References
Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985). A learning algorithm for Boltzmann machines. Cognitive Science, 9, 147–169.
Anderson, J. A. (1977). Neural models with cognitive implications. In D. LaBerge & S. J. Samuels (Eds.), Basic processes in reading perception and comprehension (pp. 27–90). Hillsdale, NJ: Lawrence Erlbaum.
Anderson, J., & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Lawrence Erlbaum Associates.
Bates, E., & MacWhinney, B. (1989). Functionalism and the competition model. In B. MacWhinney & E. Bates (Eds.), The crosslinguistic study of language processing (pp. 3–37). New York: Cambridge University Press.
Bechtel, W., & Abrahamsen, A. (1991). Connectionism and the mind. Oxford, UK: Blackwell.
Berko, J. (1958). The child’s learning of English morphology. Word, 14, 150–177.
Bishop, D. V. M. (1997). Cognitive neuropsychology and developmental disorders: Uncomfortable bedfellows. Quarterly Journal of Experimental Psychology, 50A, 899–923.
Bishop, D. V. M. (2006). Developmental cognitive genetics: How psychology can inform genetics and vice versa. Quarterly Journal of Experimental Psychology, 59(7), 1153–1168.
Botvinick, M., & Plaut, D. C. (2004). Doing without schema hierarchies: A recurrent connectionist approach to normal and impaired routine sequential action. Psychological Review, 111, 395–429.
Burton, A. M., Bruce, V., & Johnston, R. A. (1990). Understanding face recognition with an interactive activation model. British Journal of Psychology, 81, 361–380.
Bybee, J., & McClelland, J. L. (2005). Alternatives to the combinatorial paradigm of linguistic theory based on domain general principles of human cognition. The Linguistic Review, 22(2–4), 381–410.
Carey, S., & Sarnecka, B. W. (2006). The development of human conceptual representations: A case study. In Y. Munakata & M. H. Johnson (Eds.), Processes of change in brain and cognitive development: Attention and performance XXI, (pp. 473–496). Oxford, UK: Oxford University Press.
Carpenter, G. A., & Grossberg, S. (1987a). ART2: Self-organization of stable category recognition codes for analog input patterns. Applied Optics, 26, 4919–4930.
Carpenter, G. A., & Grossberg, S. (1987b). A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer Vision, Graphics and Image Processing, 37, 54–115.
Chater, N., Tenenbaum, J. B., & Yuille, A. (2006). Probabilistic models of cognition: Conceptual foundations. Trends in Cognitive Sciences 10(7), 287–291.
Christiansen, M. H., & Chater, N. (2001). Connectionist psycholinguistics. Westport, CT: Ablex.
Cohen, G., Johnstone, R. A., & Plunkett, K. (2000). Exploring cognition: damaged brains and neural networks. Hove, Sussex, UK: Psychology Press.
Cohen, J. D., Dunbar, K., & McClelland, J. L. (1990). On the control of automatic processes: A parallel distributed processing account of the Stroop effect. Psychological Review, 97, 332–361.
Cohen, J. D., & Servan-Schreiber, D. (1992). Context, cortex, and dopamine: A connectionist approach to behavior and biology in schizophrenia. Psychological Review, 99, 45–77.
Dailey, M. N., & Cottrell, G. W. (1999). Organization of face and object recognition in modular neural networks. Neural Networks, 12, 1053–1074.
Davelaar, E. J., & Usher, M. (2003). An activation-based theory of immediate item memory. In J. A. Bullinaria, & W. Lowe (Eds.), Proceedings of the Seventh Neural Computation and Psychology Workshop: Connectionist models of cognition and perception (pp. 118–130). Singapore: World Scientific.
Davies, M. (2005). Cognitive science. In F. Jackson & M. Smith (Eds.), The Oxford handbook of contemporary philosophy (pp. 358–394). Oxford, UK: Oxford University Press.
