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  1. István S. N. Berkeley, Some Myths of Connectionism.
    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, (...)
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  2. Istvan S. N. Berkeley, What is Connectionism?
    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 (...)
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  3. Istvan S. N. Berkeley (2008). What the is a Symbol? Minds and Machines 18 (1).
    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 (...)
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  4. Istvan S. N. Berkeley (2006). Moving the Goal Posts: A Reply to Dawson and Piercey. [REVIEW] Minds and Machines 16 (4):471-478.
    Berkeley [Minds Machines 10 (2000) 1] described a methodology that showed the subsymbolic nature of an artificial neural network system that had been trained on a logic problem, originally described by Bechtel and Abrahamsen [Connectionism and the mind. Blackwells, Cambridge, MA, 1991]. It was also claimed in the conclusion of this paper that the evidence was suggestive that the network might, in fact, count as a symbolic system. Dawson and Piercey [Minds Machines 11 (2001) 197] took issue with this latter (...)
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  5. Istvan S. N. Berkeley (2001). Peter Novak, Mental Symbols: A Defence of the Classical Theory of Mind. Studies in Cognitive Systems 19, Dordrecht, Netherlands: Kluwer Academic Publishers, 1997, XXII + 266 Pp., $114.00, ISBN 0-7923-4370-. [REVIEW] Minds and Machines 11 (1):148-150.
  6. Istvan S. N. Berkeley (2000). Some Counter-Examples to Page's Notion of “Localist”. Behavioral and Brain Sciences 23 (4):470-471.
    In his target article Page proposes a definition of the term “localist.” In this commentary I argue that his definition does not serve to make a principled distinction, as the inclusion of vague terms make it susceptible to some problematic counterexamples.
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  7. Istvan S. N. Berkeley (2000). What the #$*%! Is a Subsymbol? Minds and Machines 10 (1):1-13.
    In 1988, Smolensky proposed that connectionist processing systems should be understood as operating at what he termed the `subsymbolic'' level. Subsymbolic systems should be understood by comparing them to symbolic systems, in Smolensky''s view. Up until recently, there have been real problems with analyzing and interpreting the operation of connectionist systems which have undergone training. However, recently published work on a network trained on a set of logic problems originally studied by Bechtel and Abrahamsen (1991) seems to offer the potential (...)
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  8. Istvan S. N. Berkeley, Connectionism Reconsidered: Minds, Machines and Models.
    In this paper the issue of drawing inferences about biological cognitive systems on the basis of connectionist simulations is addressed. In particular, the justification of inferences based on connectionist models trained using the backpropagation learning algorithm is examined. First it is noted that a justification commonly found in the philosophical literature is inapplicable. Then some general issues are raised about the relationships between models and biological systems. A way of conceiving the role of hidden units in connectionist networks is then (...)
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  9. Istvan S. N. Berkeley, A Revisionist History of Connectionism.
    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 (...)
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  10. István S. N. Berkeley (1997). Taming Type-2 Tigers: A Nonmonotonic Strategy. Behavioral and Brain Sciences 20 (1):66-67.
    Clark & Thornton are too hasty in their dismissal of uninformed learning; nonmonotonic processing units show considerable promise on type-2 tasks. I describe a simulation which succeeds on a “pure” type-2 problem. Another simulation challenges Clark & Thornton's claims about the serendipitous nature of solutions to type-2 problems.
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  11. Michael R. W. Dawson, D. A. Medler & Istvan S. N. Berkeley (1997). PDP Networks Can Provide Models That Are Not Mere Implementations of Classical Theories. Philosophical Psychology 10 (1):25-40.
    There is widespread belief that connectionist networks are dramatically different from classical or symbolic models. However, connectionists rarely test this belief by interpreting the internal structure of their nets. A new approach to interpreting networks was recently introduced by Berkeley et al. (1995). The current paper examines two implications of applying this method: (1) that the internal structure of a connectionist network can have a very classical appearance, and (2) that this interpretation can provide a cognitive theory that cannot be (...)
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  12. Barbara Abbott, Annette Herskovits, Philip L. Peterson, Alfred R. Mele, David J. Cole, Daniel Crevier, Francis Jeffry Pelletier, Istvan S. N. Berkeley, Brendan J. Kitts, Mike Brown & George Paliouras (1996). Book Reviews. [REVIEW] Minds and Machines 6 (2):239-285.
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  13. István S. N. Berkeley (1993). Uncertainty and Quality in Science for Policy Silvio O. Funtowicz and Jerome R. Ravetz Dordrecht, Holland: Kluwer Academic Publishers, 1990, Xii + 229 Pp., US$88.50. [REVIEW] Dialogue 32 (04):837-.
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