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  1. Against neuroclassicism: On the perils of armchair neuroscience.Alex Morgan - 2022 - Mind and Language 37 (3):329-355.
    Neuroclassicism is the view that cognition is explained by “classical” computing mechanisms in the nervous system that exhibit a clear demarcation between processing machinery and read–write memory. The psychologist C. R. Gallistel has mounted a sophisticated defense of neuroclassicism by drawing from ethology and computability theory to argue that animal brains necessarily contain read–write memory mechanisms. This argument threatens to undermine the “connectionist” orthodoxy in contemporary neuroscience, which does not seem to recognize any such mechanisms. In this paper I argue (...)
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  2. A Defense of Meaning Eliminativism: A Connectionist Approach.Tolgahan Toy - 2022 - Dissertation, Middle East Technical University
    The standard approach to model how human beings understand natural languages is the symbolic, compositional approach according to which the meaning of a complex expression is a function of the meanings of its constituents. In other words, meaning plays a fundamental role in the model. In this work, because of the polysemous, flexible, dynamic, and contextual structure of natural languages, this approach is rejected. Instead, a connectionist model which eliminates the concept of meaning is proposed.
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  3. Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map.Birgitta Dresp-Langley - 2021 - Symmetry 13:299.
    Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this (...)
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  4. Predictive processing and anti-representationalism.Marco Facchin - 2021 - Synthese 199 (3-4):11609-11642.
    Many philosophers claim that the neurocomputational framework of predictive processing entails a globally inferentialist and representationalist view of cognition. Here, I contend that this is not correct. I argue that, given the theoretical commitments these philosophers endorse, no structure within predictive processing systems can be rightfully identified as a representational vehicle. To do so, I first examine some of the theoretical commitments these philosophers share, and show that these commitments provide a set of necessary conditions the satisfaction of which allows (...)
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  5. The quantization error in a Self-Organizing Map as a contrast and color specific indicator of single-pixel change in large random patterns.Birgitta Dresp-Langley - 2019 - Neural Networks 120:116-128..
    The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-take-all learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in medical time series and in time series of satellite images. Here, the functional properties of the quantization error in SOM are explored further to show that the metric is capable of reliably discriminating between the finest differences in local contrast intensities and contrast signs. While this capability of the QE is (...)
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  6. Neural Representations in Context.Alessio Plebe & Vivian M. De La Cruz - 2019 - In Antonino Pennisi & Alessandra Falzone (eds.), The Extended Theory of Cognitive Creativity: Interdisciplinary Approaches to Performativity. Springer Verlag. pp. 285-300.
    In recent years, a number of different disciplines have begun to investigate the fundamental role context appears to play in a number of cognitive phenomena. Traditionally, linguistics, and the fields of communication and pragmatics in particular, have been the areas that have focused the most on contextual effects. Context has increasingly been studied for its role in influencing mental concepts, for some scholars being considered constitutive for most – if not all – concepts. Cognitive neuroscience is now starting to consider (...)
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  7. Content and misrepresentation in hierarchical generative models.Alex Kiefer & Jakob Hohwy - 2018 - Synthese 195 (6):2387-2415.
    In this paper, we consider how certain longstanding philosophical questions about mental representation may be answered on the assumption that cognitive and perceptual systems implement hierarchical generative models, such as those discussed within the prediction error minimization framework. We build on existing treatments of representation via structural resemblance, such as those in Gładziejewski :559–582, 2016) and Gładziejewski and Miłkowski, to argue for a representationalist interpretation of the PEM framework. We further motivate the proposed approach to content by arguing that it (...)
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  8. Computational Dynamics of Natural Information Morphology, Discretely Continuous.Gordana Dodig-Crnkovic - 2017 - Philosophies 2 (4):23.
    This paper presents a theoretical study of the binary oppositions underlying the mechanisms of natural computation understood as dynamical processes on natural information morphologies. Of special interest are the oppositions of discrete vs. continuous, structure vs. process, and differentiation vs. integration. The framework used is that of computing nature, where all natural processes at different levels of organisation are computations over informational structures. The interactions at different levels of granularity/organisation in nature, and the character of the phenomena that unfold through (...)
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  9. Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different Levels of Representation.Antonio Lieto, Antonio Chella & Marcello Frixione - 2017 - Biologically Inspired Cognitive Architectures 19:1-9.
    During the last decades, many cognitive architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR [35]) adopt a classical symbolic approach, some (e.g. LEABRA[ 48]) are based on a purely connectionist model, while others (e.g. CLARION [59]) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are (...)
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  10. Polytopes as vehicles of informational content in feedforward neural networks.Feraz Azhar - 2016 - Philosophical Psychology 29 (5):697-716.