Devlin, J., Gonnerman, L., Andersen, E., & Seidenberg, M. S. (1997). Category specific semantic deficits in focal and widespread brain damage: A computational account. Journal of Cognitive Neuroscience, 10, 77–94.
Dick, F., Bates, E., Wulfeck, B., Aydelott, J., Dronkers, N., & Gernsbacher, M. A. (2001). Language deficits, localization, and grammar: Evidence for a distributive model of language breakdown in aphasic patients and neurologically intact individuals. Psychological Review, 108(3), 759–788.
Dick, F., Wulfeck, B., Krupa-Kwiatkowski, M., & Bates, E. (2004). The development of complex sentence interpretation in typically developing children compared with children with specific language impairments or early unilateral focal lesions. Developmental Science, 7(3), 360–377.
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179–211.
Elman, J. L. (1991). Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7, 195–224.
Elman, J. L. (1993). Learning and development in neural networks: The importance of starting small. Cognition, 48, 71–99.
Elman, J. L. (2005). Connectionist models of cognitive development: Where next? Trends in Cognitive Sciences, 9, 111–117.
Elman, J. L., Bates, E. A., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., & Plunkett, K. (1996). Rethinking innateness: A connectionist perspective on development. Cambridge, MA: MIT Press.
Ervin, S. M. (1964). Imitation and structural change in children’s language. In E. H. Lenneberg (Ed.), New directions in the study of language (pp. 163–189). Cambridge, MA: MIT Press.
Fahlman, S., & Lebiere, C. (1990). The cascade correlation learning architecture. In D. Touretzky (Ed.), Advances in neural information processing 2 (pp. 524–532). Los Altos, CA: Morgan Kauffman.
Feldman, J. A. (1981). A connectionist model of visual memory. In G. E. Hinton & J. A. Anderson (Eds.), Parallel models of associative memory (pp. 49–81). Hillsdale, NJ: Hawrence Erlbaum.
Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 78, 3–71.
French, R. M., Ans, B., & Rousset, S. (2001). Pseudopatterns and dual-network memory models: Advantages and shortcomings. In R. French & J. Sougné (Eds.), Connectionist models of learning, development and evolution (pp. 13–22). London: Springer.
Freud, S. (1895). Project for a scientific psychology. In J. Strachey (Ed.), The standard edition of the complete psychological works of Sigmund Freud (pp. 283–360). London: The Hogarth Press and the Institute of Psycho-Analysis.
Goebel, R., & Indefrey, P. (2000). A recurrent network with short-term memory capacity learning the German –s plural. In P. Broeder & J. Murre (Eds.), Models of language acquisition: Inductive and deductive approaches (pp. 177–200). Oxford, UK: Oxford University Press.
Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Kushnir, T., & Danks, D. (2004). A theory of causal learning in children: Causal maps and bayes nets. Psychological Review, 111(1): 3–32.
Green, D. C. (1998). Are connectionist models theories of cognition? Psycoloquy, 9(4).
Grossberg, S. (1976). Adaptive pattern classification and universal recoding: Parallel development and coding of neural feature detectors. Biological Cybernetics, 23, 121–134.
Haarmann, H., & Usher, M. (2001). Maintenance of semantic information in capacity limited item short-term memory. Psychonomic Bulletin & Review, 8, 568–578.
Hebb, D. O. (1949). The organization of behavior: A neuropsychological approach. New York: John Wiley & Sons.
Hinton, G. E. (1989). Deterministic Boltzmann learning performs steepest descent in weight-space. Neural Computation, 1, 143–150.
Hinton, G. E., & Anderson, J. A. (1981). Parallel models of associative memory. Hillsdale, NJ: Lawrence Erlbaum.
Hinton, G. E., & McClelland, J. L. (1988). Learning representations by recirculation. In D. Z. Anderson (Ed.), Neural Information Processing Systems, 1987 (pp. 358–366). New York: American Institute of Physics.
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.
Hinton, G. E., & Sejnowski, T. J. (1983). Optimal perceptual inference. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 448–453). Washington, DC.
