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.Google Scholar

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.CrossRefGoogle 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.Google 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed

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.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar

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.CrossRefGoogle ScholarPubMed

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.Google Scholar

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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed

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.CrossRefGoogle 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.CrossRefGoogle 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