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Some assumptions underlying Smolensky's treatment of connectionism

Published online by Cambridge University Press:  04 February 2010

Eric Dietrich  and
Chris Fields
Eric Dietrich
Affiliation:
Knowledge Systems Group, Computing Research Laboratory, New Mexico State University, Las Cruces, N.Mex. 88003
Chris Fields
Affiliation:
Knowledge Systems Group, Computing Research Laboratory, New Mexico State University, Las Cruces, N.Mex. 88003
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Information
Behavioral and Brain Sciences , Volume 11 , Issue 1 , March 1988 , pp. 29 - 31
Copyright
Copyright © Cambridge University Press 1988

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References

Ackley, D. H., Hinton, G. E. & Sejnowski, T. J. (1985) A learning algorithm for Boltzmann machines. Cognitive Science 9:147–69.Google Scholar
Allen, R. (1987) Natural language and back-propagation: Demonstratives, analogies, pronoun reference, and translation. Proceedings of the IEEE First Annual International Conference on Neural Networks, San Diego, Calif.Google Scholar
Alvarado, S., Dyer, M. G. & Flowers, M. (1986) Editorial comprehension of OpEd through argument units. Proceedings of the American Association of Artificial Intelligence (AAAI-86), Philadelphia, Pa.Google Scholar
Anderson, D. Z. (1986) Coherent optical eigenstate memory. Optics Letters 11:56–58.CrossRefGoogle ScholarPubMed
Anderson, J. A. & Mozer, M. C. (1981) Categorization and selective neurons. In: Parallel models of associative memory, ed. Hinton, G. E. & Anderson, J. A.. Erlbaum.Google Scholar
Anderson, J. A., Silverstein, J. W. & Ritz, S. A. (1977) Distinctive features, categorical perception, and probability learning: Some applications of a neural model. Psychological Review 84:413–51.Google Scholar
Anderson, J. R. (1981) Cognitive skills and their acquisition. Erlbaum.Google Scholar
Anderson, J. R. (1983) The architecture of cognition. Harvard University Press.Google Scholar
Anderson, J. R. (1985) Cognitive Science 9(1): Special issue on connectionist models and their applications.Google Scholar
Ashby, W. R. (1952) Design for a brain. Chapman and Hall.Google Scholar
Baird, B. (1986) Nonlinear dynamics of pattern formation and pattern recognition in the rabbit olfactory bulb. Physica 22D: 150–75.Google Scholar
Ballard, D. H. (1986) Cortical connections and parallel processing: Structure and function. Behavioral and Brain Sciences 9:67–120.CrossRefGoogle Scholar
Ballard, D. H. & Hayes, P. J. (1984) Parallel logical inference. Proceedings the Sixth Conference of the Cognitive Science Society.Google Scholar
Baron, R. J. (1987) The cerebral computer: An introduction to the computational structure of the human brain. Erlbaum.Google Scholar
Barsalou, L. (1986) The instability of graded structure: Implications for the nature of concept. In: Concepts and conceptual development: Ecological and intellectual factors in categorization, ed. Neisser, U.. Cambridge University Press,Google Scholar
Barsalou, L. (in press) Intra-concept similarity and its implications for inter-concept similarity. In: Similarity and analogical reasoning, ed. Vosniadou, S. & Ortony, A.. Cambridge University Press.Google Scholar
Barwise, J. (1986) Information and circumstance. Notre Dame Journal of Formal Logic 27(3):324–38.CrossRefGoogle Scholar
Bechtel, W. (1985) Realism, instrumentalism, and the intentional stance. Cognitive Science 9:473–97.Google Scholar
Bechtel, W. (1988) Philosophy of science: An overview for cognitive science. Erlbaum.Google Scholar
Belew, R. K. (1986) Adaptive information retrieval: Machine learning in associative networks. Ph.D. thesis, Computer Science Department, University of Michigan.Google Scholar
Bimbaum, L. A. (1986) Integrated processing in planning and understanding. Ph.D. Thesis, Yale University.Google Scholar
Burge, T. (1986) Individualism and psychology. Philosophical Review 95(1): 3–45.CrossRefGoogle Scholar
Chandrasekaran, B. (1987) Towards a functional architecture for intelligence based on generic information processing tasks. Proceedings of the Tenth International Joint Conference on Artificial Intelligence, Milan, Italy.Google Scholar
