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• W4229750790 abstract "Free Access References Michael R. W. Dawson, Michael R. W. DawsonSearch for more papers by this author Book Author(s):Michael R. W. Dawson, Michael R. W. DawsonSearch for more papers by this author First published: 01 January 2005 https://doi.org/10.1002/9780470694077.refs AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat References Aldenderfer, M. S. & Blashfield, R. K., (1984). Cluster Analysis, vol. 07–044. Beverly Hills, CA: Sage Publications. CrossrefWeb of Science®Google Scholar Anderson, J. A. & Rosenfeld, E., (1998). Talking Nets: An Oral History of Neural Networks. Cambridge, MA: MIT Press. Google Scholar Ballard, D., (1986). Cortical structures and parallel processing: Structure and function The Behavioral And Brain Sciences, 9, 67– 120. CrossrefWeb of Science®Google Scholar Barlow, H. B., (1972). Single units and sensation: A neuron doctrine for perceptual psychology? Perception. 1, 371– 94. CrossrefCASPubMedGoogle Scholar Berkeley, I. S. N., Dawson, M. R. W., Medler, D. A., Schopflocher, D. P. & Hornsby, L., (1995). Densityplots of hidden value unit activations reveal interpretable bands. Connection Science, 7, 167– 86. CrossrefGoogle Scholar Bever, T. G., Fodor, J. A. & Garrett, M., (1968). A formal limitation of associationism. In T. R. Dixon & D. L. Horton (eds), Verbal Behavior and General Behavior Theory (pp. 582– 5). Englewood Cliffs, NJ: Prentice Hall. Google Scholar Blake, C. L. & Merz, C. J., (1998). UCI Repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science. Available: [http://www.ics.uci.edu/˜mlearn/MLRepositoryhtml]. Google Scholar Blyth, T. S. & Robertson, E. F., (1986). Matrices and Vector Spaces, vol. 2. London: Chapman and Hall. Google Scholar Boole, G., (1854). An investigation of the laws of thought, on which are founded the mathematical theories of logic and probabilities. London: Walton and Maberley CrossrefGoogle Scholar Boole, G., (1952). Studies in logic and probability: Watts. Google Scholar Braitenberg, V, (1984). Vehicles: Explorations In Synthetic Psychology. Cambridge, MA: MIT Press. Web of Science®Google Scholar Brown, T H., (1990). Hebbian synapses: Biophysical mechanisms and algorithms. Annual Review of Neuroscience, 13, 475– 511. CrossrefCASPubMedWeb of Science®Google Scholar Calkins, M. W., (1894). Association. Psychological Review, 1, 476– 83. CrossrefGoogle Scholar Calvin, W H., (1996a). The Cerebral Code. Cambridge, MA: MIT Press. Google Scholar Calvin, W H., (1996b). How Brains Think. New York, NY: Basic Books. Google Scholar Carpenter, G A., (1989). Neural network models for pattern recognition and associative memory. Neural Networks, 2, 243– 57. CrossrefWeb of Science®Google Scholar Caudill, M. & Butler, B., (1992a). Understanding neural networks, vol. 1. Cambridge, MA: MIT Press. Web of Science®Google Scholar Caudill, M. & Butler, B., (1992b). Understanding neural networks, vol. 2. Cambridge, MA: MIT Press. Web of Science®Google Scholar Changeux, J.-P. & Dehaene, S., (1993). Neuronal models of cognitive functions. In M. H. Johnson (ed.), Cognition and Brain Development: A reader (pp. 363– 402). Oxford: Blackwell. Google Scholar Clark, A., (1989). Microcognition. Cambridge, MA: MIT Press. Google Scholar Clark, A., (1993). Associative engines. Cambridge, MA: MIT Press. Google Scholar Cotman, C. W., Monaghan, D. T. & Ganong, A. H., (1988). Excitatory amino acid neurotransmission: NMDA receptors and Hebb-type synaptic plasticity. Annual Review of Neuroscience, 11, 61– 80. CrossrefCASPubMedWeb of Science®Google Scholar Cotter, N. E., (1990). The Stone-Weierstrass theorem and its application to neural networks