    Localizing content in neural networks provides a bridge to understanding the way in which the brain stores and processes information. In this paper, I propose the existence of polytopes in the state space of the hidden layer of feedforward neural networks as vehicles of content. I analyze these geometrical structures from an information-theoretic point of view, invoking mutual information to help define the content stored within them. I establish how this proposal addresses the problem of misclassification and provide a novel (...)
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  11. There’s Plenty of Boole at the Bottom: A Reversible CA Against Information Entropy.Francesco Berto, Jacopo Tagliabue & Gabriele Rossi - 2016 - Minds and Machines 26 (4):341-357.
    “There’s Plenty of Room at the Bottom”, said the title of Richard Feynman’s 1959 seminal conference at the California Institute of Technology. Fifty years on, nanotechnologies have led computer scientists to pay close attention to the links between physical reality and information processing. Not all the physical requirements of optimal computation are captured by traditional models—one still largely missing is reversibility. The dynamic laws of physics are reversible at microphysical level, distinct initial states of a system leading to distinct final (...)
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  12. The epistemological import of morphological content.Jack C. Lyons - 2014 - Philosophical Studies 169 (3):537-547.
    Morphological content (MC) is content that is implicit in the standing structure of the cognitive system. Henderson and Horgan claim that MC plays a distinctive epistemological role unrecognized by traditional epistemic theories. I consider the possibilities that MC plays this role either in central cognition or in peripheral modules. I argue that the peripheral MC does not play an interesting epistemological role and that the central MC is already recognized by traditional theories.
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  13. Robot Pain.Simon van Rysewyk - 2014 - International Journal of Synthetic Emotions 4 (2):22-33.
    Functionalism of robot pain claims that what is definitive of robot pain is functional role, defined as the causal relations pain has to noxious stimuli, behavior and other subjective states. Here, I propose that the only way to theorize role-functionalism of robot pain is in terms of type-identity theory. I argue that what makes a state pain for a neuro-robot at a time is the functional role it has in the robot at the time, and this state is type identical (...)
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  14. The Linguistic Subversion of Mental Representation.Whit Schonbein - 2012 - Minds and Machines 22 (3):235-262.
    Embedded and embodied approaches to cognition urge that (1) complicated internal representations may be avoided by letting features of the environment drive behavior, and (2) environmental structures can play an enabling role in cognition, allowing prior cognitive processes to solve novel tasks. Such approaches are thus in a natural position to oppose the ‘thesis of linguistic structuring’: The claim that the ability to use language results in a wholesale recapitulation of linguistic structure in onboard mental representation. Prominent examples of researchers (...)
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  15. The Waning of Materialism. Edited by R. Koons and G. Bealer. (OUP 2010). [REVIEW]David Yates - 2012 - Philosophical Quarterly 62 (247):420-422.
  16. Representation Reconsidered. [REVIEW]Daniel D. Hutto - 2011 - Philosophical Psychology 24 (1):135-139.
  17. Criteria for the Design and Evaluation of Cognitive Architectures.Sashank Varma - 2011 - Cognitive Science 35 (7):1329-1351.
    Cognitive architectures are unified theories of cognition that take the form of computational formalisms. They support computational models that collectively account for large numbers of empirical regularities using small numbers of computational mechanisms. Empirical coverage and parsimony are the most prominent criteria by which architectures are designed and evaluated, but they are not the only ones. This paper considers three additional criteria that have been comparatively undertheorized. (a) Successful architectures possess subjective and intersubjective meaning, making cognition comprehensible to individual cognitive (...)
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  18. Unifying conceptual spaces: Concept formation in musical creative systems. [REVIEW]Alex McLean - 2010 - Minds and Machines 20 (4):503-532.
    We examine Gärdenfors’ theory of conceptual spaces, a geometrical form of knowledge representation (Conceptual spaces: The geometry of thought, MIT Press, Cambridge, 2000 ), in the context of the general Creative Systems Framework introduced by Wiggins (J Knowl Based Syst 19(7):449–458, 2006a ; New Generation Comput 24(3):209–222, 2006 b ). Gärdenfors’ theory offers a way of bridging the traditional divide between symbolic and sub-symbolic representations, as well as the gap between representational formalism and meaning as perceived by human minds. We (...)
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  19. Neural-Symbolic Cognitive Reasoning.Artur S. D'Avila Garcez, Luís C. Lamb & Dov M. Gabbay - 2009 - Berlin and Heidelberg: Springer.
    This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.
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  20. Systematicity redux.Brian P. McLaughlin - 2009 - Synthese 170 (2):251-274.