Chandrasekaran, B. (1988) What kind of information processing is intelligence? A perspective AI paradigms and a proposal. Foundations of AI: A Source Book, ed. Partridge, & Wilks, . Cambridge University Press.Google Scholar
Charniak, E. (1987) onnectionism and explanation. In: Proceedings of Theoretical Issues in Natural Language Processing 3:68–72. New Mexico State University.Google Scholar
Chomsky, N. (1972) Language and mind. Harcourt.Google Scholar
Chomsky, N. (1980a) Rules and representations. Columbia University Press.Google Scholar
Chomsky, N. (1980b) Rules and representations. Behavioral and Brain Sciences 3:1–61.Google Scholar
Chomsky, N. (1984) Lectures on government and binding (2nd rev.). Foris.Google Scholar
Churchland, P. S. (1984) Matter and consciousness. MIT Press.Google Scholar
Churchland, P. S. (1986) Neurophitosophy. MIT Press/Bradford Books.Google Scholar
Cohen, M. S. (1986) Design of a new medium for volume holographic information processing. Applied Optics 14:2288–94.Google Scholar
Cooper, L. A. (1976) Demonstration of a mental analog of an external rotation. Perception & Psychophysics 19:296–302.Google Scholar
Cottrell, C. (1987) Toward connectionist semantics. In: Proceedings of Theoretical Issues in Natural Language Processing 3:63–67. New Mexico State University.Google Scholar
Cox, R. T. (1946) Probability, frequency, and reasonable expectation. American Journal of Statistical Physics 14:1–13.CrossRefGoogle Scholar
Crick, F. & Asanuma, C. (1986) Certain aspects of the anatomy and physiology of the cerebral cortex. In: Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 2: Psychological and biological models, ed. McClelland, J. L., Rumelhart, D. E. & the PDP Research Group. MIT Press/Bradford Books.Google Scholar
Davidson, D. (1970) Mental events. In; Experience and theory, ed. Foster, L. & Swanson, J. W.. University of Massachusetts Press.Google Scholar
DeJong, G. (1983) An approach to learning from observation. Proceedings of the International Machine Learning Workshop. University of Illinois.Google Scholar
Dell, G. S. (1985) Positive feedback in hierarchical connectionist models: Applications to language production. Cognitive Science 9:3–23.Google Scholar
Dennett, D. C. (1986) The logical geography of computational approaches: A view from the east pole. In: The representation of knowledge and belief, ed. Brand, M. & Harnish, R. M.. University of Arizona Press.Google Scholar
Derthick, M. A. (1986) A connectionist knowledge representation system. Thesis proposal, Computer Science Department, Carnegie-Mellon University.Google Scholar
Derthick, M. A. (1987a) Counterfactual reasoning with direct models. Proceedings of the Sixth National Conference on Artificial Intelligence, Seattle, Wash., 346–51.Google Scholar
Derthick, M. A. (1987b) A connectionist architecture for representing and reasoning about structured knowledge. Proceedings of the Ninth Annual Conference of the Cognitive Science Society, Seattle, Wash., 131–42.Google Scholar
Dolan, C. & Dyer, M. G. (1987) Evolution of an architecture for symbol processing. Proceedings of the IEEE First Annual International Conference on Neural Networks, San Diego, Calif.Google Scholar
Dolan, C. (1987) Symbolic schemata, role binding, and the evolution of structure in connectionist memories. Proceedings of the IEEE First Annual International Conference on Neural Networks, San Diego, Calif.Google Scholar
Dresner, E. & Hornstein, N. (1976) On some supposed contributions of artificial intelligence to the scientific study of language. Cognition 4:321–98.Google Scholar
Dreyfus, H. L. & Dreyfus, S. E. (in press) Making a mind vs. modeling the brain: AI back at a branchpoint. Daedalus.Google Scholar
Feigenbaum, E. A. (1963) The simulation of verbal learning behavior. In: Computers and thought, ed. Feigenbaum, E. A. & Feldman, J.. McGraw-Hill.Google Scholar
Feldman, J. A. (1981) A connectionist model of visual memory. In: Parallel models of associative memory, ed. Hinton, G. E. & Anderson, J. A.. Erlbaum.Google Scholar
Feldman, J. A. (1985) Four frames suffice: A provisional model of vision and space. Behavioral and Brain Sciences 8:265–89.CrossRefGoogle Scholar