IEEE Transactions on Neural Networks, 1, 290– 5. CrossrefCASPubMedGoogle Scholar Cummins, R., (1983). The Nature of Psychological Explanation. Cambridge, MA.: MIT Press. Google Scholar Cybenko, G., (1989). Approximation by superpositions of a sigmoidal function Mathematics of Control, Signals, and Systems, 2, 303– 314. CrossrefGoogle Scholar Cziko, G, (1995). Without Miracles: Universal Selection Theory and the Second Darwinian Revolution. Cambridge, MA: MIT Press. Google Scholar Dallett, K. M., (1966). Effects of within-list and between-list acoustic similarity on the learning and retention of paired associates. Journal of Experimental Psychology, 72 (5), 667– 77. CrossrefCASPubMedWeb of Science®Google Scholar Dawson, M. R. W., (1991). The how and why of what went where in apparent motion: Modeling solutions to the motion correspondence process. Psychological Review, 98, 569– 603. CrossrefCASPubMedWeb of Science®Google Scholar Dawson, M. R. W., (1998). Understanding Cognitive Science. Oxford, UK: Blackwell. Google Scholar Dawson, M. R. W., (2002). From embodied cognitive science to synthetic psychology. In Wang, Y., Johnston, R. H. & Smith, M. R. (eds) Proceedings of the First IEEE International Conference on Cognitive Informatics (ICCI?02). IEEE Computer Society: Los Alamitos, CA, (pp. 13– 22) Google Scholar Dawson, M. R. W., (2004). Minds And Machines: Connectionism and Psychological Modeling. Malden, MA: Blackwell Publishing. Wiley Online LibraryGoogle Scholar Dawson, M. R. W., Boechler, P. M. & Valsangkar-Smyth, M., (2000). Representing space in a PDP network: Coarse allocentric coding can mediate metric and nonmetric spatial judgements. Spatial Cognition and Computation, 2, 181– 218. CrossrefGoogle Scholar Dawson, M. R. W., Kremer, S. & Gannon, T, (1994). Identifying the trigger features for hidden units in a PDP model of the early visual pathway. In R. Elio (ed.), Tenth Canadian Conference On Artificial Intelligence (pp. 115– 19). San Francisco, CA: Morgan Kaufmann. Web of Science®Google Scholar Dawson, M. R. W. & Medler, D. A., (1996). Of mushrooms and machine learning: Identifying algorithmsin a PDP network. Canadian Artificial Intelligence, 38, 14– 17. Google Scholar Dawson, M. R. W., Medler, D. A. & Berkeley, I. S. N., (1997). PDP networks can provide models that arenot mere implementations of classical theories. Philosophical Psychology, 10, 25– 40. CrossrefWeb of Science®Google Scholar Dawson, M. R. W., Medler, D. A., McCaughan, D. B., Willson, L. & Carbonaro, M., (2000). Using extra output learning to insert a symbolic theory into a connectionist network. Minds and Machines, 10, 171– 201. CrossrefWeb of Science®Google Scholar Dawson, M. R. W. & Piercey, C. D., (2001). On the subsymbolic nature of a PDP architecture that uses anonmonotonic activation function. Minds and Machines, 11, 197– 218. CrossrefWeb of Science®Google Scholar Dawson, M. R. W. & Pylyshyn, Z. W., (1988). Natural constraints on apparent motion. In Z. W. Pylyshyn (ed.), Computational Processes in Human Vision: An Interdisciplinary Perspective (pp. 99– 120). Norwood, NJ: Ablex. Google Scholar Dawson, M. R. W. & Schopflocher, D. P., (1992a). Autonomous processing in PDP networks Philosophical Psychology, 5, 199– 219. CrossrefWeb of Science®Google Scholar Dawson, M. R. W. & Schopflocher, D P., (1992b). Modifying the generalized delta rule to train networksof nonmonotonic processors for pattern classification. Connection Science, 4, 19– 31. CrossrefPubMedWeb of Science®Google Scholar Dawson, M. R. W. & Shamanski, K. S., (1994). Connectionism, confusion and cognitive science Journal of Intelligent Systems, 4, 215– 62. CrossrefCASPubMedGoogle Scholar