    One of the main challenges that Jerry Fodor and Zenon Pylyshyn (Cognition 28:3–71, 1988) posed for any connectionist theory of cognitive architecture is to explain the systematicity of thought without implementing a Language of Thought (LOT) architecture. The systematicity challenge presents a dilemma: if connectionism cannot explain the systematicity of thought, then it fails to offer an adequate theory of cognitive architecture; and if it explains the systematicity of thought by implementing a LOT architecture, then it fails to offer an (...)
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  21. Characterizing the effect of seating arrangement on classroom learning using neural networks.C. Monterola, R. M. Roxas & S. Carreon-Monterola - 2009 - Complexity 14 (4):26-33.
  22. The role of representation in computation.Gerard O'Brien & Jon Opie - 2009 - Cognitive Processing 10 (1):53-62.
    Reformers urge that representation no longer earns its explanatory keep in cognitive science, and that it is time to discard this troublesome concept. In contrast, we hold that without representation cognitive science is utterly bereft of tools for explaining natural intelligence. In order to defend the latter position, we focus on the explanatory role of representation in computation. We examine how the methods of digital and analog computation are used to model a relatively simple target system, and show that representation (...)
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  23. What the <0.70, 1.17, 0.99, 1.07> is a Symbol?Istvan S. N. Berkeley - 2008 - 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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  24. Semantic cognition or data mining?Denny Borsboom & Ingmar Visser - 2008 - 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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  25. Semantic cognition: Distributed, but then attractive.Emilio Kropff & Alessandro Treves - 2008 - Behavioral and Brain Sciences 31 (6):718-719.
    The parallel distributed processing (PDP) perspective brings forward the important point that all semantic phenomena are based on analog underlying mechanisms, involving the weighted summation of multiple inputs by individual neurons. It falls short of indicating, however, how the essentially discrete nature of semantic processing may emerge at the cognitive level. Bridging this gap probably requires attractor networks.
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  26. Concepts, correlations, and some challenges for connectionist cognition.Gary F. Marcus & Frank C. Keil - 2008 - Behavioral and Brain Sciences 31 (6):722-723.
    Rogers & McClelland's (R&M's) précis represents an important effort to address key issues in concepts and categorization, but few of the simulations deliver what is promised. We argue that the models are seriously underconstrained, importantly incomplete, and psychologically implausible; more broadly, R&M dwell too heavily on the apparent successes without comparable concern for limitations already noted in the literature.
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  27. Bayesian models and simulations in cognitive science.Giuseppe Boccignone & Roberto Cordeschi - 2007 - Workshop Models and Simulations 2, Tillburg, NL.
    Bayesian models can be related to cognitive processes in a variety of ways that can be usefully understood in terms of Marr's distinction among three levels of explanation: computational, algorithmic and implementation. In this note, we discuss how an integrated probabilistic account of the different levels of explanation in cognitive science is resulting, at least for the current research practice, in a sort of unpredicted epistemological shift with respect to Marr's original proposal.
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  28. A critique of the similarity space theory of concepts.Christopher Gauker - 2007 - Mind and Language 22 (4):317–345.
    A similarity space is a hyperspace in which the dimensions represent various dimensions on which objects may differ. The similarity space theory of concepts is the thesis that concepts are regions of similarity spaces that are somehow realized in the brain. Proponents of such a theory of concepts include Paul Churchland and Peter Gärdenfors. This paper argues that the similarity space theory of concepts is mistaken because regions of similarity spaces cannot serve as the components of judgments. It emerges that (...)
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  29. Kenneth Aizawa, The Systematicity Arguments, Studies in Brain and Mind.Steven Phillips - 2007 - Minds and Machines 17 (3):357-360.
  30. Content and Its vehicles in connectionist systems.Nicholas Shea - 2007 - Mind and Language 22 (3):246–269.
    This paper advocates explicitness about the type of entity to be considered as content- bearing in connectionist systems; it makes a positive proposal about how vehicles of content should be individuated; and it deploys that proposal to argue in favour of representation in connectionist systems. The proposal is that the vehicles of content in some connectionist systems are clusters in the state space of a hidden layer. Attributing content to such vehicles is required to vindicate the standard explanation for some (...)
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  31. Empiricism and state-space semantics.Jesse J. Prinz - 2006 - In Brian L Keeley (ed.), Paul Churchland. Cambridge: Cambridge University Press.
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  32. Neural networks discover a near-identity relation to distinguish simple syntactic forms.Thomas R. Shultz & Alan C. Bale - 2006 - Minds and Machines 16 (2):107-139.