Hinton, G. E., & Sejnowksi, T. (1986). Learning and relearning in Boltzmann machines. In D. Rumelhart & J. McClelland (Eds.), Parallel distributed processing, Vol. 1 (pp. 282–317). Cambridge, MA: MIT Press.
Hoeffner, J. H., & McClelland, J. L. (1993). Can a perceptual processing deficit explain the impairment of inflectional morphology in developmental dysphasia? A computational investigation. In E. V. Clark (Ed.), Proceedings of the 25th Child language research forum (pp. 38–49). Stanford, CA: Center for the Study of Language and Information.
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Science USA, 79, 2554–2558.
Houghton, G. (2005). Connectionist models in cognitive psychology. Hove, Sussex, UK: Psychology Press.
Jacobs, R. A. (1999). Computational studies of the development of functionally specialized neural modules. Trends in Cognitive Sciences, 3, 31–38.
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991). Adaptive mixtures of local experts. Neural Computation, 3, 79–87.
James, W. (1890). Principles of psychology. New York: Holt.
Joanisse, M. F., & Seidenberg, M. S. (1999). Impairments in verb morphology following brain injury: A connectionist model. Proceedings of the National Academy of Science USA, 96, 7592–7597.
Joanisse, M. F., & Seidenberg, M. S. (2003). Phonology and syntax in specific language impairment: Evidence from a connectionist model. Brain and Language, 86, 40–56.
Jordan, M. I. (1986). Attractor dynamics and parallelism in a connectionist sequential machine. In Proceedings of the Eight Annual Conference of Cognitive Science Society (pp. 531–546). Hillsdale, NJ: Lawrence Erlbaum.
Juola, P., & Plunkett, K. (2000). Why double dissociations don’t mean much. In G. Cohen, R. A. Johnston, & K. Plunkett (Eds.), Exploring cognition: Damaged brains and neural networks: Readings in cognitive neuropsychology and connectionist modelling (pp. 319–327). Hove, Sussex, UK: Psychology Press.
Karmiloff-Smith, A. (1998). Development itself is the key to understanding developmental disorders. Trends in Cognitive Sciences, 2, 389–398.
Kohonen, T. (1984). Self-organization and associative memory. Berlin: Springer-Verlag.
Kuczaj, S. A. (1977). The acquisition of regular and irregular past tense forms. Journal of Verbal Learning and Verbal Behavior, 16, 589–600.
Lashley, K. S. (1929). Brain mechanisms and intelligence: A quantitative study of injuries to the brain. New York: Dover.
Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: A network model of category learning. Psychological Review, 111, 309–332.
MacDonald, M. C., & Christiansen, M. H. (2002). Reassessing working memory: A comment on Just & Carpenter (1992) and Waters & Caplan (1996). Psychological Review, 109, 35–54.
MacKay, D. J. (1992). A practical Bayesian framework for backpropagation networks. Neural Computation, 4, 448–472.
Marcus, G. F. (2001). The algebraic mind: Integrating connectionism and cognitive science. Cambridge, MA: MIT Press
Marcus, G., Pinker, S., Ullman, M., Hollander, J., Rosen, T. & Xu, F. (1992). Overregularisation in language acquisition. Monographs of the Society for Research in Child Development, 57 (Serial No. 228).
Mareschal, D., Johnson, M., Sirios, S., Spratling, M., Thomas, M. S. C., & Westermann, G. (2007). Neuroconstructivism: How the brain constructs cognition. Oxford, UK: Oxford University Press.
Mareschal, D., & Shultz, T. R. (1996). Generative connectionist architectures and constructivist cognitive development. Cognitive Development, 11, 571–605.
Mareschal, D., & Thomas, M. S. C. (2007). Computational modeling in developmental psychology. IEEE Transactions on Evolutionary Computation, 11(2), 137–150.
Marr, D. (1982). Vision. San Francisco: W. H. Freeman.