Feldman, J. A. (1986) Neural representation of conceptual knowledge. Technical Report 189, Department of Computer Science, University of Rochester.Google Scholar
Feldman, J. A. & Ballard, D. H. (1982) Connectionist models and their properties. Cognitive Science 6:205–54.Google Scholar
Feldman, J. A., Ballard, D. H., Brown, C. M. & Dell, G. S. (1985) Rochester connectionist papers: 1979–1985. Technical Report 172, Department of Computer Science, University of Rochester.Google Scholar
Fields, C. & Dietrich, E. (1987) A stochastic computing architecture for multi-domain problem solving. Proceeding of the International Symposium on Methodologies for Intelligent Systems (in press).Google Scholar
Fodor, J. A. (1975) The language of thought. Harvard University Press.Google Scholar
Fodor, J. A. (1986) Information and association. Notre Dame Journal of Formal Logic 27:307–23.Google Scholar
Fodor, J. A. (1987) Why there still has to be a language of thought. In: Psychosemantics, ed. Fodor, J. A.. MIT Press/Bradford Books.Google Scholar
Fodor, J. A. (1983) The modularity of mind. MIT Press.Google Scholar
Fodor, J. A. & Pylyshyn, Z. W. (1988) Connectionism & cognitive architecture: A critical analysis. Cognition 28: 3–71.Google Scholar
Freeman, W. J. (1975) Mass action in the nervous system. Academic Press.Google Scholar
Freeman, W. J. (1987) Simulation of chaotic EEC patterns with a dynamic model of the olfactory system. Biological Cybernetics 56:139–50.Google Scholar
Freeman, W. J. (in press) Hardware simulation of brain dynamics of learning: The SPOCK. First International IEEE Conference on Neural Networks, San Diego.Google Scholar
Freeman, W. J. & Skarda, C. A. (1985) Spatial EEG patterns, nonlinear dynamics and perception: The neo-Sherringtonian view. Brain Research Reviews 10:147–75.Google Scholar
Freidin, R. (1987) Foundations of generative syntax. Manuscript.Google Scholar
Geman, S. & Geman, D. (1984) Stochastic relaxation, Gibbs distributions, the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6:721–41.Google Scholar
Cillispie, C. C. (1960) The edge of objectivity: An essay in the history of scientific ideas. Princeton University Press.Google Scholar
Golden, R. M. (submitted) A probabilistic computational framework for neural network models.Google Scholar
Goldman, A. I. (1970) A theory of human action. Prentice-Hall.Google Scholar
Grossberg, S. (1976) Adaptive pattern classification and universal recoding. Biological Cybernetics 23:121–34; 187–202 (in two parts).Google Scholar
Grossberg, S. (1980) How does the brain build a cognitive code? Psychological Review 87:1–51.Google Scholar
Grossberg, S. (1987) Competitive learning: From interactive activation to adaptive resonance. Cognitive Science 11:23–63.Google Scholar
Grossberg, S. ed. (1987a) The adaptive brain I: Cognition, learning, reinforcement, and rhythm. North-Holland,Google Scholar
Grossberg, S. ed. (1987b) The adaptive brain II: Vision, speech, language, and motor control. North-Holland.Google Scholar
Grossberg, S. & Mingolla, E. (1985) Neural dynamics of form perception: Boundary completion, illusory figures, and neon color spreading. Psychological Review 92:173–211.Google Scholar
Grossberg, S. & Stone, G. (1986) Neural dynamics of word recognition and recall: Attentional priming, learning, and resonance. Psychological Review 93:46–74.CrossRefGoogle ScholarPubMed
Grossberg, S. (1986) Neural dynamics of attention switching and temporal order information in short-term memory. Memory and Cognition 14:451–68.Google Scholar
Halle, M. (1962) Phonology in generative grammar. Word 18:54–72.Google Scholar
Hammond, K. J. (1986) Case-based planning: An integrated theory of planning, learning and memory. Ph.D. Thesis, Yale University.Google Scholar
Hanson, S. J. & Kegl, J. (1987) PARSNIP: A connectionist network that learns natural language grammar on exposure to natural language sentences. Unpublished manuscript, Bell Communications Research and Princeton University.Google Scholar
Haugeland, J. (1978) The nature and plausibility of cognitivism. Behavioral and Brain Sciences 1:215–26.Google Scholar
Hebb, D. O. (1949) The organization of behavior. Wiley.Google Scholar
