Connectionist models of development

Developmental Science

Volumn 6, Issue 4, 2003, Pages 413-429

  Munakata, Yuko ^a   McClelland, James L ^b  

^a UNIVERSITY OF COLORADO   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0042363343     PISSN: 1363755X     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-7687.00296     Document Type: Article

References (78)

1. Aguiar, A., & Baillargeon, R. (2000). Perseveration and problem solving in infancy. In H. Reese (Ed.), Advances in child development and behavior, Vol. 27 (pp. 135-180). New York: Academic Press.
2. Andersen, J.R. (1990). The adaptive character of thought. Hillsdale, NJ: Lawrence Erlbaum Associates.
3. Anderson, J.R., & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Erlbaum.
4. Carmignoto, G., & Vicini, S. (1992). Activity-dependent decrease in NMDA receptor responses during development of the visual-cortex. Science, 258, 1007-1011.
5. Diamond, A. (1985). Development of the ability to use recall to guide action, as indicated by infants' performance on AB. Child Development, 56, 868-883.
6. Ellis, A.W., & Lambon-Ralph, M. (2000). Age of acquisition effects in adult lexical processing reflect loss of plasticity in maturing systems: insights from connectionist networks. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 1103-1123.
7. Elman, J.L. (1993). Learning and development in neural networks: the importance of starting small. Cognition, 48 (1), 71-99.
8. Harm, M.W., & Seidenberg, M.S. (1999). Phonology, reading acquisition, and dyslexia: insights from connectionist models. Psychological Review, 106 (3), 491-528.
9. 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 Annual Child Language Research Forum (pp. 38-49). Stanford, CA: Center for the Study of Language and Information.
10. Hofstadter, M.C., & Reznick, J.S. (1996). Response modality affects human infant delayed-response performance. Child Development, 67, 646-658.
11. 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.
12. Inhelder, B., & Piaget, J. (1958). The growth of logical thinking from childhood to adolescence. New York: Basic Books.
13. Just, M., & Carpenter, P. (1992). A capacity theory of comprehension: individual differences in working memory. Psychological Review, 99, 122-149.
14. Karmiloff-Smith, A. (1998). Development itself is the key to understanding developmental disorders. Trends in Cognitive Sciences, 2, 389-398.
15. Karmiloff-Smith, A., Scerif, G., & Thomas, M. (2002). Different approaches to relating genotype to phenotype in developmental disorders. Developmental Psychobiology, 40, 311-322.
16. Levitt, J.B., Lewis, D.A., Yoshioka, T., & Lund, J.S. (1993). Topography of pyramidal neuron intrinsic connections in macaque monkey prefrontal cortex (areas 9 & 46). Journal of Comparative Neurology, 338, 360-376.
17. McClelland, J.L. (1979). On the time relations of mental processes: an examination of systems of processes in cascade. Psychological Review, 86, 287-330.
18. McClelland, J.L. (1989). Parallel distributed processing: implications for cognition and development. In R.G.M. Morris (Ed.), Parallel distributed processing: Implications for psychology and neurobiology (Chap. 2, pp. 8-45). Oxford: Oxford University Press.
19. McClelland, IL. (1993). The GRAIN model: a framework for modeling the dynamics of information processing. In D.E. Meyer & S. Kornblum (Eds.), Attention and performance XIV: Synergies in experimental psychology, artifical intelligence, and cognitive neuroscience (pp. 655-688). Hillsdale, NJ: Lawrence Erlbaum Associates.
20. McClelland, J.L. (1995). A connectionist perspective on knowledge and development. In T.J. Simon & G.S. Halford (Eds.), Developing cognitive competence: New approaches to process modeling (pp. 157-204). Hillsdale, NJ: Erlbaum.
21. 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.
22. McClelland, J.L., & Rogers, T.T. (2003). The parallel distributed processing approach to semantic cognition. Nature Reviews Neuroscience, 4, 310-322.
23. McClelland, J.L., Rumelhart, D.E., & PDP Research Group (Eds.) (1986). Parallel distributed processing. Volume 2: Psychological and biological models. Cambridge, MA: MIT Press.
24. McClelland, J., Thomas, A., McCandliss, B., & Fiez, J. (1999). Understanding failures of learning: Hebbian learning, competition for representational space and some preliminary experimental data. In J. Reggia, E. Ruppin & D. Glanzman (Eds.), Progress in brain research, Vol. 121, Disorders of brain, behavior and cognition: The neurocomputational perspective (pp. 75-80). Amsterdam: Elsevier.
25. Mareschal, D., Plunkett, K., & Harris, P. (1999). A computational and neuropsychological account of object-oriented behaviours in infancy. Developmental Science, 2, 306-317.