Marr, D., & Poggio, T. (1976). Cooperative computation of stereo disparity. Science, 194, 283–287.
Mayberry, M. R., Crocker, M., & Knoeferle, P. (2005). A Connectionist Model of Sentence Comprehension in Visual Worlds. In: Proceedings of the 27th Annual Conference of the Cognitive Science Society, (COGSCI-05, Streas, Italy), Mahwah, NJ: Erlbaum.
McClelland, J. L. (1981). Retrieving general and specific information from stored knowledge of specifics. In Proceedings of the Third Annual Meeting of the Cognitive Science Society (pp. 170–172). Hillsdale, NJ: Lawrence Erlbaum Associates.
McClelland, J. L. (1989). Parallel distributed processing: Implications for cognition and development. In M. G. M. Morris (Ed.), Parallel distributed processing, implications for psychology and neurobiology (pp. 8–45). Oxford, UK: Clarendon Press.
McClelland, J. L. (1998). Connectionist models and Bayesian inference. In M. Oaksford & N. Chater (Eds.), Rational models of cognition (pp. 21–53). Oxford, UK: Oxford University Press.
McClelland, J. L., & Elman, J. L. (1986). The Trace model of speech perception. Cognitive Psychology, 18, 1–86.
McClelland, J. L., McNaughton, B. L., & O’Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102, 419–457.
McClelland, J. L., Plaut, D. C., Gotts, S. J., & Maia, T. V. (2003). Developing a domain-general framework for cognition: What is the best approach? Commentary on a target article by Anderson and Lebiere. Behavioral and Brain Sciences, 22, 611–614.
McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: Part 1. An account of basic findings. Psychological Review, 88(5), 375–405.
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 (pp. 2–4). Cambridge, MA: MIT Press.
McClelland, J. L., Thomas, A. G., McCandliss, B. D., & Fiez, J. A. (1999). Understanding failures of learning: Hebbian learning, competition for representation space, and some preliminary data. In J. A. Reggia, E. Ruppin, & D. Glanzman (Eds.), Disorders of brain, behavior, and cognition: The neurocomputational perspective (pp. 75–80). Oxford, UK: Elsevier.
McClelland, J. L., & Thompson, R. M. (2007). Using domain-general principles to explain children’s causal reasoning abilities. Developmental Science, 10, 333–356.
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133. (Reprinted in Anderson J., & Rosenfield E. (1988). Neurocomputing: Foundations of research) Cambridge, MA: MIT Press.
McLeod, P., Plunkett, K., & Rolls, E. T. (1998). Introduction to connectionist modelling of cognitive processes. Oxford, UK: Oxford University Press
Meynert, T. (1884). Psychiatry: A clinical treatise on diseases of the forebrain. Part I. The anatomy, physiology and chemistry of the brain (B. Sachs, Trans.). New York: G.P. Putnam’s Sons.
Miikkulainen, R., & Mayberry, M. R. (1999). Disambiguation and grammar as emergent soft constraints. In B. MacWhinney (Ed.), Emergence of language (pp. 153–176). Hillsdale, NJ: Lawrence Erlbaum.
Minsky, M., & Papert, S. (1969). Perceptrons: An introduction to computational geometry. Cambridge, MA: MIT Press.
Minsky, M. L., & Papert, S. (1988). Perceptrons: An introduction to computational geomety. Cambridge, MA: MIT Press.
Morris, W., Cottrell, G., & Elman, J. (2000). A connectionist simulation of the empirical acquisition of grammatical relations. In S. Wermter & R. Sun (Eds.), Hybrid neural systems (pp. 175–193). Heidelberg: Springer Verlag.
Morton, J. (1969). Interaction of information in word recognition. Psychological Review, 76, 165–178.
Morton, J. B., & Munakata, Y. (2002). Active versus latent representations: A neural network model of perseveration, dissociation, and decalage in childhood. Developmental Psychobiology, 40, 255–265.