Hinton, G. E. (1981) Implementing semantic networks in parallel hardware. In: Parallel models of associative memory, ed. Hinton, G. E. & Anderson, J. A.. Erlbaum.Google Scholar
Hinton, G. E. & Anderson, J. A., eds. (1981) Parallel models of associative memory. Erlbaum.Google Scholar
Hinton, G. E., McClelland, J. L. & Rumelhart, D. E. (1986) Distributed representations. In: Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 1: Foundations, ed. McClelland, J. L., Rumelhart, D. E. & the PDP Research Group. MIT Press/Bradford Books.Google Scholar
Hinton, G. E. & Plaut, D. C. (1987) Using weights to deblur old memories. In: Proceedings of the Ninth Annual Cognitive Science Society Conference. Erlbaum.Google Scholar
Hinton, G. E. & Sejnowski, T. J. (1983a) Analyzing cooperative computation. Proceedings of the Fifth Annual Conference of the Cognitive Science Society.Google Scholar
Hinton, G. E. & Sejnowski, T. J. (1983b) Optimal perceptual inference. Proceedings of the l.E.E.E. Conference on Computer Vision and Pattern Recognition.Google Scholar
Hinton, G. E. & Sejnowski, T. J. (1986) Learning and relearning in Boltzmann machines. In: Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 1: Foundations, ed. McClelland, J. L., Rumelhart, D. E. & the PDP Research Group. MIT Press/Bradford Books.Google Scholar
Hinton, G. E., Sejnowski, T. J. & Ackley, D. H. (1984) Boltzmann machines: Constraint satisfaction networks that learn. Technical report CMUCS–84–119, Computer Science Department, Carnegie-Mellon University.Google Scholar
Hofstadter, D. R. (1979) Godel, Escher, Bach: An eternal golden braid. Basic Books.Google Scholar
Hofstadter, D. R. (1983) The architecture of Jumbo. Proceedings of the International Machine Learning Workshop.Google Scholar
Hofstadter, D. R. (1984) The Copycat Project: An experiment in nondeterminism and creative analogies. AI Memo 755, MIT Artificial Intelligence Laboratory.Google Scholar
Hofstadter, D. R. (1985a) Variations on a theme as the crux of creativity. In: Metamagical themas. Basic Books.Google Scholar
Hofstadter, D. R. (1985b) Waking up from the Boolean dream, or, subcognition as computation. In: Metamagical themas. Basic Books.Google Scholar
Holland, J. H. (1986) Escaping brittleness: The possibilities of generalpurpose machine-learning algorithms applied to parallel rule-based systems. In: Machine learning: An artificial-intelligence approach, vol. 2, ed. Michalski, R. S. et al. William Kaufmann.Google Scholar
Holland, J. H., Holyoak, K. J., Nisbitt, R. E. & Thagard, P. (1986) Induction: Processes of learning, inference and discovery. Bradford Books.Google Scholar
Hopcroft, J. E. & Ullman, J. D. (1979) Introduction to automata theory, languages, and computation. Addison-Wesley.Google Scholar
Hopfield, J. J. (1982) Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Science 79:2554–58.Google Scholar
Hopfield, J. J. (1984) Neurons with graded response have collective properties like those of two-state neurons. Proceedings of the National Academy of Sciences, USA 81:3088–92.Google Scholar
Horgan, T. (1982) Supervenience and microphysics. Pacific Philosophical Quarterly 63:29–43.Google Scholar
Hummel, R. A. & Zucker, S. W. (1983) On the foundations of relaxation labeling processes. IEEE Transactions on Pattern Analysis and Machine Intelligence 5:267–87.CrossRefGoogle ScholarPubMed
Jakobson, R., Fant, G. & Halle, M. (1951) Preliminaries to speech analysis. MIT Press.Google Scholar
James, W. (1967) The writings of William James: A comprehensive edition. Random House.Google Scholar
Jeffreys, H. (1983) Theory of probability. Clarendon.Google Scholar
Jordan, M. I. (1986) Attractor dynamics and parallelism in a connectionist sequential machine. Proceedings of the Eighth Meeting of the Cognitive Science Society.Google Scholar
Kaas, A. (1986) Modifying explanations to understand stories. Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Amherst, Ma.Google Scholar
Kant, I. (1787/1963) Critique of pure reason. Smith, N. Kemp, trans., 2nd ed.Macmillan.Google Scholar
Kohonen, T. (1984) Self-organization and associative memory. Springer-Verlag.Google Scholar
Kosslyn, S. M. (1987) Seeing and imaging in the cerebral hemispheres: A computational approach. Psychological Review 94:148–75.Google Scholar
