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Jordan B. Pollack (1990). Recursive Distributed Representations.

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  1.  9
    Biologically Plausible, Human‐Scale Knowledge Representation.Eric Crawford, Matthew Gingerich & Chris Eliasmith - 2016 - Cognitive Science 40 (4):782-821.
    Several approaches to implementing symbol-like representations in neurally plausible models have been proposed. These approaches include binding through synchrony, “mesh” binding, and conjunctive binding. Recent theoretical work has suggested that most of these methods will not scale well, that is, that they cannot encode structured representations using any of the tens of thousands of terms in the adult lexicon without making implausible resource assumptions. Here, we empirically demonstrate that the biologically plausible structured representations employed in the Semantic Pointer Architecture approach (...)
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  2.  16
    Learning Orthographic Structure With Sequential Generative Neural Networks.Alberto Testolin, Ivilin Stoianov, Alessandro Sperduti & Marco Zorzi - 2016 - Cognitive Science 40 (3):579-606.
    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine, a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual (...)
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  3.  31
    Interactive Effects of Explicit Emergent Structure: A Major Challenge for Cognitive Computational Modeling.Robert M. French & Elizabeth Thomas - 2015 - Topics in Cognitive Science 7 (2):206-216.
    David Marr's three-level analysis of computational cognition argues for three distinct levels of cognitive information processing—namely, the computational, representational, and implementational levels. But Marr's levels are—and were meant to be—descriptive, rather than interactive and dynamic. For this reason, we suggest that, had Marr been writing today, he might well have gone even farther in his analysis, including the emergence of structure—in particular, explicit structure at the conceptual level—from lower levels, and the effect of explicit emergent structures on the level that (...)
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  4.  16
    Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition.Timothy T. Rogers & James L. McClelland - 2014 - Cognitive Science 38 (6):1024-1077.
    This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary (...)
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  5.  39
    Optimization and Quantization in Gradient Symbol Systems: A Framework for Integrating the Continuous and the Discrete in Cognition.Paul Smolensky, Matthew Goldrick & Donald Mathis - 2014 - Cognitive Science 38 (6):1102-1138.
    Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed Symbol Systems, (...)
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  6.  62
    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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  7.  13
    Modeling Complexity in Musical Rhythm.Cheng-Yuan Liou, Tai-Hei Wu & Chia-Ying Lee - 2010 - Complexity 15 (4):NA-NA.
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  8.  64
    Composition in Distributional Models of Semantics.Jeff Mitchell & Mirella Lapata - 2010 - Cognitive Science 34 (8):1388-1429.
    Vector-based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector-based models are typically directed at representing words in isolation, and methods for constructing representations for phrases or sentences have received little attention in the literature. This is in marked contrast to experimental evidence (e.g., in (...)
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  9.  58
    Connectionist Semantic Systematicity.Stefan L. Frank, Willem F. G. Haselager & Iris van Rooij - 2009 - Cognition 110 (3):358-379.
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  10.  29
    A Neural Network for Creative Serial Order Cognitive Behavior.Steve Donaldson - 2008 - Minds and Machines 18 (1):53-91.
    If artificial neural networks are ever to form the foundation for higher level cognitive behaviors in machines or to realize their full potential as explanatory devices for human cognition, they must show signs of autonomy, multifunction operation, and intersystem integration that are absent in most existing models. This model begins to address these issues by integrating predictive learning, sequence interleaving, and sequence creation components to simulate a spectrum of higher-order cognitive behaviors which have eluded the grasp of simpler systems. Its (...)
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  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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  12.  7
    Harmony in Linguistic Cognition.Paul Smolensky - 2006 - Cognitive Science 30 (5):779-801.
    In this article, I survey the integrated connectionist/symbolic (ICS) cognitive architecture in which higher cognition must be formally characterized on two levels of description. At the microlevel, parallel distributed processing (PDP) characterizes mental processing; this PDP system has special organization in virtue of which it can be characterized at the macrolevel as a kind of symbolic computational system. The symbolic system inherits certain properties from its PDP substrate; the symbolic functions computed constitute optimization of a well-formedness measure called Harmony. The (...)