Dawson, M. R. W. & Zimmerman, C., (2003). Interpreting the internal structure of a connectionistmodel of the balance scale task. Brain and Mind, 4, 129– 49. CrossrefWeb of Science®Google Scholar De Wilde, P., (1997). Neural Network Models, 2nd edn. London: Springer. CrossrefGoogle Scholar Deese, J. & Hulse, S. H., (1967). The Psychology Of Learning, 3rd edn. New York, NY: McGraw-Hill. Google Scholar Delamater, A. R., Sosa, W. & Koch, M., (1999). Elemental and configural processes in patterningdiscrimination learning. The Quarterly Journal Of Experimental Psychology, 52B, 97– 124. Google Scholar Duch, W. & Jankowski, N., (1999). Survey of neural transfer functions. Neural Computing Surveys, 2, 163– 212. Google Scholar Dutton, J. M. & Starbuck, W. H., (1971). Computer simulation of human behavior. New York: John Wiley & Sons. Google Scholar Edelman, G. M., (1987). Neural Darwinism. New York: Basic Books. Google Scholar Edelman, G. M., (1988). Topobiology. New York: Basic Books. Google Scholar Edelman, G. M., (1989). The Remembered Present. New York: Basic Books. Google Scholar Edelman, G. M., (1992). Bright Air, Brilliant Fire. New York: Basic Books. Google Scholar Eich, J. M., (1982). A composite holographic associative recall model. Psychological Review, 89, 627– 61. CrossrefWeb of Science®Google Scholar Eichenbaum, H., (1992). The Hippocampal System and Declarative Memory in Animals Journal of Cognitive Neuroscience, 4 (3), 217– 31. CrossrefCASPubMedWeb of Science®Google Scholar Eichenbaum, H., (2000). A cortical-hippocampal system for declarative memory Nature Reviews Neuroscience, 1 (1), 41– 50. CrossrefCASPubMedWeb of Science®Google Scholar Eichenbaum, H., Dudchenko, P., Wood, E., Shapiro, M. & Tanila, H., (1999). The hippocampus, memoryand place cells: Is it spatial memory or a memory space? Neuron, 23 (2), 209– 26. CrossrefCASPubMedWeb of Science®Google Scholar Everitt, B., (1980). Cluster Analysis. New York: Halsted. Google Scholar Fodor, J. A. & Pylyshyn, Z. W, (1988). Connectionism and cognitive architecture. Cognition, 28, 3– 71. CrossrefCASPubMedWeb of Science®Google Scholar Freeman, J. A., (1994). Simulating Neural Networks With Mathematica. Reading, MA: Addison-Wesley. Google Scholar Funahashi, K., (1989). On the approximate realization of continuous mappings by neural networks. Neural Networks, 2, 183– 92. CrossrefWeb of Science®Google Scholar Furumoto, L., (1980). Mary Whiton Calkins (1863–1930). Psychology of Women Quarterly, 5, 55– 68. Wiley Online LibraryWeb of Science®Google Scholar Gallant, S. I., (1993). Neural network learning and expert systems. Cambridge, MA: MIT Press. CrossrefGoogle Scholar Gallistel, C. R., (1990). The Organization of Learning. Cambridge, MA: MIT Press. Google Scholar Girosi, F. & Poggio, T, (1990). Networks and the best approximation property. Biological Cybernetics, 63, 169– 76. CrossrefWeb of Science®Google Scholar Gorsuch, R. L., (1983). Factor Analysis, 2nd edn. Hillsdale, NJ: Lawrence Erlbaum Associates. Web of Science®Google Scholar Hanson, S. J. & Burr, D. J., (1990). What connectionist models learn: Learning and representation inconnectionist networks. Behavioral and Brain Sciences, 13, 471– 518. CrossrefWeb of Science®Google Scholar Hartman, E., Keeler, J. D. & Kowalski, J. M., (1989). Layered neural networks with Gaussian hidden unitsas universal approximation. Neural Computation, 2, 210– 15. CrossrefGoogle Scholar Hawkins, R. D. B. & Bower, G H., (1989). Computational models of learning in simple neural systems. San Diego: Academic Press. CrossrefGoogle Scholar Hebb, D. O., (1949). The Organization Of Behavior. New York: John Wiley & Sons. Google Scholar Hertz, J., Krogh, A. & Palmer, R. G., (1991). Introduction to the