Kripke, S. (1980) Wittgenstein on following a rule. Harvard University Press.Google Scholar
Lakoff, G. (1987) Women, fire, and dangerous things: What categories reveal about the mind. University of Chicago Press.Google Scholar
Langacker, R. (1987) Foundations of cognitive grammar. Stanford University Press.Google Scholar
Larkin, J. H., McDermott, J., Simon, D. P. & Simon, H. A. (1980) Models of competence in solving physics problems. Cognitive Science 4:317–45.Google Scholar
Lashley, K. (1950) In search of the engram. In: Psychological mechanisms in animal behavior. Symposia of the Society for Experimental Biology, No. 4. Academic Press.Google Scholar
Lewis, C. H. (1978) Production system models of practice effects. Unpublished doctoral dissertation, University of Michigan.Google Scholar
Luenberger, D. G. (1984) Linear and nonlinear programming. Addison-Wesley.Google Scholar
Luria, A. R. (1966) Higher cortical functions in man. Basic Books.Google Scholar
Lycan, W. G. (1981) Form, function, and feel. Journal of Philosophy 78:24–50.Google Scholar
Lycan, W. G. (1987) Consciousness. MIT Press/Bradford Books.Google Scholar
Marr, D. (1982) Vision. W. H. Freeman.Google Scholar
McClelland, J. L. (1987) Parallel distributed processing and role assignment constraints. In: Proceedings of Theoretical Issues in Natural Language Processing 3:73–77. New Mexico State University.Google Scholar
McClelland, J. L. & Kawamoto, A. H. (1986) Mechanisms of sentence processing: Assigning roles to constituents of sentences. In: Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 2: Psychological and biological models, ed. McClelland, J. L., Rumelhart, D. E. & the PDP Research Croup. MIT Press/Bradford Books.Google Scholar
McClelland, J. L. & Rumelhart, D. E. (1981) An interactive activation model of context effects in letter perception: Part I. An account of th e basic findings. Psychological Review 88:375–407.Google Scholar
McClelland, J. L., Rumelhart, D. E. & the PDP Research Group (1986) Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 2: Psychological and biological models. MIT Press/Bradford Books.Google Scholar
Miikkulainen, R. & Dyer, M. G. (1987) Building distributed representations without microfeatures. Technical report UCLA-AI-87–17, Computer Science Dept, UCLA, Los Angeles, Calif.Google Scholar
Miller, G. A. & Chomsky, N. (1963) Finitary models of language users. In: Handbook of mathematical psychology, ed. Bush, R. R., Galanter, E. & Luce, R. D.. Wiley.Google Scholar
Minsky, M. (1963) Steps toward artificial intelligence. In: Computers and thought, ed. Feigenbaum, E. A. & Feldman, J.. McGraw-Hill.Google Scholar
Minsky, M. (1975) A framework for representing knowledge. In: The psychology of computer vision, ed. Winston, P. H.. McGraw-Hill.Google Scholar
Mishkin, M., Malamut, B. & Bachevalier, J. (1984) Memories and habits: Two neural systems. In: Neurobiology of learning and memory, ed. Lynch, G., McGauth, J. L. & Weinberger, N. M..Google Scholar
Minsky, M. & Papert, S. (1969) Perception. MIT Press.Google Scholar
Moran, J. & Desimone, R. (1985) Selective attention gates visual processing in the extrastriate cortex. Science 229:782–84.Google Scholar
Nelson, R. J. (1982) The logic of mind. D. Reidel.Google Scholar
Nelson, R. J. (1987a) Church's Thesis in cognitive science. Notre Dame Journal of Formal Logic. Forthcoming.Google Scholar
Nelson, R. J. (1987b) Models for cognitive science. Philosophy of Science. Forthcoming.Google Scholar
Newell, A. (1980) Physical symbol systems. Cognitive Science 4:135–83.Google Scholar
Newell, A., Shaw, J. C. & Simon, H. A. (1958) Elements of a theory of human problem solving. Psychological Review 65:151–66.Google Scholar
Newell, A. & Simon, H. A. (1972) Human problem solving. Prentice-Hall.Google Scholar
Newell, A. & Simon, H. A. (1976) Computer science as empirical inquiry: Symbols and search. Communications of the Association for Computing Machinery 19:113–26.Google Scholar
Oden, G. C. (1977) Integration of fuzzy logical information. Journal of Experimental Psychology: Human Perception and Performance 3:565–75.Google Scholar
Oden, G. C. (1987) Concept, knowledge, and thought. Annual Review of Psychology 38:203–28.Google Scholar
