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  1. Philosophy and Memory Traces: Descartes to Connectionism.John Sutton - 1998 - Cambridge University Press.
    Philosophy and Memory Traces defends two theories of autobiographical memory. One is a bewildering historical view of memories as dynamic patterns in fleeting animal spirits, nervous fluids which rummaged through the pores of brain and body. The other is new connectionism, in which memories are 'stored' only superpositionally, and reconstructed rather than reproduced. Both models, argues John Sutton, depart from static archival metaphors by employing distributed representation, which brings interference and confusion between memory traces. Both raise urgent issues about control (...)
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  • From Simple Associations to Systematic Reasoning: A Connectionist Representation of Rules, Variables, and Dynamic Binding Using Temporal Synchrony.Lokendra Shastri & Venkat Ajjanagadde - 1993 - Behavioral and Brain Sciences 16 (3):417-51.
    Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large (...)
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  • 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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  • The Dynamical Hypothesis in Cognitive Science.Tim van Gelder - 1998 - Behavioral and Brain Sciences 21 (5):615-28.
    According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive systems may well be dynamical systems, (...)
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  • Perceptual Symbol Systems.Lawrence W. Barsalou - 1999 - Behavioral and Brain Sciences 22 (4):577-660.
    Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the (...)
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  • Connectionist Semantic Systematicity.Stefan L. Frank, Willem F. G. Haselager & Iris van Rooij - 2009 - Cognition 110 (3):358-379.
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  • Scaling Connectionist Compositional Representations.John C. Flackett, John Tait & Guy Littlefair - 2004 - In Simon D. Levy & Ross Gayler (eds.), Compositional Connectionism in Cognitive Science. Aaai Press. pp. 20--24.
  • Context-Sensitive Inference, Modularity, and the Assumption of Formal Processing.Mitch Parsell - 2005 - Philosophical Psychology 18 (1):45-58.
    Performance on the Wason selection task varies with content. This has been taken to demonstrate that there are different cognitive modules for dealing with different conceptual domains. This implication is only legitimate if our underlying cognitive architecture is formal. A non-formal system can explain content-sensitive inference without appeal to independent inferential modules.
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  • [email protected]Antony Browne & Ron Sun - unknown
    Variable binding has long been a challenge to connectionists. Attempts to perform variable binding using localist and distributed connectionist representations are discussed, and problems inherent in each type of representation are outlined.
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  • Mental Imagery.Nigel J. T. Thomas - 2001 - Stanford Encyclopedia of Philosophy.
    Mental imagery (varieties of which are sometimes colloquially refered to as “visualizing,” “seeing in the mind's eye,” “hearing in the head,” “imagining the feel of,” etc.) is quasi-perceptual experience; it resembles perceptual experience, but occurs in the absence of the appropriate external stimuli. It is also generally understood to bear intentionality (i.e., mental images are always images of something or other), and thereby to function as a form of mental representation. Traditionally, visual mental imagery, the most discussed variety, was thought (...)
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  • What Levels of Explanation in the Behavioural Sciences?Giuseppe Boccignone & Roberto Cordeschi (eds.) - 2015 - Frontiers Media SA.
    Complex systems are to be seen as typically having multiple levels of organization. For instance, in the behavioural and cognitive sciences, there has been a long lasting trend, promoted by the seminal work of David Marr, putting focus on three distinct levels of analysis: the computational level, accounting for the What and Why issues, the algorithmic and the implementational levels specifying the How problem. However, the tremendous developments in neuroscience knowledge about processes at different scales of organization together with the (...)
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  • Directions in Connectionist Research: Tractable Computations Without Syntactically Structured Representations.Jonathan Waskan & William Bechtel - 1997 - Metaphilosophy 28 (1‐2):31-62.
    Figure 1: A pr ototyp ical exa mple of a three-layer feed forward network, used by Plunkett and M archm an (1 991 ) to simulate learning the past-tense of En glish verbs. The inpu t units encode representations of the three phonemes of the present tense of the artificial words used in this simulation. Th e netwo rk is trained to produce a representation of the phonemes employed in the past tense form and the suffix (/d/, /ed/, or /t/) (...)