Theory of Neural Computation. Redwood City, CA: Addison-Wesley CrossrefGoogle Scholar Hillis, W D., (1988). Intelligence as emergent behavior, or, the songs of Eden. In S. R. Graubard (ed.), The Artificial Intelligence Debate (pp. 175– 89). Cambridge, MA: MIT Press. Web of Science®Google Scholar Hinton, G E., (1986). Learning distributed representations of concepts. Paper presented at the 8th Annual Meeting of the Cognitive Science Society, Ann Arbor, MI. Google Scholar Hinton, G. E. & Anderson, J. A., (1981). Parallel Models Of Associative Memory. Hillsdale, NJ: Lawrence Erlbaum Associates. Google Scholar Honda, M., Barrett, G, Yoshimura, N, Sadato, N, Yonekura, Y & Shibasaki, H., (1998). Comparative study of event-related potentials and positron emission tomography activation during a paired-associatememory paradigm. Experimental Brain Research, 119 (1), 103– 15. CrossrefCASPubMedWeb of Science®Google Scholar Hornik, M., Stinchcombe, M. & White, H., (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359– 66. CrossrefWeb of Science®Google Scholar Hunt, R. G., (1959). Meaningfulness and articulation of stimulus and response in paired-associate learningand stimulus recall. Journal of Experimental Psychology, 57 (4), 262– 7. CrossrefCASPubMedWeb of Science®Google Scholar James, W., (1890). The Principles Of Psychology, Volume One. New York, NY: Dover Publications. Google Scholar Jerne, N. K., (1967). Antibodies and learning: Selection versus instruction. In G. C. Quarton, T. Melnechuk & F. O. Schmitt (eds), The Neurosciences: A study program (pp. 200– 208). New York: Rockefeller University Press. Google Scholar Jevons, W. S., (1971). Pure logic, and other minor works. B. Franklin. Google Scholar Kahana, M. J., (2002). Associative symmetry and memory theory. Memory & Cognition, 30 (6), 823– 40. CrossrefPubMedWeb of Science®Google Scholar Kasabov, N. K., (1996). Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. Cambridge, MA: MIT Press. Google Scholar Kehoe, E. J., (1988). A layered network model of associative learning: Learning to learn and configuration. Psychological Review, 95, 411– 33. CrossrefCASPubMedWeb of Science®Google Scholar Kintsch, W., (1970). Learning, Memory, and Conceptual Processes. New York, NY: John Wiley & Sons. Google Scholar Kruschke, J. K., (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22– 44. CrossrefCASPubMedWeb of Science®Google Scholar Kruschke, J. K., (1993). Human category learning: Implications for backpropagation models Connection Science, 5, 3– 36. CrossrefGoogle Scholar Leighton, J. P. & Dawson, M. R. W., (2001). A parallel distributed processing model of Wason's selectiontask. Cognitive Systems Research, 2, 207– 31. CrossrefGoogle Scholar Lettvin, J. Y, Maturana, H. R., McCulloch, W. S. & Pitts, W. H., (1959). What the frog's eye tells the frog'sbrain. Proceedings of the IRE, 47 (11), 1940– 51. CrossrefWeb of Science®Google Scholar Levitan, I. B. & Kaczmarek, L. K., (1991). The Neuron: Cell and Molecular Biology. New York: Oxford University Press. Google Scholar Lewis, C. I. & Langford, C. H., (1959). Symbolic logic, 2nd edn. New York: The Century Co. Google Scholar Lippmann, R. P., (1987). An introduction to computing with neural nets. IEEE ASSP Magazine, April, 4–22. Google Scholar Lippmann, R. P., (1989). Pattern classification using neural networks. IEEE Communications Magazine, November, 47–64. Google Scholar Long, J. M., Mellem, J. E. & Kesner, R. P., (1998). The effects of parietal cortex lesions on an object spatiallocation paired-associate task in rats. Psychobiology, 26 (2), 128– 33. Web of Science®Google Scholar Martinez, J. L. & Derrick, B. E., (1996). Long-term potentiation and learning. Annual