Movellan, J. R., & McClelland, J. L. (1993). Learning continuous probability distributions with symmetric diffusion networks. Cognitive Science, 17, 463–496.
Munakata, Y. (1998). Infant perseveration and implications for object permanence theories: A PDP model of the AB task. Developmental Science, 1, 161–184.
Munakata, Y., & McClelland, J. L. (2003). Connectionist models of development. Developmental Science, 6, 413–429.
O’Reilly, R. C. (1996). Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm. Neural Compuation, 8(5), 895–938.
O’Reilly, R. C. (1998). Six principles for biologically-based computational models of cortical cognition. Trends in Cognitive Sciences, 2, 455–462.
O’Reilly, R. C., Braver, T. S., & Cohen, J. D. (1999). A biologically based computational model of working memory. In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 375–411). New York: Cambridge University Press.
O’Reilly, R. C., & Munakata, Y. (2000). Computational explorations in cognitive neuroscience: Understanding the mind by simulating the brain. Cambridge, MA: MIT Press.
Pinker, S. (1984). Language learnability and language development. Cambridge, MA: Harvard University Press.
Pinker, S. (1999). Words and rules. London: Weidenfeld & Nicolson.
Pinker, S., & Prince, A. (1988). On language and connectionism: Analysis of a parallel distributed processing model of language acquisition. Cognition, 28, 73–193.
Plaut, D. C., & Kello, C. T. (1999). The emergence of phonology from the interplay of speech comprehension and production: A distributed connectionist approach. In B. MacWhinney (Ed.), The emergence of language (pp. 381–415). Mahwah, NJ: Lawrence Erlbaum.
Plaut, D., & McClelland, J. L. (1993). Generalization with componential attractors: Word and nonword reading in an attractor network. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society (pp. 824–829). Hillsdale, NJ: Lawrence Erlbaum.
Plaut, D. C., McClelland, J. L., Seidenberg, M.S., & Patterson, K. E. (1996). Understanding normal and impaired word reading: Computational principles in quasi-regular domains. Psychological Review, 103, 56–115.
Plaut, D. C., & Shallice, T. (1993). Deep dyslexia: A case study of connectionist neuropsychology. Cognitive Neuropsychology, 10, 377–500.
Plomin, R., Owen, M. J., & McGuffin, P. (1994). The genetic basis of complex human behaviors. Science, 264, 1733–1739.
Plunkett, K., & Bandelow, S. (2006). Stochastic approaches to understanding dissociations in inflectional morphology. Brain and Language, 98, 194–209.
Plunkett, K., & Marchman, V. (1991). U-shaped learning and frequency effects in a multilayered perceptron: Implications for child language acquisition. Cognition, 38, 1–60.
Plunkett, K., & Marchman, V. (1993). From rote learning to system building: acquiring verb morphology in children and connectionist nets. Cognition, 48, 21–69.
Plunkett, K., & Marchman, V. (1996). Learning from a connectionist model of the English past tense. Cognition, 61, 299–308.
Plunkett, K., & Nakisa, R. (1997). A connectionist model of the Arabic plural system. Language and Cognitive Processes, 12, 807–836.
Quartz, S. R. (1993). Neural networks, nativism, and the plausibility of constructivism. Cognition, 48, 223–242.
Quartz, S. R. & Sejnowski, T. J. (1997). The neural basis of cognitive development: A constructivist manifesto. Behavioral and Brain Sciences, 20, 537–596.
Rashevsky, N. (1935). Outline of a physicomathematical theory of the brain. Journal of General Psychology, 13, 82–112.
Reicher, G. M. (1969). Perceptual recognition as a function of meaningfulness of stimulus material. Journal of Experimental Psychology, 81, 274–280.
Rohde, D. L. T. (2002). A connectionist model of sentence comprehension and production. Unpublished doctoral dissertation, Carnegie Mellon University, Pittsburgh, PA.
Rohde, D. L. T., & Plaut, D. C. (1999). Language acquisition in the absence of explicit negative evidence: How important is starting small? Cognition, 72, 67–109.
