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  1. John A. Barnden & Kankanahalli Srinivas (1996). Quantification Without Variables in Connectionism. Minds and Machines 6 (2):173-201.
    Connectionist attention to variables has been too restricted in two ways. First, it has not exploited certain ways of doing without variables in the symbolic arena. One variable-avoidance method, that of logical combinators, is particularly well established there. Secondly, the attention has been largely restricted to variables in long-term rules embodied in connection weight patterns. However, short-lived bodies of information, such as sentence interpretations or inference products, may involve quantification. Therefore short-lived activation patterns may need to achieve the effect of (...)
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  2. Istvan S. Berkeley (2008). What the <0.70, 1.17, 0.99, 1.07> is a Symbol? Minds and Machines 18 (1):93-105.
    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 rise to (...)
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  3. 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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  4. 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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  5. Denny Borsboom & Ingmar Visser (2008). Semantic Cognition or Data Mining? Behavioral and Brain Sciences 31 (6):714-715.
    We argue that neural networks for semantic cognition, as proposed by Rogers & McClelland (R&M), do not acquire semantics and therefore cannot be the basis for a theory of semantic cognition. The reason is that the neural networks simply perform statistical categorization procedures, and these do not require any semantics for their successful operation. We conclude that this has severe consequences for the semantic cognition views of R&M.
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  6. David J. Chalmers (1992). Subsymbolic Computation and the Chinese Room. In J. Dinsmore (ed.), The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum. 25--48.
    More than a decade ago, philosopher John Searle started a long-running controversy with his paper “Minds, Brains, and Programs” (Searle, 1980a), an attack on the ambitious claims of artificial intelligence (AI). With his now famous _Chinese Room_ argument, Searle claimed to show that despite the best efforts of AI researchers, a computer could never recreate such vital properties of human mentality as intentionality, subjectivity, and understanding. The AI research program is based on the underlying assumption that all important aspects of (...)
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  7. Patricia S. Churchland, Ilya B. Farber & Will Peterman (2001). The View From Here: The Nonsymbolic Structure of Spatial Representation. In Joao Branquinho (ed.), The Foundations of Cognitive Science. Oxford: Clarendon Press.
  8. Andy Clark (1993). Superpositional Connectionism: A Reply to Marinov. [REVIEW] Minds and Machines 3 (3):271-81.
    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 fundamental (...)
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  9. Axel Cleeremans (1998). The Other Hard Problem: How to Bridge the Gap Between Subsymbolic and Symbolic Cognition. Behavioral and Brain Sciences 21 (1):22-23.
    The constructivist notion that features are purely functional is incompatible with the classical computational metaphor of mind. I suggest that the discontent expressed by Schyns, Goldstone and Thibaut about fixed-features theories of categorization reflects the growing impact of connectionism, and show how their perspective is similar to recent research on implicit learning, consciousness, and development. A hard problem remains, however: How to bridge the gap between subsymbolic and symbolic cognition.
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  10. J. Dinsmore (ed.) (1992). The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum.
    This book records the thoughts of researchers -- from both computer science and philosophy -- on resolving the debate between the symbolic and connectionist...
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  11. Hubert L. Dreyfus (2002). Refocusing the Question: Can There Be Skillful Coping Without Propositional Representations or Brain Representations? [REVIEW] Phenomenology and the Cognitive Sciences 1 (4):413-25.
  12. Juan Felipe Martinez Florez (2012). Dietmar Heinke and Eirini Mavritsaki (Eds): Computational Modelling in Behavioural Neuroscience. [REVIEW] Minds and Machines 22 (1):57-60.
    Dietmar Heinke and Eirini Mavritsaki (eds): Computational Modelling in Behavioural Neuroscience Content Type Journal Article Category Book Review Pages 57-60 DOI 10.1007/s11023-011-9265-8 Authors Juan Felipe Martinez Florez, Institute of Psychology, Universidad del Valle, Campus Universitario Melndez, Ed. 388, Of. 4017, Cali, Colombia Journal Minds and Machines Online ISSN 1572-8641 Print ISSN 0924-6495 Journal Volume Volume 22 Journal Issue Volume 22, Number 1.
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  13. Peter Gardenfors (2004). Conceptual Spaces as a Framework for Knowledge Representation. Mind and Matter 2 (2):9-27.