Review of Psychology, 47, 173– 203. CrossrefPubMedWeb of Science®Google Scholar McCaughan, D. B., Medler, D. A. & Dawson, M. R. W., (1999). Internal representation in networks of nonmonotonic processing units. Paper presented at the International Joint Conference on Neural Networks, Washington, DC. Google Scholar McClelland, J. L., (1986). Resource requirements of standard and programmable nets. In D. Rumelhart, J. McClelland and the PDP Group (eds), Parallel Distributed Processing (vol. 1, pp. 460– 87). Cambridge, MA: MIT Press. Google Scholar McClelland, J. L. & Rumelhart, D E., (1986). Parallel Distributed Processing, vol. 2. Cambridge, MA: MIT Press. Web of Science®Google Scholar McClelland, J. L. & Rumelhart, D E., (1988). Explorations In Parallel Distributed Processing. Cambridge, MA: MIT Press. Google Scholar McCloskey, M., (1991). Networks and theories: The place of connectionism in cognitive science. Psychological Science, 2, 387– 95. CrossrefWeb of Science®Google Scholar McCulloch, W S., (1988). Agathy tyche of nervous nets - the lucky reckoners. In W. S. McCulloch (ed.), Embodiments of Mind. Cambridge, MA: MIT Press. Google Scholar McCulloch, W S. & Pitts, W H., (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115– 33. CrossrefGoogle Scholar McCulloch, W S. & Pitts, W H., (1988). A logical calculus of the ideas immanent in nervous activity. In W. S. McCulloch (ed.), Embodiments Of Mind (pp. 19– 39). Cambridge, MA: MIT Press. Web of Science®Google Scholar Medler, D. A., (1998). A brief history of connectionism. Neural Computing Surveys, 1, 18– 72. Google Scholar Medler, D. A., McCaughan, D. B., Dawson, M. R. W. & Willson, L., (1999). When local isn't enough: Extracting distributed rules from networks. Paper presented at the International Joint Conference On Neural Networks, Washington, D.C. Google Scholar Mendelson, E., (1970). Schaum's outline of theory and problems of Boolean algebra and switching circuits. New York: McGraw-Hill. Google Scholar Minsky, M., (1963). Steps toward artificial intelligence. In E. A. Feigenbaum & J. Feldman (eds), Computers and Thought (pp. 406– 50). New York, NY: McGraw-Hill. Google Scholar Minsky, M. & Papert, S., (1988). Perceptrons, 3rd edn. Cambridge, MA: MIT Press. Google Scholar Moody, J., (1998). Forecasting the economy with neural nets: A survey of challenges and solutions. In G. Orr & K. Muller (eds), Neural Networks: tricks of the trade (pp. 347– 72). Berlin; New York: Springer. CrossrefWeb of Science®Google Scholar Moorhead, I. R., Haig, N. D. & Clement, R. A., (1989). An investigation of trained neural networks from a neurophysiological perspective. Perception, 18, 793– 803. CrossrefCASPubMedWeb of Science®Google Scholar Mozer, M. C. & Smolensky, P., (1989). Using relevance to reduce network size automatically Connection Science, 1, 3– 16. CrossrefGoogle Scholar Murdock, B. B., (1982). A theory for the storage and retrieval of item and associative information. Psychological Review, 89, 609– 26. CrossrefWeb of Science®Google Scholar Murdock, B. B., (1997). Context and mediators in a theory of distributed associative memory (TODAM2). Psychological Review, 104, 839– 62. CrossrefWeb of Science®Google Scholar Neuneier, R. & Zimmermann, H. G, (1998). How to train neural networks. In G. Orr & K. Muller (eds), Neural Networks: tricks of the trade (pp. 373– 423). Berlin; New York: Springer. CrossrefWeb of Science®Google Scholar O'Keefe, J. & Dostrovsky, J., (1971). The hippocampus as a spatial map: preliminary evidence from unitactivity in the freely moving rat. Brain Research, 34, 171– 5. CrossrefPubMedWeb of Science®Google Scholar O'Keefe, J. & Nadel, L., (1978). The Hippocampus As A Cognitive Map. Oxford: Clarendon Press. Google Scholar Omlin, C. W. & Giles, C. L., (1996). Extraction of rules from discrete-time recurrent neural networks. Neural Networks, 9, 41– 52. CrossrefWeb of Science®Google Scholar Orr, G. & Muller, K., (1998). Neural Networks: tricks of the trade. Berlin; New York: Springer. CrossrefGoogle Scholar Pao, Y.