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  13.  2
    Logic Programs, Iterated Function Systems, and Recurrent Radial Basis Function Networks.Sebastian Bader & Pascal Hitzler - 2004 - Journal of Applied Logic 2 (3):273-300.
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  14.  7
    A Bound on Synchronically Interpretable Structure.Jon M. Slack - 2004 - Mind and Language 19 (3):305–333.
    Multiple explanatory frameworks may be required to provide an adequate account of human cognition. This paper embeds the classical account within a neural network framework, exploring the encoding of syntacticallystructured objects over the synchronicdiachronic characteristics of networks. Synchronic structure is defined in terms of temporal binding and the superposition of states. To accommodate asymmetric relations, synchronic structure is subject to the type uniqueness constraint. The nature of synchronic structure is shown to underlie Xbar theory that characterizes the phrasal structure of (...)
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  15.  12
    Learning First-Pass Structural Attachment Preferences with Dynamic Grammars and Recursive Neural Networks.Patrick Sturt, Fabrizio Costa, Vincenzo Lombardo & Paolo Frasconi - 2003 - Cognition 88 (2):133-169.
  16.  29
    Connectionist Natural Language Parsing.Dominic Palmer-Brown, Jonathan A. Tepper & Heather M. Powell - 2002 - Trends in Cognitive Sciences 6 (10):437-442.
  17.  17
    Toward a Connectionist Model of Recursion in Human Linguistic Performance.Morten H. Christiansen & Nick Chater - 1999 - Cognitive Science 23 (2):157-205.
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  18.  16
    Grammar‐Based Connectionist Approaches to Language.Paul Smolensky - 1999 - Cognitive Science 23 (4):589-613.
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  19.  10
    Connectionist Sentence Processing in Perspective.Mark Steedman - 1999 - Cognitive Science 23 (4):615-634.
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  20.  3
    Dynamical Models of Sentence Processing.Whitney Tabor & Michael K. Tanenhaus - 1999 - Cognitive Science 23 (4):491-515.
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  21. Representations and Cognitive Explanations: Assessing the Dynamicist Challenge in Cognitive Science.William P. Bechtel - 1998 - Cognitive Science 22 (3):295-317.
    Advocates of dynamical systems theory (DST) sometimes employ revolutionary rhetoric. In an attempt to clarify how DST models differ from others in cognitive science, I focus on two issues raised by DST: the role for representations in mental models and the conception of explanation invoked. Two features of representations are their role in standing-in for features external to the system and their format. DST advocates sometimes claim to have repudiated the need for stand-ins in DST models, but I argue that (...)
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  22.  35
    Chaotic Emergence and the Language of Thought.James W. Garson - 1998 - Philosophical Psychology 11 (3):303-315.
    The purpose of this paper is to explore the merits of the idea that dynamical systems theory (also known as chaos theory) provides a model of the mind that can vindicate the language of thought (LOT). I investigate the nature of emergent structure in dynamical systems to assess its compatibility with causally efficacious syntactic structure in the brain. I will argue that anyone who is committed to the idea that the brain's functioning depends on emergent features of dynamical systems should (...)
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  23.  72
    Explaining Systematicity.Kenneth Aizawa - 1997 - Mind and Language 12 (2):115-36.
  24.  15
    Explaining Systematicity.Kenneth Aizawa - 1997 - Mind and Language 12 (2):115-136.
  25.  41
    Cognition, Systematicity, and Nomic Necessity.Robert F. Hadley - 1997 - Mind and Language 12 (2):137-53.
  26.  4
    Cognition, Systematicity and Nomic Necessity.Robert F. Hadley - 1997 - Mind and Language 12 (2):137-153.
  27.  37
    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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  28.  22
    Subsymbolic Case‐Role Analysis of Sentences with Embedded Clauses.Risto Miikkulainen - 1996 - Cognitive Science 20 (1):47-73.
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  29.  12
    Reduced Memory Representations for Music.Edward W. Large, Caroline Palmėr & Jordan B. Pollack - 1995 - Cognitive Science 19 (1):53-96.
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  30.  20
    Cognition and Decision in Biomedical Artificial Intelligence: From Symbolic Representation to Emergence. [REVIEW]Vincent Rialle - 1995 - AI and Society 9 (2-3):138-160.