    Computer simulations show that an unstructured neural-network model [Shultz, T. R., & Bale, A. C. (2001). Infancy, 2, 501–536] covers the essential features␣of infant learning of simple grammars in an artificial language [Marcus, G. F., Vijayan, S., Bandi Rao, S., & Vishton, P. M. (1999). Science, 283, 77–80], and generalizes to examples both outside and inside of the range of training sentences. Knowledge-representation analyses confirm that these networks discover that duplicate words in the sentences are nearly identical and that they (...)
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  33. Paul Churchland.Brian L. Keeley (ed.) - 2005 - Cambridge: Cambridge University Press.
  34. Nonmonotonic Inferences and Neural Networks.Reinhard Blutner - 2004 - Synthese 142 (2):143-174.
    There is a gap between two different modes of computation: the symbolic mode and the subsymbolic (neuron-like) mode. The aim of this paper is to overcome this gap by viewing symbolism as a high-level description of the properties of (a class of) neural networks. Combining methods of algebraic semantics and non-monotonic logic, the possibility of integrating both modes of viewing cognition is demonstrated. The main results are (a) that certain activities of connectionist networks can be interpreted as non-monotonic inferences, and (...)
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  35. On the proper treatment of semantic systematicity.Robert F. Hadley - 2004 - Minds and Machines 14 (2):145-172.
    The past decade has witnessed the emergence of a novel stance on semantic representation, and its relationship to context sensitivity. Connectionist-minded philosophers, including Clark and van Gelder, have espoused the merits of viewing hidden-layer, context-sensitive representations as possessing semantic content, where this content is partially revealed via the representations'' position in vector space. In recent work, Bodén and Niklasson have incorporated a variant of this view of semantics within their conception of semantic systematicity. Moreover, Bodén and Niklasson contend that they (...)
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  36. Notes toward a structuralist theory of mental representation.Jonathan Opie & Gerard O'Brien - 2004 - In Hugh Clapin, Phillip Staines & Peter Slezak (eds.), Representation in Mind: New Approaches to Mental Representation. Elsevier. pp. 1--20.
    Any creature that must move around in its environment to find nutrients and mates, in order to survive and reproduce, faces the problem of sensorimotor control. A solution to this problem requires an on-board control mechanism that can shape the creature’s behaviour so as to render it “appropriate” to the conditions that obtain. There are at least three ways in which such a control mechanism can work, and Nature has exploited them all. The first and most basic way is for (...)
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  37. Structural content: A naturalistic approach to implicit belief.Paul Skokowski - 2004 - Philosophy of Science 71 (3):362-369.
    Various systems that learn are examined to show how content is carried in connections installed by a learning history. Agents do not explicitly use the content of such states in practical reasoning, yet the content plays an important role in explaining behavior, and the physical state carrying that content plays a role in causing behavior, given other occurrent beliefs and desires. This leads to an understanding of the environmental reasons which are the determinate content of these states, and leads to (...)
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  38. Connectionist semantics and the collateral information challenge.Francisco Calvo Garzón - 2003 - Mind and Language 18 (1):77-94.
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  39. Transcending Turing computability.B. J. Maclennan - 2003 - Minds and Machines 13 (1):3-22.
    It has been argued that neural networks and other forms of analog computation may transcend the limits of Turing-machine computation; proofs have been offered on both sides, subject to differing assumptions. In this article I argue that the important comparisons between the two models of computation are not so much mathematical as epistemological. The Turing-machine model makes assumptions about information representation and processing that are badly matched to the realities of natural computation (information representation and processing in or inspired by (...)
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  40. Varieties of representation in evolved and embodied neural networks.Pete Mandik - 2003 - Biology and Philosophy 18 (1):95-130.
    In this paper I discuss one of the key issuesin the philosophy of neuroscience:neurosemantics. The project of neurosemanticsinvolves explaining what it means for states ofneurons and neural systems to haverepresentational contents. Neurosemantics thusinvolves issues of common concern between thephilosophy of neuroscience and philosophy ofmind. I discuss a problem that arises foraccounts of representational content that Icall ``the economy problem'': the problem ofshowing that a candidate theory of mentalrepresentation can bear the work requiredwithin in the causal economy of a mind and (...)
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  41. The short-term dynamics within a network of connections is creative.William A. Phillips - 2003 - Behavioral and Brain Sciences 26 (6):752-753.
    Although visual long-term memory (VLTM) and visual short-term memory (VSTM) can be distinguished from each other (and from visual sensory storage [SS]), they are embodied within the same modality-specific brain regions, but in very different ways: VLTM as patterns of connectivity and VSTM as patterns of activity. Perception and VSTM do not “activate” VLTM. They use VLTM to create novel patterns of activity relevant to novel circumstances.
