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21Taming type-2 tigers: A nonmonotonic strategyBehavioral and Brain Sciences 20 (1): 66-67. 1997.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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23Some counter-examples to page's notion of “localist”Behavioral and Brain Sciences 23 (4): 470-471. 2000.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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79Peter Novak, Mental Symbols: A Defence of the Classical Theory of Mind (review)Minds and Machines 11 (1): 148-150. 2001.
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67Moving the goal posts: A reply to Dawson and Piercey (review)Minds and Machines 16 (4): 471-478. 2006.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 …Read more
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57PDP networks can provide models that are not mere implementations of classical theoriesPhilosophical Psychology 10 (1): 25-40. 1997.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 interp…Read more
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41The Curious Case of ConnectionismOpen Philosophy 2 (1): 190-205. 2019.Connectionist research first emerged in the 1940s. The first phase of connectionism attracted a certain amount of media attention, but scant philosophical interest. The phase came to an abrupt halt, due to the efforts of Minsky and Papert (1969), when they argued for the intrinsic limitations of the approach. In the mid-1980s connectionism saw a resurgence. This marked the beginning of the second phase of connectionist research. This phase did attract considerable philosophical attention. It was…Read more
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26A Computational Conundrum: “What is a Computer?” A Historical OverviewMinds and Machines 28 (3): 375-383. 2018.This introduction begins by posing the question that this Special Issue addresses and briefly considers historical precedents and why the issue is important. The discussion then moves on to the consideration of important milestones in the history of computing, up until the present time. A brief specification of the essential components of computational systems is then offered. The final section introduces the papers that are included in this volume.
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140What the #$*%! is a Subsymbol?Minds and Machines 10 (1): 1-14. 2000.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 Be…Read more
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237What the <0.70, 1.17, 0.99, 1.07> is a Symbol?Minds and Machines 18 (1): 93-105. 2008.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…Read more
Istvan Stephen Norman Berkeley
The University of Louisiana At Lafayette
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The University of Louisiana At LafayetteAssociate Professor
Areas of Specialization
Science, Logic, and Mathematics |
Other Academic Areas |