    This paper presents work in progress on artificial intelligence in medicine (AIM) within the larger context of cognitive science. It introduces and develops the notion ofemergence both as an inevitable evolution of artificial intelligence towards machine learning programs and as the result of a synergistic co-operation between the physician and the computer. From this perspective, the emergence of knowledge takes placein fine in the expert's mind and is enhanced both by computerised strategies of induction and deduction, and by software abilities (...)
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  31. Natural Deduction in Connectionist Systems.William P. 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.  30
    Systematicity in Connectionist Language Learning.Robert F. Hadley - 1994 - Mind and Language 9 (3):247-72.
  33.  23
    Systematicity Revisited.Robert F. Hadley - 1994 - Mind and Language 9 (4):431-44.
  34.  84
    A Nonclassical Framework for Cognitive Science.Terence E. Horgan & John L. Tienson - 1994 - Synthese 101 (3):305-45.
    David Marr provided a useful framework for theorizing about cognition within classical, AI-style cognitive science, in terms of three levels of description: the levels of (i) cognitive function, (ii) algorithm and (iii) physical implementation. We generalize this framework: (i) cognitive state transitions, (ii) mathematical/functional design and (iii) physical implementation or realization. Specifying the middle, design level to be the theory of dynamical systems yields a nonclassical, alternative framework that suits (but is not committed to) connectionism. We consider how a brain's (...)
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  35.  14
    Representations Don't Need Rules: Reply to James Garson.Terence E. Horgan & John L. Tienson - 1994 - Mind and Language 9 (1):1-24.
  36.  56
    Three-Concept Monte: Explanation, Implementation, and Systematicity.Robert J. Matthews - 1994 - Synthese 101 (3):347-63.
    Fodor and Pylyshyn (1988), Fodor and McLaughlin (1990) and McLaughlin (1993) challenge connectionists to explain systematicity without simply implementing a classical architecture. In this paper I argue that what makes the challenge difficult for connectionists to meet has less to do with what is to be explained than with what is to count as an explanation. Fodor et al. are prepared to admit as explanatory, accounts of a sort that only classical models can provide. If connectionists are to meet the (...)
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  37.  94
    On Being Systematically Connectionist.L. F. Niklasson & Tim van Gelder - 1994 - Mind and Language 9 (3):288-30.
    In 1988 Fodor and Pylyshyn issued a challenge to the newly-popular connectionism: explain the systematicity of cognition without merely implementing a so-called classical architecture. Since that time quite a number of connectionist models have been put forward, either by their designers or by others, as in some measure demonstrating that the challenge can be met (e.g., Pollack, 1988, 1990; Smolensky, 1990; Chalmers, 1990; Niklasson and Sharkey, 1992; Brousse, 1993). Unfortu- nately, it has generally been unclear whether these models actually do (...)
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  38.  7
    Reconstructing Physical Symbol Systems.David S. Touretzky & Dean A. Pomerleau - 1994 - Cognitive Science 18 (2):345-353.
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  39.  38
    Currents in Connectionism.William Bechtel - 1993 - Minds and Machines 3 (2):125-153.
    This paper reviews four significant advances on the feedforward architecture that has dominated discussions of connectionism. The first involves introducing modularity into networks by employing procedures whereby different networks learn to perform different components of a task, and a Gating Network determines which network is best equiped to respond to a given input. The second consists in the use of recurrent inputs whereby information from a previous cycle of processing is made available on later cycles. The third development involves developing (...)
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  40.  80
    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.
  41.  76
    The Connectionism/Classicism Battle to Win Souls.Brian P. McLaughlin - 1993 - Philosophical Studies 71 (2):163-190.
  42.  82
    Cognitive Systems as Dynamic Systems.Terence E. Horgan & John L. Tienson - 1992 - Topoi 11 (1):27-43.
  43.  7
    Compositionality: A Connectionist Variation on a Classical Theme.Tim Gelder - 1990 - Cognitive Science 14 (3):355-384.
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  44.  98
    Compositionality: A Connectionist Variation on a Classical Theme.Tim van Gelder - 1990 - Cognitive Science 14 (3):355-84.