Pazanni, M. & Dyer, M. G. (1987) A comparison of concept identification in human learning and network learning with the generalized delta rule. Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-87). 08, Milan, Italy.Google Scholar
Pearl, J. (1985) Bayesian networks: A model of self-activated memory for evidential reasoning. Proceedings of the Seventh Conference of the Cognitive Science Society.Google Scholar
Pinker, S. (1984) Language learnability and language development. Harvard University Press.Google Scholar
Pinker, S. & Prince, A. (1988) On language and connectionism: Analysis of a parallel distributed processing model of language acquisition. Cognition 28: 73–193.Google Scholar
Pollack, J. B. (1987) On connectionist models of natural language processing MCCS-87–100. Ph.D. dissertation, Computing Research Laboratory, New Mexico State University.Google Scholar
Putnam, H. (1975) Philosophy and our mental life. In: Philosophical papers, vol. 2. Cambridge University Press.Google Scholar
Putnam, H. (1980) Philosophy and our mental life. In: Readings in philosophy of psychology, vol. 1, ed. Block, N.. Harvard University Press.Google Scholar
Pylyshyn, Z. W. (1984) Computation and cognition: Toivard a foundation for cognitive science. MIT Press/Bradford Books.Google Scholar
Reddy, D. R., Erman, L. D., Fennell, R. D. & Neely, R. B. (1973) The HEARSAY speech understanding system: An example of the recognition process. Proceedings of the Third International Joint Conference on Artificial Intelligence, Stanford, Calif.Google Scholar
Reeves, A. & Sperling, G. (1986) Attentional theory of order information in short-term memory. Psychological Review 93:180–206.Google Scholar
Rey, G. (1983) Concepts and stereotypes. Cognition 15:237–62.Google Scholar
Rey, G. (1985) Concepts and conceptions. Cognition 19:297–303.Google Scholar
Riley, M. S. & Smolensky, P. (1984) A parallel model of (sequential) problem solving. Proceedings of the Sixth Annual Conference of the Cognitive Science Society.Google Scholar
Rumelhart, D. E. (1975) Notes on a schema for stories. In: Representation and understanding, ed. Bobrow, D. G. & Collins, A.. Academic Press.Google Scholar
Rumelhart, D. E. (1980) Schemata: The building blocks of cognition. In: Theoretical issues in reading comprehension, ed. Spiro, R., Bruce, B. & Brewer, W.. Erlbaum.Google Scholar
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986) Learning and internal representations by error propagation. In: Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 1: Foundations, ed. McClelland, J. L., Rumelhart, D. E. & the PDP Research Group. MIT Press/Bradford Books.Google Scholar
Rumelhart, D. E. & McClelland, J. L. (1982) An interactive activation model of context effects in letter perception: Part 2. The contextual enhancement effect and some tests and extensions of the model. Psychological Review 89:60–94.Google Scholar
Rumelhart, D. E. & McClelland, J. L. (1986) On learning the past tenses of English verbs. In: Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 2: Psychological and biological models, ed. McClelland, J. L., Rumelhart, D. E. & the PDP Research Group. MIT Press/Bradford Books.Google Scholar
Rumelhart, D. E. & McClelland, J. L. (1986a) PDP models and general issues in cognitive science. In: Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1, ed. McClelland, J. L., Rumelhart, D. E. & the PDP Research Group. MIT Press/Bradford Books.Google Scholar
Rumelhart, D. E., McClelland, J. L. & the PDP Research Group (1986) Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 1: Foundations. MIT Press/Bradford Books.Google Scholar
Rumelhart, D. E., Smolensky, P., McClelland, J. L. & Hinton, G. E. (1986) Schemata and sequential thought processes in parallel distributed processing models. In: Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 2: Psychological and biological models, ed. McClelland, J. L., Rumelhart, D. E. & the PDP Research Group. MIT Press/Bradford Books.Google Scholar
Savage, L. J. (1971) The foundations of statistics. Wiley.Google Scholar
Schank, R. C. (1972) Conceptual dependency: A theory of natural language understanding. Cognitive Psychology 3(4):552–631.Google Scholar
