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  1. Active symbols and internal models: Towards a cognitive connectionism. [REVIEW]Stephen Kaplan, Mark Weaver & Robert French - 1990 - AI and Society 4 (1):51-71.
    In the first section of the article, we examine some recent criticisms of the connectionist enterprise: first, that connectionist models are fundamentally behaviorist in nature (and, therefore, non-cognitive), and second that connectionist models are fundamentally associationist in nature (and, therefore, cognitively weak). We argue that, for a limited class of connectionist models (feed-forward, pattern-associator models), the first criticism is unavoidable. With respect to the second criticism, we propose that connectionist modelsare fundamentally associationist but that this is appropriate for building models (...)
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  • On language and connectionism: Analysis of a parallel distributed processing model of language acquisition.Steven Pinker & Alan Prince - 1988 - Cognition 28 (1-2):73-193.
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  • On the proper treatment of connectionism.Paul Smolensky - 1988 - Behavioral and Brain Sciences 11 (1):1-23.
    A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...)
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  • The symbol grounding problem.Stevan Harnad - 1990 - Physica D 42:335-346.
    There has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the symbol grounding problem : How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their shapes, be grounded (...)
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  • Minds, brains, and programs.John Searle - 1980 - Behavioral and Brain Sciences 3 (3):417-57.
    What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI. According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. (...)
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  • A logical calculus of the ideas immanent in nervous activity.Warren S. McCulloch & Walter Pitts - 1943 - The Bulletin of Mathematical Biophysics 5 (4):115-133.
    Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic. It is found that the behavior of every net can be described in these terms, with the addition of more complicated logical means for nets containing circles; and that for any logical expression satisfying certain conditions, one can find a net behaving in the fashion it describes. It is shown that many particular choices among possible neurophysiological assumptions (...)
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  • An interactive activation model of context effects in letter perception: I. An account of basic findings.James L. McClelland & David E. Rumelhart - 1981 - Psychological Review 88 (5):375-407.
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  • Toward a modern theory of adaptive networks: Expectation and prediction.Richard S. Sutton & Andrew G. Barto - 1981 - Psychological Review 88 (2):135-170.
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  • Features of similarity.Amos Tversky - 1977 - Psychological Review 84 (4):327-352.
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  • Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions.Roger Ratcliff - 1990 - Psychological Review 97 (2):285-308.
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  • Vehicles: Experiments in Synthetic Psychology.Valentino Braitenberg - 1986 - Philosophical Review 95 (1):137-139.
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  • Value, variable, and coarse coding by posterior parietal neurons.Richard A. Andersen - 1986 - Behavioral and Brain Sciences 9 (1):90-91.
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  • 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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  • Connectionist Models and Their Properties.J. A. Feldman & D. H. Ballard - 1982 - Cognitive Science 6 (3):205-254.
    Much of the progress in the fields constituting cognitive science has been based upon the use of explicit information processing models, almost exclusively patterned after conventional serial computers. An extension of these ideas to massively parallel, connectionist models appears to offer a number of advantages. After a preliminary discussion, this paper introduces a general connectionist model and considers how it might be used in cognitive science. Among the issues addressed are: stability and noise‐sensitivity, distributed decision‐making, time and sequence problems, and (...)
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  • Steps Toward Artificial Intelligence.Marvin Minsky - unknown
    Received by the IRE, October 24, 1960. The author's work summarized here—which was done at the MIT Lincoln Laboratory, a center for research operated by MIT at Lexington, Mass., with the joint Support of the U. S. Army, Navy, and Air Force under Air Force Contract AF 19-5200; and at the Res. Lab. of Electronics, MIT, Cambridge, Mass., which is supported in part by the U. S. Army Signal Corps, the Air Force Office of Scientific Research, and the ONR—is based (...)
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  • Vision.David Marr - 1982 - W. H. Freeman.
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  • The Senses Considered as Perceptual Systems.Charles K. West & James J. Gibson - 1969 - Journal of Aesthetic Education 3 (1):142.
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  • The Senses Considered as Perceptual Systems.James Jerome Gibson - 1966 - Boston, USA: Houghton Mifflin.
    Describes the various senses as sensory systems that are attuned to the environment. Develops the notion of rich sensory information that specifies the distal environment. Includes a discussion of affordances.
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  • Science and Human Behavior.Burrhus Frederic Skinner - 1963 - New York: Free Press.
    A detailed study of scientific theories of human nature and the possible ways in which human behavior can be predicted and controlled.
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  • The relation between linguistic structure and associative theories of language learning—A constructive critique of some connectionist learning models.Joel Lachter & Thomas G. Bever - 1988 - Cognition 28 (1-2):195-247.
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  • Representation: A concept that fills no gaps.Robert Epstein - 1982 - Behavioral and Brain Sciences 5 (3):377-378.
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  • Some thoughts on the proper foundations for the study of cognition in animals.Lynn Nadel - 1982 - Behavioral and Brain Sciences 5 (3):383-384.
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  • The meaning of representation in animal memory.H. L. Roitblat - 1982 - Behavioral and Brain Sciences 5 (3):353-372.