-H., (1989). Adaptive Pattern Recognition and Neural Networks. Reading, MA: Addison-Wesley. Google Scholar Papert, S., (1988). One Ai or Many? Daedalus, 117 (1), 1– 14. Web of Science®Google Scholar Pearce, J. M., (1997). Animal Learning and Cognition: An Introduction. East Sussex: Psychology Press. Google Scholar Pearce, J. M. & Bouton, M. E., (2001). Theories of associative learning in animals Annual Review of Psychology, 52, 111– 39. CrossrefCASPubMedWeb of Science®Google Scholar Pfeifer, R. & Scheier, C., (1999). Understanding Intelligence. Cambridge, MA: MIT Press. Google Scholar Piattelli-Palmarini, M., (1989). Evolution, selection and cognition: From “learning” to parameter settingin biology and in the study of language. Cognition, 31, 1– 44. CrossrefCASPubMedWeb of Science®Google Scholar Piercey, C. D. & Dawson, M. R. W, (1999). Coarse coding in value units: Subsymbolic implications of nonmonotonic processing units. In M. Hahn & S. C. Stoness (eds), Proceedings of the Twenty First Annual Conference of the Cognitive Science Society (pp. 537– 42). Malwah, NJ: Lawrence Erlbaum Associates. Google Scholar Pike, R., (1984). Comparison of convolution and matrix distributed memory systems for associativerecall and recognition. Psychological Review, 91, 281– 94. CrossrefWeb of Science®Google Scholar Pinker, S., (2002). The Blank Slate. New York, NY: Viking. Google Scholar Poggio, T. & Girosi, F., (1990). Regularization algorithms for learning that are equivalent to multilayernetworks. Science, 247, 978– 82. CrossrefCASPubMedWeb of Science®Google Scholar Poggio, T, Torre, V & Koch, C., (1985). Computational vision and regularization theory. Nature, 317, 314– 19. CrossrefPubMedWeb of Science®Google Scholar Poldrack, R. A., Clark, J., Pare-Blagoev, E. J., Shohamy, D., Moyano, J. C, Myers, C & Gluck, M. A., (2001). Interactive memory systems in the human brain. Nature, 414 (6863), 546– 50. CrossrefCASPubMedWeb of Science®Google Scholar Pylyshyn, Z. W, (1991). The role of cognitive architectures in theories of cognition. In K. Van Lehn (ed.), Architectures For Intelligence (pp. 189– 223). Hillsdale, NJ: Lawrence Erlbaum Associates. Google Scholar Renals, S., (1989). Radial basis function network for speech pattern classification. Electronics Letters, 25, 437– 9. CrossrefWeb of Science®Google Scholar Ripley, B. D., (1996). Pattern Recognition and Neural Networks. Cambridge, UK: Cambridge University Press. CrossrefGoogle Scholar Rizzuto, D. S. & Kahana, M. J., (2001). An autoassociative neural network model of paired-associatelearning. Neural Computation, 13 (9), 2075– 92. CrossrefCASPubMedWeb of Science®Google Scholar Rojas, R., (1996). Neural Networks: A Systematic Exploration. Berlin: Springer. CrossrefGoogle Scholar Rosenblatt, F., (1962). Principles of Neurodynamics. Washington: Spartan Books. Google Scholar Rumelhart, D. E., Hinton, G. E. & Williams, R. J., (1986a). Learning internal representations by back-propagating errors. Nature, 323, 533– 6. CrossrefWeb of Science®Google Scholar Rumelhart, D. E., Hinton, G. E. & Williams, R. J., (1986b). Learning internal representations by error backpropagation. In D. Rumelhart, J. McClelland and the PDP Group (eds), Parallel Distributed Processing (vol. 1. pp. 318– 62) Cambridge, MA: MIT Press. Google Scholar Rumelhart, D. E. & McClelland, J. L., (1986). Parallel Distributed