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102 found
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1 — 50 / 102
  1. added 2018-09-06
    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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  2. added 2018-02-17
    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 establishing a limiting case correspondence between symbolic and (...)
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  3. added 2017-02-14
    Long Term Cost Allocation Methodology for Distribution Networks with Distributed Generation.P. M. De Oliveira-De Jesus & Mt Ponce de Leão - 2005 - In Alan F. Blackwell & David MacKay (eds.), Power. Cambridge University Press. pp. 1.
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  4. added 2017-02-14
    Semantic Networks.M. Ross Quillian - 1968 - In Marvin L. Minsky (ed.), Semantic Information Processing. MIT Press.
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  5. added 2017-02-13
    Learning From Learned Networks.M. Pavel - 1990 - Behavioral and Brain Sciences 13 (3):503-504.
  6. added 2017-02-13
    A Solution to the Tag-Assignment Problem for Neural Networks.Gary W. Strong & Bruce A. Whitehead - 1989 - Behavioral and Brain Sciences 12 (3):381-397.
    Purely parallel neural networks can model object recognition in brief displays – the same conditions under which illusory conjunctions have been demonstrated empirically. Correcting errors of illusory conjunction is the “tag-assignment” problem for a purely parallel processor: the problem of assigning a spatial tag to nonspatial features, feature combinations, and objects. This problem must be solved to model human object recognition over a longer time scale. Our model simulates both the parallel processes that may underlie illusory conjunctions and the serial (...)
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  7. added 2017-02-11
    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.
  8. added 2017-02-11
    Principles of Semantic Networks.Steven Schwartz - 1984 - Behavioral and Brain Sciences 7 (4).
    A semantic network or net is a graphic notation for representing knowledge in patterns of interconnected nodes and arcs. Computer implementations of semantic networks were first developed for artificial intelligence and machine translation, but earlier versions have long been used in philosophy, psychology, and linguistics. What is common to all semantic networks is a declarative graphic representation that can be used either to represent knowledge or to support automated systems for reasoning about knowledge. Some versions are highly informal, but other (...)
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  9. added 2017-02-01
    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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  10. added 2017-01-25
    Networks Are Not ‘Hidden Rules’.Mark S. Seidenberg & Jeffrey L. Elman - 1999 - Trends in Cognitive Sciences 3 (8):288-289.
  11. added 2017-01-25
    Are Semantic Networks of Schizophrenic Samples Intact?Richard W. J. Neufeld - 1984 - Behavioral and Brain Sciences 7 (4):749.
  12. added 2017-01-17
    Uncertain Reasoning with RAM Neural Networks.J. Austin - 1992 - Journal of Intelligent Systems 2 (1-4):121-154.
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  13. added 2017-01-16
    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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  14. added 2017-01-15
    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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  15. added 2016-12-12
    Semantics San Diego Style.Evan Tiffany - 1999 - Journal of Philosophy 96 (8):416.
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  16. added 2016-12-08
    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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  17. added 2016-12-08
    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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  18. added 2016-12-05
    A Connectionist Defence of the Inscrutability Thesis.Calvo Garzon Francisco - 2000 - Mind and Language 15 (5):465-480.
  19. added 2016-10-12
    Connectionism, Analogicity and Mental Content.Gerard O'Brien - 1989 - Acta Analytica 22 (22):111-31.
    In Connectionism and the Philosophy of Psychology, Horgan and Tienson (1996) argue that cognitive processes, pace classicism, are not governed by exceptionless, “representation-level” rules; they are instead the work of defeasible cognitive tendencies subserved by the non-linear dynamics of the brain’s neural networks. Many theorists are sympathetic with the dynamical characterisation of connectionism and the general (re)conception of cognition that it affords. But in all the excitement surrounding the connectionist revolution in cognitive science, it has largely gone unnoticed that connectionism (...)
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  20. added 2016-10-09
    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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  21. added 2016-09-23
    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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  22. added 2015-10-24
    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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  23. added 2015-07-29
    Knowledge Bases and Neural Network Synthesis.Todd R. Davies - 1991 - In Hozumi Tanaka (ed.), Artificial Intelligence in the Pacific Rim: Proceedings of the Pacific Rim International Conference on Artificial Intelligence. IOS Press. pp. 717-722.