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  42. The explanatory need for mental representations in cognitive science.Barbara Von Eckardt - 2003 - Mind and Language 18 (4):427-439.
    Ramsey (1997) argues that connectionist representations 'do not earn their explanatory keep'. The aim of this paper is to examine the argument Ramsey gives to support that conclusion. In doing so, I identify two kinds of explanatory need—need relative to a possible explanation and need relative to a true explanation and argue that internal representations are not needed for either connectionist or nonconnectionist possible explanations but that it is quite likely that they are needed for true explanations. However, to show (...)
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  43. A defence of connectionism against the "syntactic" argument.Marcello Guarini - 2001 - Synthese 128 (3):287-317.
    In "Representations without Rules, Connectionism and the Syntactic Argument'', Kenneth Aizawa argues against the view that connectionist nets can be understood as processing representations without the use of representation-level rules, and he provides a positive characterization of how to interpret connectionist nets as following representation-level rules. He takes Terry Horgan and John Tienson to be the targets of his critique. The present paper marshals functional and methodological considerations, gleaned from the practice of cognitive modelling, to argue against Aizawa's characterization of (...)
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  44. Knowledge Representation: Two Kinds Of Emergence.Veikko Rantala - 2001 - Synthese 129 (2):195-209.
    Two different but closely related issues in current cognitive science will be considered in this essay. One is the controversial and extensively discussed question of how connectionist and symbolic representations of knowledge are related to each other. The other concerns the notion of connectionist learning and its relevance for the understanding of the distinction between propositional and nonpropositional knowledge. More specifically, I shall give an overview of a result in Rantala and Vadén (1994) establishing a limiting case correspondence between symbolic (...)
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  45. A critique of connectionist semantics.Jonathan A. Waskan - 2001 - Connection Science 13 (3):277-292.
  46. Further arguments in support of localist coding in connectionist networks.Jeffrey S. Bowers - 2000 - Behavioral and Brain Sciences 23 (4):471-471.
    Two additional sources of evidence are provided in support of localist coding within connectionist networks. First, only models with localist codes can currently represent multiple pieces of information simultaneously or represent order among a set of items on-line. Second, recent priming data appear problematic for theories that rely on distributed representations. However, a faulty argument advanced by Page is also pointed out.
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  47. State space semantics and conceptual similarity: Reply to Churchland.Francisco Calvo Garzón - 2000 - Philosophical Psychology 13 (1):77-95.
    Jerry Fodor and Ernest Lepore [(1992) Holism: a shopper's guide, Oxford: Blackwell; (1996) in R. McCauley (Ed.) The Churchlands and their critics , Cambridge: Blackwell] have launched a powerful attack against Paul Churchland's connectionist theory of semantics--also known as state space semantics. In one part of their attack, Fodor and Lepore argue that the architectural and functional idiosyncrasies of connectionist networks preclude us from articulating a notion of conceptual similarity applicable to state space semantics. Aarre Laakso and Gary Cottrell [(1998) (...)
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  48. A Connectionist Defence of the Inscrutability Thesis.Francisco Calvo Garzón - 2000 - Mind and Language 15 (5):465-480.
    This paper consists of four parts. In section 1, I shall offer a strategy to bypass a counter‐example which Gareth Evans (1975) offers against Quine’s Thesis of the Inscrutability of Reference. In section 2, I shall introduce a criterion recently pro‐duced by Crispin Wright (1997) in terms of ‘psychological simplicity’ which threatens the perverse route offered in section 1. In section 3, I shall argue that a LOT model of human cognition could motivate Wright’s criterion. In section 4, I shall (...)
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  49. The causal and explanatory role of information stored in connectionist networks.Daniel M. Haybron - 2000 - Minds and Machines 10 (3):361-380.
    In this paper I defend the propriety of explaining the behavior of distributed connectionist networks by appeal to selected data stored therein. In particular, I argue that if there is a problem with such explanations, it is a consequence of the fact that information storage in networks is superpositional, and not because it is distributed. I then develop a ``proto-account'''' of causation for networks, based on an account of Andy Clark''s, that shows even superpositionality does not undermine information-based explanation. Finally, (...)
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  50. Localism as a first step toward symbolic representation.John E. Hummel - 2000 - Behavioral and Brain Sciences 23 (4):480-481.
    Page argues convincingly for several important properties of localist representations in connectionist models of cognition. I argue that another important property of localist representations is that they serve as the starting point for connectionist representations of symbolic (relational) structures because they express meaningful properties independent of one another and their relations.
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