Processing, vol. 1. Cambridge, MA: MITPress. CrossrefGoogle Scholar Seidenberg, M., (1993). Connectionist models and cognitive theory. Psychological Science, 4, 228– 35. CrossrefWeb of Science®Google Scholar Seidenberg, M. & McClelland, J., (1989). A distributed, developmental model of word recognition andnaming. Psychological Review, 97, 447– 52. CrossrefWeb of Science®Google Scholar Selfridge, O. G, (1956). Pattern recognition and learning. In C. Cherry (ed.), Information Theory (pp. 345– 53). London: Butterworth Scientific Publications. Google Scholar Shanks, D. R., (1995). The Psychology of Associative Learning. Cambridge, UK: Cambridge University Press. CrossrefGoogle Scholar Shannon, C. E., (1938). A symbolic analysis of relay and switching circuits Transactions of the American Institute of Electrical Engineers, 57, 713– 23. CrossrefGoogle Scholar Shannon, C. E., (1949). The synthesis of two-terminal switching circuits. Bell System Technical Journal, 28, 59– 98. Wiley Online LibraryWeb of Science®Google Scholar Shepherd, A. J., (1997). Second-Order Methods For Neural Networks. London: Springer. CrossrefGoogle Scholar Smolensky, P., (1988). On the proper treatment of connectionism. Behavioral and Brain Sciences, 11, 1– 74. CrossrefWeb of Science®Google Scholar Sporns, O. & Tononi, G., (1994). Selectionism and the Brain. San Diego, CA: Academic Press. CrossrefGoogle Scholar Steinbuch, K, (1961). Die lernmatrix. Kybernetik, 1, 36– 45. CrossrefWeb of Science®Google Scholar Sutton, R. S. & Barto, A. G., (1981). Toward a modern theory of adaptive networks: Expectation and prediction. Psychological Review, 88 (2), 135– 70. CrossrefCASPubMedWeb of Science®Google Scholar Sutton, R. S. & Barto, A. G., (1998). Reinforcement Learning. Cambridge, MA: MIT Press. Web of Science®Google Scholar Taylor, W K, (1956). Electrical simulation of some nervous system functional activities. In C. Cherry (ed.), Information Theory (pp. 314– 28). London: Butterworth Scientific Publications. Google Scholar Tesauro, G. & Janssens, B., (1988). Scaling relationships in backpropagation learning. Complex Systems, 2, 39– 44. Google Scholar Thrun, S. B., Bala, J., Bloedorn, E., Bratko, I., Cestnik, B., Cheng, J., De Jong, K., Dzeroski, S., Fahlman, S. E., Fisher, D., Hamann, R., Kaufman, K, Keller, S., Kononenko, I., Kreuziger, J., Michalski, R. S., Mitchell, T, Pachowicz, P., Reich, Y., Vafaie, H., van de Welde, W., Wenzel, W, Wnek, J. & Zhang, J., (1991). The MONK's Problems - A Performance Comparison of Different Learning Algorithms (Technical Report CS-CMU-91–197). Pittsburgh: Carnegie Mellon University. Google Scholar Underwood, B. J., (1957). Interference and forgetting. Psychological Review, 64 (1), 49– 60. CrossrefCASPubMedWeb of Science®Google Scholar Underwood, B. J., (1966). Experimental Psychology, 2nd edn. New York, NY: Appleton-Century-Crofts. Google Scholar Underwood, B. J., Runquist, R. N. & Schulz, R. W, (1959). Response learning in paired-associate lists as a function of intralist similarity. Journal of Experimental Psychology, 58 (1), 70– 78. CrossrefCASPubMedWeb of Science®Google Scholar Widrow, B. & Hoff, M. E., (1960). Adaptive switching circuits. Institute of Radio Engineers, Wester Electronic Show and Convention, Convention Record, Part, 4, 96– 104. Google Scholar Wimer, C. C. & Lambert, W. E., (1959). The differential effects of word and object stimuli on the learning of paired associates. Journal of Experimental Psychology, 57 (1), 31– 6. CrossrefCASPubMedWeb of Science®Google Scholar Zurada, J. M., (1992). Introduction To Artificial Neural Systems. St. Paul, MN: West. Google Scholar Connectionism: A Hands-On Approach ReferencesRelatedInformation" @default.