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386–408.
Rosenblatt, F. (1962). Principles of neurodynamics: Perceptrons and the theory of brain mechanisms. Washington, DC: Spartan Books.
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.
Rumelhart, D. E., Hinton, G. E., & McClelland, J. L. (1986). A general framework for parallel distributed processing. In D. E. Rumelhart, J. L. McClelland, & the PDP Research Group, Parallel distributed processing: Expolrations in the microstructure of congnition. Volume 1: Foundations (pp. 45–76). Cambridge, MA: MIT Press.
Rumelhart, D. E., & McClelland, J. L. (1985). Levels indeed! Journal of Experimental Psychology General, 114(2), 193–197.
Rumelhart, D. E., & McClelland, J. L. (1986). On learning the past tense of English verbs. In J. L. McClelland, D. E. Rumelhart & the PDP Research Group (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 2: Psychological and biological models (pp. 216–271). Cambridge, MA: MIT Press.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. In D. E. Rumelhart, J. L. McClelland and The PDP Research Group, Parallel distributed processing: Explorations in the microstructure of cognition. Volume 1: Foundations (pp. 318–362). Cambridge, MA: MIT Press.
Rumelhart, D. E., McClelland, J. L., & the PDP Research Group (1986). Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 1: Foundations (p. 2-4). Cambridge, MA: MIT Press.
Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. E. (1986). Schemata and sequential thought processes in PDP models. In J. L. McClelland & D. E. Rumelhart (Eds.), Parallel distributed processing, Vol. 2 (pp. 7–57). Cambridge, MA: MIT Press.
Saffran, J. R., Newport, E. L., & Aslin, R. N. (1996). Word segmentation: The role of distributional cues. Journal of Memory and Language, 35, 606–621.
Selfridge, O. G. (1959). Pandemonium: A paradigm for learning. In D. V. Blane, & A. M. Uttley (Eds.). Proceedings of the Symposium on Mechanisation of Thought Processes (pp. 511–529). London: HMSO.
Shallice, T. (1988). From neuropsychology to mental structure. Cambridge, UK: Cambridge University Press.
Sharkey, N., Sharkey, A., & Jackson, S. (2000). Are SRNs sufficient for modelling language acquisition. In P. Broeder & J. Murre (Eds.), Models of language acquisition: Inductive and deductive approaches (pp. 33–54). Oxford, UK: Oxford University Press.
Shultz, T. R. (2003). Computational developmental psychology. Cambridge, MA: MIT Press.
Smolensky, P. (1988). On the proper treatment of connectionism. Behavioral and Brain Sciences, 11, 1–74.
Spencer, H. (1872). Principles of psychology (3rd ed.). London: Longman, Brown, Green, & Longmans.
Sun, R. (1995). Robust reasoning: Integrating rule-based and similarity-based reasoning. Artificial Intelligence, 75, 241–295.
Sun, R. (2002a). Hybrid connectionist symbolic systems. In M. Arbib (Ed.), Handbook of brain theories and neural networks (2nd ed.), (pp. 543–547). Cambridge, MA: MIT Press.
Sun, R. (2002b). Hybrid systems and connectionist implementationalism. In L. Nadel, D. Chalmers, P. Culicover, R. Goldstone, & B. French (Eds.), Encyclopedia of Cognitive Science (pp. 697–703). London: Macmillan.
Sun, R., & Peterson, T. (1998). Autonomous learning of sequential tasks: Experiments and analyses. IEEE Transactions on Neural Networks, 9(6), 1217–1234.
Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences 10(7), 309–318.
Thomas, M. S. C. (2004). The state of connectionism in 2004. Parallaxis, 8, 43–61.
Thomas, M. S. C. (2005). Characterising compensation. Cortex, 41(3), 434–442.
Thomas, M. S. C., Forrester, N. A., & Richardson, F. M. (2006). What is modularity good for? In R. Sun & N. Miyake (Eds.). Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 2240–2245). Vancouver, Canada: Cognitive Science Society.