26. Mareschal, D., & Shultz, T.R. (1996). Generative connectionist networks and constructivist cognitive development. Cognitive Development, 11, 571-603.
27. Morton, J.B., & Munakata, Y. (2002a). Active versus latent representations: a neural network model of perseveration and dissociation in early childhood. Developmental Psychobiology, 40, 255-265.
28. Morton, J.B., & Munakata, Y. (2002b). Are you listening? Exploring a knowledge-action dissociation in a speech interpretation task. Developmental Science, 5, 435-440.
29. Morton, J.B., Trehub, S.E., & Zelazo, P.D. (in preparation). Representational inflexibility in 6-year-olds' understanding of emotion in speech.
30. Mozer, M.C. (1987). Early parallel processing in reading: a connectionist approach. In M. Colthheart (Ed.), Attention and performance XII: The psychology of reading (pp. 83-104). Hillsdale, NJ: Lawrence Erlbaum Associates.
31. Munakata, Y. (1997). Perseverative reaching in infancy: the roles of hidden toys and motor history in the AB̄ task. Infant Behavior and Development, 20 (3), 405-416.
32. Munakata, Y. (1998). Infant perseveration and implications for object permanence theories: a PDP model of the AB task. Developmental Science, 1, 161-184.
33. Munakata, Y. (2001). Graded representations in behavioral dissociations. Trends in Cognitive Sciences, 5 (7), 309-315.
34. Munakata, Y., McClelland, J.L., Johnson, M.H., & Siegler, R. (1997). Rethinking infant knowledge: toward an adaptive process account of successes and failures in object permanence tasks. Psychological Review, 104, 686-713.
35. Munakata, Y., Morton, J.B., & Stedron, J.M. (in press). The role of prefrontal cortex in perseveration: developmental and computational explorations. In P. Quinlan (Ed.), Connectionist models of development. East Sussex: Psychology Press.
36. Newport, E. (1988). Constraints on learning and their role in language acquisition: studies of the acquisition of American sign language. Language Sciences, 10, 147-172.
37. Newport, E.L. (1990). Maturational constraints on language learning. Cognitive Science, 14, 11-28.
38. Oaksford, M., & Chater, N. (1994). A rational analysis of the selection task as optimal data selection. Psychological Review, 101, 608-631.
39. Oliver, A., Johnson, M.H., Karmiloff-Smith, A., & Pennington, B. (2000). Deviations in the emergence of representations: a neuroconstructivist framework for analysing developmental disorders. Developmental Science, 3, 1-23.
40. O'Reilly, R.C. (1996). Biologically plausible error-driven learning using local activation differences: the generalized recirculation algorithm. Neural Computation, 8 (5), 895-938.
41. O'Reilly, R.C., & Hoeffner, J.H. (submitted). Competition, priming, and the past tense U-shaped developmental curve.
42. O'Reilly, R.C., & Johnson, M.H. (1994). Object recognition and sensitive periods: a computational analysis of visual imprinting. Neural Computation, 6 (3), 357-389.
43. O'Reilly, R.C., & McClelland, J.L. (1992). The self-organization of spatially invariant representations (Parallel Distributed Processing and Cognitive Neuroscience PDP. CNS. 92. 5). Carnegie Mellon University, Department of Psychology.
44. O'Reilly, R.C., Mozer, M., Munakata, Y., & Miyake, A. (1999). Discrete representations in working memory: a hypothesis and computational investigations. The Second International Conference on Cognitive Science (pp. 183-188). Tokyo: Japanese Cognitive Science Society.
45. O'Reilly, R.C., & Munakata, Y. (2000). Computational explorations in cogntive neuroscience: Understanding the mind by simulating the brain. Cambridge, MA: MIT Press.
46. O'Reilly, R.C., & Rudy, J.W. (2001). Conjunctive representations in learning and memory: principles of cortical and hippocampal function. Psychological Review, 108, 311-345.
47. Piaget, J. (1954). The construction of reality in the child. New York: Basic Books.
48. Pinker, S., & Prince, A. (1988). On language and connectionism: analysis of a parallel distributed processing model of language acquisition. Cognition, 28, 73-193.
49. 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.
50. Plunkett, K., & Marchman, V.A. (1993). From rote learning to system building: acquiring verb morphology in children and connectionist nets. Cognition, 48 (1), 21-69.
51. Quartz, S.R., & Sejnowski, T.J. (1997). The neural basis of cognitive development: a constructivist manifesto. Behavioral and Brain Sciences, 20, 537-596.
52. Quinlan, E.M., Olstein, D.H., & Bear, M.F. (1999). Bidirectional, experience-dependent regulation of n-methyl-d-aspartate receptor subunit composition in the rat visual cortex during postnatal development. Neurobiology, 96, 12876-12880.