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  • What Knowledge Must Be in the Head in Order to Acquire Language.William P. Bechtel - 1996 - In B. Velichkovsky & Duane M. Rumbaugh (eds.), Communicating Meaning: The Evolution and Development of Language. Hillsdale, Nj: Lawrence Erlbaum Associates. pp. 45.
    Many studies of language, whether in philosophy, linguistics, or psychology, have focused on highly developed human languages. In their highly developed forms, such as are employed in scientific discourse, languages have a unique set of properties that have been the focus of much attention. For example, descriptive sentences in a language have the property of being "true" or "false," and words of a language have senses and referents. Sentences in a language are structured in accord with complex syntactic rules. Theorists (...)
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  • Representations Don't Need Rules: Reply to James Garson.Terence Horgan & John Tienson - 1994 - Mind and Language 9 (1):1-24.
  • 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.
  • 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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  • Systematicity Revisited: Reply to Christiansen and Chater and Niklasson and van Gelder.Robert F. Hadley - 1994 - Mind and Language 9 (4):431-444.
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  • Systematicity Revisited.Robert F. Hadley - 1994 - Mind and Language 9 (4):431-44.
  • On Being Systematically Connectionist.Lars F. Niklasson & Tim Gelder - 1994 - Mind and Language 9 (3):288-302.
  • 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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  • Systematicity in Connectionist Language Learning.Robert F. Hadley - 1994 - Mind and Language 9 (3):247-72.
  • On the Potential of Non-Classical Constituency.W. F. G. Haselager - 1999 - Acta Analytica 144:23-42.
  • Subsymbolic Computation and the Chinese Room.David J. Chalmers - 1992 - In J. Dinsmore (ed.), The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum. pp. 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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  • Perceptions of Perceptual Symbols.Lawrence W. Barsalou - 1999 - Behavioral and Brain Sciences 22 (4):637-660.
    Various defenses of amodal symbol systems are addressed, including amodal symbols in sensory-motor areas, the causal theory of concepts, supramodal concepts, latent semantic analysis, and abstracted amodal symbols. Various aspects of perceptual symbol systems are clarified and developed, including perception, features, simulators, category structure, frames, analogy, introspection, situated action, and development. Particular attention is given to abstract concepts, language, and computational mechanisms.
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  • Causality.Jessica M. Wilson - 2006 - In Jessica Pfeifer & Sahotra Sarkar (eds.), The Philosophy of Science: An Encyclopedia. Routledge. pp. 90--100.
    Arguably no concept is more fundamental to science than that of causality, for investigations into cases of existence, persistence, and change in the natural world are largely investigations into the causes of these phenomena. Yet the metaphysics and epistemology of causality remain unclear. For example, the ontological categories of the causal relata have been taken to be objects (Hume 1739), events (Davidson 1967), properties (Armstrong 1978), processes (Salmon 1984), variables (Hitchcock 1993), and facts (Mellor 1995). (For convenience, causes and effects (...)
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  • In Defense of Representation.Arthur B. Markman & Eric Dietrich - 2000 - Cognitive Psychology 40 (2):138--171.
    The computational paradigm, which has dominated psychology and artificial intelligence since the cognitive revolution, has been a source of intense debate. Recently, several cognitive scientists have argued against this paradigm, not by objecting to computation, but rather by objecting to the notion of representation. Our analysis of these objections reveals that it is not the notion of representation per se that is causing the problem, but rather specific properties of representations as they are used in various psychological theories. Our analysis (...)
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  • 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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  • 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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  • Compositionality: A Connectionist Variation on a Classical Theme.Tim Gelder - 1990 - Cognitive Science 14 (3):355-384.