Schank, R. C. (1975) Conceptual information processing. North-Holland.Google Scholar
Schank, R. C. (1982) Dynamic memory: A theory of learning in computers and people. Cambridge University Press.Google Scholar
Schank, R. C. & Abelson, R. (1977) Scripts, plans, goals and understanding. Erlbaum.Google Scholar
Schank, R. C., Collins, G. C. & Hunter, L. E. (1986) Transcending inductive category formation in learning. Behavioral and Brain Sciences 9:639–86.Google Scholar
Schneider, W. & Detweiler, M. (1987) A connectionist/control architecture for working memory. In: The psychology of learning and motivation, vol. 21, ed.Bower, G. H.. Academic Press.Google Scholar
Schneider, W. & Mumme, D. (forthcoming) Attention, automaticity and the capturing of knowledge: A two-level cognitive architecture.Google Scholar
Searle, J. R. (1980) Minds, brains, and programs. Behavioral and Brain Sciences 3:417–57.Google Scholar
Sejnowski, T. J. (1976) On the stochastic dynamics of neuronal interactions. Biological Cybernetics 22:203–11.Google Scholar
Sejnowski, T. J. & Rosenberg, C. R. (1986) NETtalk: A parallel network that learns to read aloud. Technical Report JHU/EECS-86/01, Department of Electrical Engineering and Computer Science, John Hopkins University.Google Scholar
Sejnowski, T. J. & Rosenberg, C. R. (1987) Parallel networks that learn to pronounce English text. Complex Systems 1:145–68.Google Scholar
Selfridge, O. G. (1959) Pandemonium: A paradigm for learning. In: Symposium on the mechanisation of thought processes. London: H. M. Stationery Office.Google Scholar
Shastri, L. (1985) Evidential reasoning in semantic networks: A formal theory and its parallel implementation. Technical Report TR 166, Department of Computer Science, University of Rochester.Google Scholar
Shastri, L. (1987) A connectionist encoding of semantic networks. In: Proceedings of the Ninth Annual Conference of the Cognitive Science Society, 143–54.Google Scholar
Shastri, L. & Feldman, J. A. (1985) Evidential reasoning in semantic networks: A formal theory. In: Proceedings of the International Joint Conference on Artificial Intelligence, Los Angeles, 465–74.Google Scholar
Shastri, L. & Feldman, J. A. (1988)The constituent structure of connectionist mental states: A reply to Fodorand Pylyshyn, Southern Journal of Philosophy. Special issue on connectionism and the foundations of cognitive science. In press.Google Scholar
Shepard, R. N. (1962) The analysis of proximities: Multidimensional scaling with an unknown distance function. I & II. Psychometrika 27:125–40, 219–46.Google Scholar
Shepard, R. N. (1964) Review of Computers and thought (ed. Feigenbaum, E. & Feldman, J.). Behavioral Science 9:57–65.Google Scholar
Shepard, R. N. (1981) Psychophysical complementarity. In: Perceptual organization, ed. Kubovy, M. & Pomerantz, J.. Erlbaum.Google Scholar
Shepard, R. N. (1984) Ecological constraints on internal representation: Resonant kinematics of perceiving, imagining, thinking, and dreaming. Psychological Review 91:417–47.Google Scholar
Shepard, R. N. (1987) Towards a universal law of generalization for psychological science. Science (in press).Google Scholar
Shepard, R. N. & Cooper, L. A. (1982) Mental images and their transformations. MIT Press/Bradford Books.Google Scholar
Shepard, R. N. & Metzler, J. (1971) Mental rotation of three-dimensional objects. Science 171:701–3.Google Scholar
Shiffrin, R. M. & Schneider, W. (1977) Controlled an d automatic human information processing. II. Perceptual learning. Psychological Review 84:127–90.Google Scholar
Skarda, C. A. & Freeman, W. J. (1987) How brains make chaos in order to make sense of the world. Behavioral and Brain Sciences 10:161–95.Google Scholar
Smale, S. (1987) On the topology of algorithms, I. Journal of Complexity 3:81–89.Google Scholar
Smart, J. J. C. (1959) Sensations and brain processes. Philosophical Review 141–56.Google Scholar
Smolensky, P. (1983) Schema selection and stochastic inference in modular environments. Proceedings of the National Conference on Artificial Intelligence.Google Scholar