53. Raijmakers, M.E.J., van Koten, S., & Molenaar, P.C.M. (1996). On the validity of simulating stagewise development by means of pdp networks: application of catastrophe analysis and an experimental test of rule-like network performance. Cognitive Science, 20, 101-136.
54. Rao, S.G., Williams, G.V., & Goldman-Rakic, P.S. (1999). Iso-directional tuning of adjacent interneurons and pyramidal cells during working memory: evidence for microcolumnar organization in PFC. Journal of Neurophysiology, 81, 1903.
55. Reber, A.S. (1976). Implicit learning of synthetic languages: the role of instructional set. Journal of Experimental Psychology: Human Learning and Memory, 2, 88-94.
56. Rogers, T., & McClelland, J. (in press). Semantic cognition: A parallel distributed processing approach. Cambridge, MA: MIT Press.
57. Rohde, D., & Plaut, D. (1999). Language acquisition in the absence of explicit negative evidence: how important is starting small? Cognition, 72, 67-109.
58. Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Learning internal representations by error propagation. In D.E. Rumelhart, J.L. McClelland & PDP Research Group (Eds.), Parallel distributed processing. Volume 1: Foundations (Chap. 8, pp. 318-362). Cambridge, MA: MIT Press.
59. Rumelhart, D.E., & McClelland, J.L. (1986). On learning the past tenses of English verbs. In J.L. McClelland, D.E. Rumelhart & PDP Research Group (Eds.), Parallel distributed processing. Volume 2: Psychological and biological models (pp. 216-271). Cambridge, MA: MIT Press.
60. Rumelhart, D.E., McClelland, J.L., & PDP Research Group (Eds.) (1986). Parallel distributed processing. Volume 1: Foundations. Cambridge, MA: MIT Press.
61. Rumelhart, D.E., & Todd, P.M. (1993). Learning and connectionist representations. In D.E. Meyer & S. Kornblum (Eds.), Attention and performance XIV: Synergies in experimental psychology, artifical intelligence, and cognitive neuroscience (pp. 3-30). Cambridge, MA: MIT Press.
62. Saad, D. (1998). On-line learning in neural networks. New York: Cambridge University Press.
63. Servan-Schreiber, D., Cleeremans, A., & McClelland, J.L. (1991). Graded state machines: the representation of temporal contingencies in simple recurrent networks. Machine Learning, 1, 161-193.
64. Shrager, J., & Johnson, M.H. (1996). Dynamic plasticity influences the emergence of function in a simple cortical array. Neural Networks, 9, 1119.
65. Siegler, R. (1976). Three aspects of cognitive development, Cognitive Psychology, 8, 481-520.
66. Siegler, R., & Munakata, Y. (1993). Beyond the immaculate transition: advances in the understanding of change. SRCD Newsletter, Winter Issue.
67. Smith, L.B., & Thelen, E. (Eds.) (1993). A dynamic systems approach to development: Applications. Cambridge, MA: MIT Press.
68. Smith, L.B., Thelen, E., Titzer, B., & McLin, D. (1999). Knowing in the context of acting: the task dynamics of the A-not-B error. Psychological Review, 106, 235-260.
69. Squire, L.R., Shimamura, A.P., & Amaral, D.G. (1989). Memory and the hippocampus. In J.H. Byrne & W.O. Berry (Eds.), Neural models of plasticity: Experimental and theoretical approaches (pp. 208-239). San Diego, CA: Academic Press.
70. Stedron, J., Munakata, Y., & Sahni, S.D. (2002). In plain sight: simulating the role of memory in perseveration with visible solutions. Manuscript in preparation.
71. Stiles, J., Bates, E.A., Thai, D., Trauner, D., & Reilly, J. (2002). Linguistic and spatial cognitive development in children with pre- and perinatal focal brain injury: a ten-year overview from the San Diego longitudinal project. In M.H. Johnson, R.O. Gilmore & Y. Munakata (Eds.), Brain development and cognition: A reader (pp. 272-291). Oxford: Blackwell, 2nd edition.
72. Thelen, E., Schoner, G., Scheier, C., & Smith, L.B. (2001). The dynamics of embodiment: a field theory of infant perseverative reaching. Behavioral and Brain Sciences, 24, 1-86.
73. Thelen, E., & Smith, L.B. (1994). A dynamic systems approach to the development of cognition and action. Cambridge, MA: MIT Press.
74. Thomas, M., & Karmiloff-Smith, A. (in press). Are developmental disorders like cases of adult brain damage? Implications from connectionist modelling. Behavioral and Brain Sciences.
75. White, H. (1989). Learning in artificial neural networks: a statistical perspective. Neural Computation, 1, 425-464.
76. Wilkening, F., & Anderson, N.H. (1982). Comparison of two rule-assessment methodologies for studying cognitive development and knowledge structure. Psychological Bulletin, 92, 215-237.
77. Zelazo, P.D., Frye, D., & Rapus, T. (1996). An age-related dissociation between knowing rules and using them. Cognitive Development, 11, 37-63.
78. Zevin, J., & Seidenberg, M. (2002). Age of acquisition effects in reading and other tasks. Journal of Memory & Language, 47, 1-29.