN Australian National University, Canberra ACT 0200
  Additional links for this entry:
  http://www.phil.canterbury.ac.nz/tom_bestor/e-texts/iklasson and van Gelder - connectionist structure sensitivity.pdf
  http://citeseer.ist.psu.edu/niklasson94can.html
  http://citeseer.ist.psu.edu/326140.html
  http://www.phil.canterbury.ac.nz/tom_bestor/e-texts/Niklassen and van Gelder - Can connectionist models exhibit non-classical structure sensitivity.pdf
  http://www.his.se/ida/~lars/PUBLICATIONS/cogs94.ps
  http://www.philosophy.unimelb.edu.au/tgelder/papers/cogs94.pdf
  
• Gerard O'Brien & Jonathan Opie (2002). Radical connectionism: Thinking with (not in) language.   (Abstract & more)
  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
  Additional links for this entry:
  http://digital.library.adelaide.edu.au/dspace/handle/2440/16139
  http://linkinghub.elsevier.com/retrieve/pii/S0271530902000101
  http://www.ingentaconnect.com/content/els/02715309/2002/00000022/00000003/art00010
  
• Gualtiero Piccinini (2007). Connectionist computation.   (Abstract & more)
  Abstract: Why paradigmatic connectionist systems perform computations even though they do not execute programs. [new 12/06]
• William Ramsey & Stephen Stich (1990). Connectionism and three levels of nativism.   (Abstract & more)
  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
  Additional links for this entry:
  http://www.springerlink.com/index/P66N338HJQ44332P.pdf
  http://www.springerlink.com/content/p66n338hjq44332p/fulltext.pdf
  
• Barry Smith (1997). The connectionist mind: A study of Hayekian psychology.   (Abstract & more)
  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
  Additional links for this entry:
  http://cogprints.ecs.soton.ac.uk/archive/00000306/
  http://cogprints.org/306/0/connect.html
  http://wings.buffalo.edu/philosophy/faculty/smith/articles/HAYEK.HTM
  http://cogprints.org/306/00/connect.html
  http://wings.buffalo.edu/academic/department/philosophy/faculty/smith/articles/HAYEK.HTM
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:306
  
• Tim van Gelder (1993). Connectionism and the mind-body problem: Exposing the distinction between mind and cognition.   (More)
  Additional links for this entry:
  http://www.arts.unimelb.edu.au/~tgelder/papers/SNCC.html
  

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