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  • 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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  • Language of Thought Hypothesis: State of the Art.Murat Aydede - manuscript
    [This is an earlier, much longer and more detailed version of my entry on LOTH in the _Stanford Encyclopedia of Philosophy_] The Language of Thought Hypothesis (LOTH) is an empirical thesis about thought and thinking. For their explication, it postulates a physically realized system of representations that have a combinatorial syntax (and semantics) such that operations on representations are causally sensitive only to the syntactic properties of representations. According to LOTH, thought is, roughly, the tokening of a representation that has (...)
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  • Representations and Cognitive Explanations: Assessing the Dynamicist Challenge in Cognitive Science.William 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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  • 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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  • Computation and Dynamical Models of Mind.Chris Eliasmith - 1997 - Minds and Machines 7 (4):531-41.
    Van Gelder (1995) has recently spearheaded a movement to challenge the dominance of connectionist and classicist models in cognitive science. The dynamical conception of cognition is van Gelder's replacement for the computation bound paradigms provided by connectionism and classicism. He relies on the Watt governor to fulfill the role of a dynamicist Turing machine and claims that the Motivational Oscillatory Theory (MOT) provides a sound empirical basis for dynamicism. In other words, the Watt governor is to be the theoretical exemplar (...)
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  • Subsymbolic Case‐Role Analysis of Sentences with Embedded Clauses.Risto Miikkulainen - 1996 - Cognitive Science 20 (1):47-73.
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  • Connectionist Sentence Processing in Perspective.Mark Steedman - 1999 - Cognitive Science 23 (4):615-634.
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  • Learning Representations of Wordforms.Daragh E. Sibley, Christopher T. Kello, David C. Plaut & Jeffrey L. Elman - 2008 - Cognitive Science 32 (4):741-754.
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  • Reconstructing Physical Symbol Systems.David S. Touretzky & Dean A. Pomerleau - 1994 - Cognitive Science 18 (2):345-353.
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  • The Curious Case of Connectionism.Istvan S. N. Berkeley - 2019 - Open Philosophy 2 (1):190-205.
    Connectionist research first emerged in the 1940s. The first phase of connectionism attracted a certain amount of media attention, but scant philosophical interest. The phase came to an abrupt halt, due to the efforts of Minsky and Papert, when they argued for the intrinsic limitations of the approach. In the mid-1980s connectionism saw a resurgence. This marked the beginning of the second phase of connectionist research. This phase did attract considerable philosophical attention. It was of philosophical interest, as it offered (...)
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  • 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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  • 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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  • Grammar‐Based Connectionist Approaches to Language.Paul Smolensky - 1999 - Cognitive Science 23 (4):589-613.
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  • 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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  • Cognition, Systematicity and Nomic Necessity.Robert F. Hadley - 1997 - Mind and Language 12 (2):137-153.
  • Cognition, Systematicity, and Nomic Necessity.Robert F. Hadley - 1997 - Mind and Language 12 (2):137-53.
  • Explaining Systematicity.Kenneth Aizawa - 1997 - Mind and Language 12 (2):115-136.
    Despite the considerable attention that the systematicity argument has enjoyed, it is worthwhile examining the argument within the context of similar explanatory arguments from the history of science. This kind of analysis helps show that Connectionism, qua Connectionism, really does not have an explanation of systematicity. Second, and more surprisingly, one finds that the systematicity argument sets such a high explanatory standard that not even Classicism can explain the systematicity of thought.
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  • Explaining Systematicity.Kenneth Aizawa - 1997 - Mind and Language 12 (2):115-36.
    Despite the considerable attention that the systematicity argument has enjoyed, it is worthwhile examining the argument within the context of similar explanatory arguments from the history of science. This kind of analysis helps show that Connectionism, qua Connectionism, really does not have an explanation of systematicity. Second, and more surprisingly, one finds that the systematicity argument sets such a high explanatory standard that not even Classicism can explain the systematicity of thought.
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  • 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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  • Reduced Memory Representations for Music.Edward W. Large, Caroline Palmėr & Jordan B. Pollack - 1995 - Cognitive Science 19 (1):53-96.
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  • Dynamical Models of Sentence Processing.Whitney Tabor & Michael K. Tanenhaus - 1999 - Cognitive Science 23 (4):491-515.
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