    A representation is a remnant of previous experience that allows that experience to affect later behavior. This paper develops a metatheoretical view of representation and applies it to issues concerning representation in animals. To describe a representational system one must specify the following: thedomainor range of situations in the represented world to which the system applies; thecontentor set of features encoded and preserved by the system; thecodeor transformational rules relating features of the representation to the corresponding features of the represented (...)
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  • A layered network model of associative learning: Learning to learn and configuration.E. James Kehoe - 1988 - Psychological Review 95 (4):411-433.
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  • A learning algorithm for boltzmann machines.David H. Ackley, Geoffrey E. Hinton & Terrence J. Sejnowski - 1985 - Cognitive Science 9 (1):147-169.
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  • Neural Connections, Mental Computations.L. Nadel (ed.) - 1989 - MIT Press.
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  • Philosophy and Connectionist Theory.William Ramsey, Stephen P. Stich & D. M. Rumelhart (eds.) - 1991 - Hillsdale, N.J.: Lawrence Erlbaum.
    The philosophy of cognitive science has recently become one of the most exciting and fastest growing domains of philosophical inquiry and analysis. Until the early 1980s, nearly all of the models developed treated cognitive processes -- like problem solving, language comprehension, memory, and higher visual processing -- as rule-governed symbol manipulation. However, this situation has changed dramatically over the last half dozen years. In that period there has been an enormous shift of attention toward connectionist models of cognition that are (...)
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  • Autonomy, implementation and cognitive architecture: A reply to Fodor and Pylyshyn.Nick Chater & Mike Oaksford - 1990 - Cognition 34 (1):93-107.
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  • {Finding structure in time}.J. Elman - 1993 - {Cognitive Science} 48:71-99.
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  • Cognitive psychology's representation of behaviorism.A. W. Logue - 1982 - Behavioral and Brain Sciences 5 (3):381-382.
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  • Competitive Learning: From Interactive Activation to Adaptive Resonance.Stephen Grossberg - 1987 - Cognitive Science 11 (1):23-63.
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  • Why am and eurisko appear to work.Douglas B. Lenat & John Seely Brown - 1984 - Artificial Intelligence 23 (3):269-294.
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  • Review of Burrhus F. Skinner: Science and Human Behavior[REVIEW]Harry Prosch - 1953 - Ethics 63 (4):314-314.
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  • Compositionality: A connectionist variation on a classical theme.Tim van Gelder - 1990 - Cognitive Science 14 (3):355-84.
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  • Information processing abstractions: The message still counts more than the medium.B. Chandrasekaran, Ashok Goel & Dean Allemang - 1988 - Behavioral and Brain Sciences 11 (1):26-27.
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  • How fully should connectionism be activated? Two sources of excitation and one of inhibition.Roger N. Shepard - 1988 - Behavioral and Brain Sciences 11 (1):52-52.
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  • A distributed, developmental model of word recognition and naming.Mark S. Seidenberg & James L. McClelland - 1989 - Psychological Review 96 (4):523-568.
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  • Connectionist models and their applications: Introduction.Jerome A. Feldman - 1985 - Cognitive Science 9 (1):1-2.
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  • Distributed memory and the representation of general and specific information.James L. McClelland & David E. Rumelhart - 1985 - Journal of Experimental Psychology 114 (2):159-188.
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  • The child's trigger experience: Degree-0 learnability.David Lightfoot - 1989 - Behavioral and Brain Sciences 12 (2):321-334.
    According to a “selective” (as opposed to “instructive”) model of human language capacity, people come to know more than they experience. The discrepancy between experience and eventual capacity (the “poverty of the stimulus”) is bridged by genetically provided information. Hence any hypothesis about the linguistic genotype (or “Universal Grammar,” UG) has consequences for what experience is needed and what form people's mature capacities (or “grammars”) will take. This BBS target article discusses the “trigger experience,” that is, the experience that actually (...)
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  • Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks.Robert A. Jacobs, Michael I. Jordan & Andrew G. Barto - 1991 - Cognitive Science 15 (2):219-250.
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  • Marker‐passing over Microfeatures: Towards a Hybrid Symbolic/Connectionist Model.James A. Hendler - 1989 - Cognitive Science 13 (1):79-106.
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  • Statistical rationality.Richard M. Golden - 1988 - Behavioral and Brain Sciences 11 (1):35-35.
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  • Toward a statistical theory of learning.William K. Estes - 1950 - Psychological Review 57 (2):94-107.
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  • Misrepresenting behaviorism.Marc N. Branch - 1982 - Behavioral and Brain Sciences 5 (3):372-373.
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  • Distinctive features, categorical perception, and probability learning: Some applications of a neural model.James A. Anderson, Jack W. Silverstein, Stephen A. Ritz & Randall S. Jones - 1977 - Psychological Review 84 (5):413-451.
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  • Formal Grammars in Linguistics and Psycholinguistics.W. J. M. Levelt - 1976 - Foundations of Language 14 (4):609-609.
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