    The dominating models of information processes have been based on symbolic representations of information and knowledge. During the last decades, a variety of non-symbolic models have been proposed as superior. The prime examples of models within the non-symbolic approach are neural networks. However, to a large extent they lack a higher-level theory of representation. In this paper, conceptual spaces are suggested as an appropriate framework for non- symbolic models. Conceptual spaces consist of a number of 'quality dimensions' that often are (...)
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  14. Douglas R. Hofstadter (1983). Artificial Intelligence: Subcognition as Computation. In Fritz Machlup (ed.), The Study of Information: Interdisciplinary Messages. Wiley.
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  15. Fritz Machlup (ed.) (1983). The Study of Information: Interdisciplinary Messages. Wiley.
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  16. Marin Marinov (1993). On the Spuriousness of the Symbolic/Subsymbolic Distinction. Minds and Machines 3 (3):253-70.
    The article criticises the attempt to establish connectionism as an alternative theory of human cognitive architecture through the introduction of thesymbolic/subsymbolic distinction (Smolensky, 1988). The reasons for the introduction of this distinction are discussed and found to be unconvincing. It is shown that thebrittleness problem has been solved for a large class ofsymbolic learning systems, e.g. the class oftop-down induction of decision-trees (TDIDT) learning systems. Also, the process of articulating expert knowledge in rules seems quite practical for many important domains, (...)
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  17. Arthur B. Markman & Eric Dietrich (2000). Extending the Classical View of Representation. Trends in Cognitive Sciences 4 (12):470-475.
    Representation is a central part of models in cognitive science, but recently this idea has come under attack. Researchers advocating perceptual symbol systems, situated action, embodied cognition, and dynamical systems have argued against central assumptions of the classical representational approach to mind. We review the core assumptions of the dominant view of representation and the four suggested alternatives. We argue that representation should remain a core part of cognitive science, but that the insights from these alternative approaches must be incorporated (...)
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  18. Jonathan Opie & Gerard O'Brien (2006). How Do Connectionist Networks Compute? Cognitive Processing 7 (1):30-41.
    Although connectionism is advocated by its proponents as an alternative to the classical computational theory of mind, doubts persist about its _computational_ credentials. Our aim is to dispel these doubts by explaining how connectionist networks compute. We first develop a generic account of computation—no easy task, because computation, like almost every other foundational concept in cognitive science, has resisted canonical definition. We opt for a characterisation that does justice to the explanatory role of computation in cognitive science. Next we examine (...)
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  19. Ron Sun Todd Peterson, A Subsymbolic Symbolic Model for Learning Sequential Navigation.
    To deal with reactive sequential decision tasks we present a learning model Clarion which is a hybrid connectionist model consisting of both localist and dis tributed representations based on the two level ap proach proposed in Sun The model learns and utilizes procedural and declarative knowledge tapping into the synergy of the two types of processes It uni es neural reinforcement and symbolic methods to perform on line bottom up learning Experiments in various situations are reported that shed light on (...)
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  20. Erich Rast (2013). Review of Fenstad's "Grammar, Geometry & Brain&Quot;. [REVIEW] Studia Logica 101 (5).
    In this small book logician and mathematician Jens Erik Fenstad addresses some of the most important foundational questions of linguistics: What should a theory of meaning look like and how might we provide the missing link between meaning theory and our knowledge of how the brain works? The author’s answer is twofold. On the one hand, he suggests that logical semantics in the Montague tradition and other broadly conceived symbolic approaches do not suffice. On the other hand, he does not (...)
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  21. Jay F. Rosenberg (1990). Treating Connectionism Properly: Reflections on Smolensky. Psychological Research 52.
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  22. Paul Smolensky (1988). On the Proper Treatment of Connectionism. Behavioral and Brain Sciences 11 (1):1-23.
    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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  23. Paul Smolensky (1987). Connectionist, Symbolic, and the Brain. AI Review 1:95-109.
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  24. Damian G. Stephen & Guy van Orden (2012). Searching for General Principles in Cognitive Performance: Reply to Commentators. Topics in Cognitive Science 4 (1):94-102.
    The commentators expressed concerns regarding the relevance and value of non-computational non-symbolic explanations of cognitive performance. But what counts as an “explanation” depends on the pre-theoretical assumptions behind the scenes of empirical science regarding the kinds of variables and relationships that are sought out in the first place, and some of the present disagreements stem from incommensurate assumptions. Traditional cognitive science presumes cognition to be a decomposable system of components interacting according to computational rules to generate cognitive performances (i.e., component-dominant (...)
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