    We describe and try to motivate our project to build systems using both a knowledge based and a neural network approach. These two approaches are used at different stages in the solution of a problem, instead of using knowledge bases exclusively on some problems, and neural nets exclusively on others. The knowledge base (KB) is defined first in a declarative, symbolic language that is easy to use. It is then compiled into an efficient neural network (NN) representation, run, and the (...)
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  24. added 2015-05-15
    Learned Categorical Perception in Neural Nets: Implications for Symbol Grounding.Stevan Harnad & Stephen J. Hanson - unknown
    After people learn to sort objects into categories they see them differently. Members of the same category look more alike and members of different categories look more different. This phenomenon of within-category compression and between-category separation in similarity space is called categorical perception (CP). It is exhibited by human subjects, animals and neural net models. In backpropagation nets trained first to auto-associate 12 stimuli varying along a onedimensional continuum and then to sort them into 3 categories, CP arises as a (...)
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  25. added 2015-05-11
    Trading Spaces: Computation, Representation, and the Limits of Uninformed Learning.Andy Clark & Chris Thornton - 1997 - Behavioral and Brain Sciences 20 (1):57-66.
    Some regularities enjoy only an attenuated existence in a body of training data. These are regularities whose statistical visibility depends on some systematic recoding of the data. The space of possible recodings is, however, infinitely large – it is the space of applicable Turing machines. As a result, mappings that pivot on such attenuated regularities cannot, in general, be found by brute-force search. The class of problems that present such mappings we call the class of “type-2 problems.” Type-1 problems, by (...)
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  26. added 2015-05-11
    Churchland on State Space Semantics.J. Fodor & E. Lepore - 1996 - In Robert N. McCauley (ed.), The Churchlands and Their Critics. Blackwell. pp. 145--158.
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  27. added 2014-08-15
    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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  28. added 2014-04-15
    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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  29. added 2014-04-15
    Quantification Without Variables in Connectionism.John A. Barnden & Kankanahalli Srinivas - 1996 - 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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  30. added 2014-04-02
    The Cognizer's Innards: A Psychological and Philosophical Perspective on the Development of Thought.Andy Clark & Annette Karmiloff-Smith - 1993 - Mind and Language 8 (4):487-519.
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  31. added 2014-03-31
    Natural Deduction in Connectionist Systems.William Bechtel - 1994 - Synthese 101 (3):433-463.
    The relation between logic and thought has long been controversial, but has recently influenced theorizing about the nature of mental processes in cognitive science. One prominent tradition argues that to explain the systematicity of thought we must posit syntactically structured representations inside the cognitive system which can be operated upon by structure sensitive rules similar to those employed in systems of natural deduction. I have argued elsewhere that the systematicity of human thought might better be explained as resulting from the (...)
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  32. added 2014-03-30
    Representation and Computation in a Deflationary Assessment of Connectionist Cognitive Science.Keith Butler - 1995 - Synthese 104 (1):71-97.
    Connectionism provides hope for unifying work in neuroscience, computer science, and cognitive psychology. This promise has met with some resistance from Classical Computionalists, which may have inspired Connectionists to retaliate with bold, inflationary claims on behalf of Connectionist models. This paper demonstrates, by examining three intimately connected issues, that these inflationary claims made on behalf of Connectionism are wrong. This should not be construed as an attack on Connectionism, however, since the inflated claims made on its behalf have the look (...)
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  33. added 2014-03-30
    Grounding Symbols in the Analog World with Neural Nets.Stevan Harnad - 1993 - Think (misc) 2 (1):12-78.
    Harnad's main argument can be roughly summarised as follows: due to Searle's Chinese Room argument, symbol systems by themselves are insufficient to exhibit cognition, because the symbols are not grounded in the real world, hence without meaning. However, a symbol system that is connected to the real world through transducers receiving sensory data, with neural nets translating these data into sensory categories, would not be subject to the Chinese Room argument. Harnad's article is not only the starting point for the (...)