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• W4229750790 title "References" @default.
• W4229750790 cites W1025501654 @default.
• W4229750790 cites W122552260 @default.
• W4229750790 cites W1498436455 @default.
• W4229750790 cites W1520248950 @default.
• W4229750790 cites W1563677324 @default.
• W4229750790 cites W1583309920 @default.
• W4229750790 cites W162548580 @default.
• W4229750790 cites W1971392964 @default.
• W4229750790 cites W1971735090 @default.
• W4229750790 cites W1973578705 @default.
• W4229750790 cites W1985811648 @default.
• W4229750790 cites W1994487861 @default.
• W4229750790 cites W1994863141 @default.
• W4229750790 cites W1995341919 @default.
• W4229750790 cites W1996692737 @default.
• W4229750790 cites W1997457575 @default.
• W4229750790 cites W2006971810 @default.
• W4229750790 cites W2007700211 @default.
• W4229750790 cites W2008645179 @default.
• W4229750790 cites W2012066980 @default.
• W4229750790 cites W2013239224 @default.
• W4229750790 cites W2013825190 @default.
• W4229750790 cites W2014702547 @default.
• W4229750790 cites W2018124860 @default.
• W4229750790 cites W2019417891 @default.
• W4229750790 cites W2026628830 @default.
• W4229750790 cites W2038306533 @default.
• W4229750790 cites W2040598998 @default.
• W4229750790 cites W2046827412 @default.
• W4229750790 cites W2048325563 @default.
• W4229750790 cites W2048822829 @default.
• W4229750790 cites W2048915379 @default.
• W4229750790 cites W2052515926 @default.
• W4229750790 cites W2053619330 @default.
• W4229750790 cites W2053987251 @default.
• W4229750790 cites W2055617311 @default.
• W4229750790 cites W2056566670 @default.
• W4229750790 cites W2058709617 @default.
• W4229750790 cites W2058877549 @default.
• W4229750790 cites W2065767723 @default.
• W4229750790 cites W2068422506 @default.
• W4229750790 cites W2069632779 @default.
• W4229750790 cites W2070568497 @default.
• W4229750790 cites W2072773743 @default.
• W4229750790 cites W2076414357 @default.
• W4229750790 cites W2076852153 @default.
• W4229750790 cites W2086472796 @default.
• W4229750790 cites W2088876640 @default.
• W4229750790 cites W2102457056 @default.
• W4229750790 cites W2103496339 @default.
• W4229750790 cites W2103752162 @default.
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• W4229750790 cites W2106625755 @default.
• W4229750790 cites W2114928916 @default.
• W4229750790 cites W2117812871 @default.
• W4229750790 cites W2118017998 @default.
• W4229750790 cites W2118373646 @default.
• W4229750790 cites W2122988375 @default.
• W4229750790 cites W2131904148 @default.
• W4229750790 cites W2133036738 @default.
• W4229750790 cites W2136952238 @default.
• W4229750790 cites W2137983211 @default.
• W4229750790 cites W2138733328 @default.
• W4229750790 cites W2152619728 @default.
• W4229750790 cites W2153297147 @default.
• W4229750790 cites W2161202473 @default.
• W4229750790 cites W2180304924 @default.
• W4229750790 cites W2320278928 @default.
• W4229750790 cites W2486886792 @default.
• W4229750790 cites W2498072278 @default.
• W4229750790 cites W329543953 @default.
• W4229750790 cites W4214870031 @default.
• W4229750790 cites W4214871172 @default.
• W4229750790 cites W4236355931 @default.
• W4229750790 cites W4238384232 @default.
• W4229750790 cites W4240768087 @default.
• W4229750790 cites W4240778903 @default.
• W4229750790 cites W4241395986 @default.
• W4229750790 cites W4244023744 @default.
• W4229750790 cites W4246599888 @default.
• W4229750790 cites W4299101042 @default.
• W4229750790 cites W4300402905 @default.
• W4229750790 cites W4312750805 @default.
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