Smolensky, P. (1984a) Harmony theory: Thermal parallel models in a computational context. In: Harmony theory: Problem solving, parallel cognitive models, and thermal physics, ed. Smolensky, P. & Riley, M. S.. Technical Report 8404, Institute for Cognitive Science, University of California at San Diego.Google Scholar
Smolensky, P. (1984b) The mathematical role of self-consistency in parallel computation. Proceedings of the Sixth Annual Conference of the Cognitive Science Society.Google Scholar
Smolensky, P. (1986a) Information processing in dynamical systems: Foundations of harmony theory. In: Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 1: Foundation, ed. McClelland, J. L., Rumelhart, D. E. & the PDP Research Group. MIT Press/Bradford Books.Google Scholar
Smolensky, P. (1986b) Neural and conceptual interpretations of parallel distributed processing models. In: Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 2: Psychological and biological models, ed. McClelland, J. L., Rumelhart, D. E. & the PDP Research Group. MIT Press/Bradford Books.Google Scholar
Smolensky, P. (1986c) Formal modeling of subsymbolic processes: An introduction to harmony theory. In: Directions in the science of cognition, ed. Sharkey, N. E.. Ellis Horwood.Google Scholar
Smolensky, P. (1987) On variable binding and th e representation of symbolic structures in connectionist systems. Technical Report CU-CS-355–87, Department of Computer Science, University of Colorado at Boulder.Google Scholar
Smolensky, P. (1987a) Connectionist AI, symbolic Al, and the brain. Artificial Intelligence Review 1:95–109.Google Scholar
Smolensky, P. (1988) The constituent structure of connectionist mental states: A reply to Fodor and Pylyshyn. Southern Journal of Philosophy. Special issue on connectionism and the foundations of cognitive science.Google Scholar
Stich, S. P. (1983) From folk psychology to cognitive science. MIT Press.Google Scholar
Tolman, E. C. (1932) Purposive behavior in animals and men. Appleton-Century-Crofts.Google Scholar
Toulouse, G., Dehaene, S. & Changeux, J.-P. (1986) A spin glass model of learning by selection. Technical Report, Unite de Neurobiologie Moleculaire, Institut Pasteur, Paris.Google Scholar
Touretzky, D. S. (1986) BoltzCONS: Reconciling connectionism with the recursive nature of stacks and trees. Proceedings of the Eighth Conference of the Cognitive Science Society.Google Scholar
Touretzky, D. S. (1987) Representing conceptual structures in a neural network. Proceedings of the IEEE First Annual International Conference on Neural Networks, San Diego, Calif.Google Scholar
Touretzky, D. S. & Geva, S. (1987) A distributed connectionist representation for concept structures. In: Proceedings of the Ninth Annual Conference of the Cognitive Science Society, 155–64.Google Scholar
Touretzky, D. S. & Hinton, G. E. (1985) Symbols among the neurons: Details of a connectionist inference architecture. Proceedings of the International Joint Conference on Artificial Intelligence.Google Scholar
Turing, A. (1936) On computable numbers, with an application to Entscheidungs problem. Proceedings of the London Mathematical Society (Ser. 2) 42:230–65 and 43:544–46.Google Scholar
Tversky, A. & Kahneman, D. (1983) Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychological Review 90:293–315.Google Scholar
Ullman, S. (1984) Visual routines. Cognition 18:97–159.Google Scholar
den Uyl, M. J. (1986) Representing magnitud e by memory resonance: A hypothesis on qualitative judgment. Proceedings of the Eighth Annual Conference on the Cognitive Science Society.Google Scholar
Van Essen, D. C. (1985) Functional organization of primate visual cortex. The cerebral cortex, vol. 3.Google Scholar
Waldrop, M. M. (1984) Artificial intelligence in parallel. Science 225:608–10.Google Scholar
Waltz, D. L. (1978) An English language question answering system for a large relational database. Communications of the Association for Computing Machinery 21:526–39.Google Scholar
Waltz, D. L. & Pollack, J. B. (1985) Massively parallel parsing: A strongly interactive model of natural language interpretation. Cognitive Science 9:51–74.Google Scholar
Wilks, Y. A. (1978) Making preference more active. Artificial Intelligence 11:197–223.Google Scholar
Wittgenstein, L. (1956) Remarks on the foundations of mathematics. Black-well.Google Scholar