Thomas, M. S. C., & Johnson, M. H. (2006). The computational modelling of sensitive periods. Developmental Psychobiology, 48(4), 337–344.
Thomas, M. S. C., & Karmiloff-Smith, A. (2002a). Are developmental disorders like cases of adult brain damage? Implications from connectionist modelling. Behavioral and Brain Sciences, 25(6), 727–788.
Thomas, M. S. C., & Karmiloff-Smith, A. (2002b). Modelling typical and atypical cognitive development. In U. Goswami (Ed.), Handbook of childhood development (pp. 575–599). Oxford, UK: Blackwells.
Thomas, M. S. C., & Karmiloff-Smith, A. (2003a). Connectionist models of development, developmental disorders and individual differences. In R. J. Sternberg, J. Lautrey, & T. Lubart (Eds.), Models of intelligence: International perspectives (pp. 133–150). Washington, DC: American Psychological Association.
Thomas, M. S. C., & Karmiloff-Smith, A. (2003b). Modeling language acquisition in atypical phenotypes. Psychological Review, 110(4), 647–682.
Thomas, M. S. C., & Karmiloff-Smith, A. (2005). Can developmental disorders reveal the component parts of the human language faculty? Language Learning and Development, 1(1), 65–92.
Thomas, M. S. C., & Redington, M. (2004). Modelling atypical syntax processing. In W. Sakas (Ed.), Proceedings of the first workshop on psycho-computational models of human language acquisition at the 20th International Conference on Computational Linguistics (pp. 85–92).
Thomas, M. S. C., &
Richardson, F. (
2006).
Atypical representational change: Conditions for the emergence of atypical modularity. In
Y. Munakata &
M. H. Johnson (Eds.),
Processes of change in brain and cognitive development: Attention and Performance XXI, (pp. 315–347).
Oxford, UK:
Oxford University Press.
Thomas, M. S. C., & van Heuven, W. (2005). Computational models of bilingual comprehension. In J. F. Kroll & A. M. B. De Groot (Eds.), Handbook of bilingualism: Psycholinguistic approaches (pp. 202–225). Oxford, UK: Oxford University Press.
Touretzky, D. S., & Hinton, G. E. (1988). A distributed connectionist production system. Cognitive Science, 12, 423–466.
Usher, M., & McClelland, J. L. (2001). On the time course of perceptual choice: The leaky competing accumulator model. Psychological Review, 108, 550–592.
van Gelder, T. (1991). Classical questions, radical answers: Connectionism and the structure of mental representations. In T. Horgan & J. Tienson (Eds.), Connectionism and the philosophy of mind. (pp. 355–381). Dordrecht: Kluwer Academic.
Weckerly, J., & Elman, J. L. (1992). A PDP approach to processing center-embedded sentences. In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Lawrence Erlbaum.
Westermann, G. (1998). Emergent modularity and U-shaped learning in a constructivist neural network learning the English past tense. In Proceedings of the 20th Annual Conference of the Cognitive Science Society (pp. 1130–1135). Hillsdale, NJ: Lawrence Erlbaum.
Westermann, G., Mareschal, D., Johnson, M. H., Sirois, S., Spratling, M. W., & Thomas, M. S. C. (2007). Neuroconstructivism. Developmental Science, 10, 75–83.
Williams, R. J., & Zipser, D. (1995). Gradient-based learning algorithms for recurrent networks and their computational complexity. In Y. Chauvin & D. E. Rumelhart (Eds.), Back-propagation: Theory, architectures and applications. Hillsdale, NJ: Lawrence Erlbaum.
Xie, X., & Seung, H. S. (2003). Equivalence of backpropagation and contrastive Hebbian learning in a layered network. Neural Computation, 15, 441–454.
Xu, F., & Pinker, S. (1995). Weird past tense forms. Journal of Child Language, 22, 531–556.