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  34. added 2014-03-28
    Systematic Minds, Unsystematic Models: Learning Transfer in Humans and Networks. [REVIEW]Steven Phillips - 1999 - Minds and Machines 9 (3):383-398.
    Minds are said to be systematic: the capacity to entertain certain thoughts confers to other related thoughts. Although an important property of human cognition, its implication for cognitive architecture has been less than clear. In part, the uncertainty is due to lack of precise accounts on the degree to which cognition is systematic. However, a recent study on learning transfer provides one clear example. This study is used here to compare transfer in humans and feedforward networks. Simulations and analysis show, (...)
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  35. added 2014-03-28
    On the Potential of Non-Classical Constituency.W. F. G. Haselager - 1999 - Acta Analytica 22 (22):23-42.
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  36. added 2014-03-28
    Out of Their Minds: Legal Theory in Neural Networks. [REVIEW]Dan Hunter - 1999 - Artificial Intelligence and Law 7 (2-3):129-151.
    This paper examines the use of connectionism (neural networks) in modelling legal reasoning. I discuss how the implementations of neural networks have failed to account for legal theoretical perspectives on adjudication. I criticise the use of neural networks in law, not because connectionism is inherently unsuitable in law, but rather because it has been done so poorly to date. The paper reviews a number of legal theories which provide a grounding for the use of neural networks in law. It then (...)
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  37. added 2014-03-28
    Analogy Making in Legal Reasoning with Neural Networks and Fuzzy Logic.Jürgen Hollatz - 1999 - Artificial Intelligence and Law 7 (2-3):289-301.
    Analogy making from examples is a central task in intelligent system behavior. A lot of real world problems involve analogy making and generalization. Research investigates these questions by building computer models of human thinking concepts. These concepts can be divided into high level approaches as used in cognitive science and low level models as used in neural networks. Applications range over the spectrum of recognition, categorization and analogy reasoning. A major part of legal reasoning could be formally interpreted as an (...)
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  38. added 2014-03-28
    Do Connectionist Representations Earn Their Explanatory Keep?William Ramsey - 1997 - Mind and Language 12 (1):34-66.
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  39. added 2014-03-27
    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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  40. added 2014-03-23
    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. added 2014-03-23
    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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  42. added 2014-03-23
    Content and Cluster Analysis: Assessing Representational Similarity in Neural Systems.Aarre Laakso & Garrison W. Cottrell - 2000 - Philosophical Psychology 13 (1):47-76.
    If connectionism is to be an adequate theory of mind, we must have a theory of representation for neural networks that allows for individual differences in weighting and architecture while preserving sameness, or at least similarity, of content. In this paper we propose a procedure for measuring sameness of content of neural representations. We argue that the correct way to compare neural representations is through analysis of the distances between neural activations, and we present a method for doing so. We (...)
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  43. added 2014-03-22
    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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  44. added 2014-03-21
    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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  45. added 2014-03-21
    Autonomous Psychology and the Moderate Neuron Doctrine.Tony Stone & Martin Davies - 1999 - Behavioral and Brain Sciences 22 (5):849-850.
    _Two notions of autonomy are distinguished. The respective_ _denials that psychology is autonomous from neurobiology are neuron_ _doctrines, moderate and radical. According to the moderate neuron_ _doctrine, inter-disciplinary interaction need not aim at reduction. It is_ _proposed that it is more plausible that there is slippage from the_ _moderate to the radical neuron doctrine than that there is confusion_ _between the radical neuron doctrine and the trivial version._.
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  46. added 2014-03-17
    Empiricism and State-Space Semantics.Jesse J. Prinz - 2006 - In Brian L Keeley (ed.), Paul Churchland. Cambridge: Cambridge University Press.
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  47. added 2014-03-15
    Kenneth Aizawa, The Systematicity Arguments, Studies in Brain and Mind: Dordrecht, The Netherlands, Kluwer Academic Publishers, 2002, Xiii+255, Euro 100.00, ISBN 1-4020-7271-6.Steven Phillips - 2007 - Minds and Machines 17 (3):357-360.
  48. added 2014-03-12
    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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  49. added 2014-03-12
    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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  50. added 2014-03-11
    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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