Letting structure emerge: Connectionist and dynamical systems approaches to cognition

Trends in Cognitive Sciences

Volumn 14, Issue 8, 2010, Pages 348-356

  McClelland, James L ^a   Botvinick, Matthew M ^b   Noelle, David C ^c   Plaut, David C ^d   Rogers, Timothy T ^e   Seidenberg, Mark S ^e   Smith, Linda B ^f  

^a STANFORD UNIVERSITY   (United States)

^b PRINCETON UNIVERSITY   (United States)

^c Merced   (United States)

^d CARNEGIE MELLON UNIVERSITY   (United States)

^e UNIVERSITY OF WISCONSIN MADISON   (United States)

^f INDIANA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COGNITIVE ABILITY; DYNAMICAL SYSTEMS APPROACH; PROBABILISTIC APPROACHES;

LINGUISTICS;

DYNAMICAL SYSTEMS;

ARTICLE; BEHAVIOR; COGNITION; COGNITIVE DEVELOPMENT; HUMAN; LANGUAGE; PROBABILITY; STATISTICAL MODEL; THINKING;

COGNITION; HUMANS; LANGUAGE; LEARNING; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; PROBABILITY;

EID: 77955270906     PISSN: 13646613     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tics.2010.06.002     Document Type: Article

References (69)

1. Johnson S.B. Emergence: The connected lives of ants, brains, cities, and software 2001, Scribner's.
2. Griffiths T.L., et al. Probabilistic models of cognition: Exploring the laws of thought. Trends Cogn. Sci. 2010, 14:357-364.
3. Marr D. Vision 1982, W. H. Freeman.
4. Chomsky N. Aspects of the theory of syntax 1965, MIT Press.
5. Sternberg, D. and McClelland, J.L. (2009) When should we expect indirect effects in human contingency learning? In Proceedings of the 31st Annual Conference of the Cognitive Science Society (Taatgen, N.A. and van Rijn, H., eds), pp. 206-211, Cognitive Science Society.
6. Movellan J.R., McClelland J.L. Learning continuous probability distributions with symmetric diffusion networks. Cogn. Sci. 1993, 17:463-496.
7. Hinton G.E., Salakhutdinov R.R. Reducing the dimensionality of data with neural networks. Science 2006, 313:504-507.
8. Perfors, A. et al. (2006) Poverty of the stimulus? A rational approach. In Proceedings of the 28th Annual Conference of the Cognitive Science Society, pp. 663-668, Lawrence Erlbaum Associates.
9. Kemp C., Tenenbaum J.B. Structured statistical models of inductive reasoning. Psychol. Rev. 2009, 116:20-58.
10. Sobel D., et al. Children's causal inferences from indirect evidence: backwards blocking and Bayesian reasoning in preschoolers. Cogn. Sci. 2004, 28:303-333.
11. Piaget J. The construction of reality in the child 1954, Basic Books.
12. Baillargeon R. How do infants learn about the physical world?. Curr. Dir. Psychol. Sci. 1994, 3:133-140.
13. Thelen E., et al. The dynamics of embodiment: A field theory of infant perseverative reaching. Behav. Brain Sci. 2001, 24:1-86.
14. Clearfield M.W., et al. Cue salience and infant perseverative reaching: Tests of the dynamic field theory. Dev. Sci. 2009, 12:26-40.
15. Clearfield M.W., et al. Young infants reach correctly in A-not-B tasks: On the development of stability and perseveration. Infant Behav. Dev. 2006, 29:435-444.
16. Spencer J.P., et al. Tests of a dynamic systems account of the A-not-B error: The influence of prior experience on the spatial memory abilities of two-year-olds. Child Dev. 2001, 72:1327-1346.
17. Smith L.B., et al. Knowing in the context of acting: The task dynamics of the A-not-B error. Psychol. Rev. 1999, 106:235-260.
18. Diedrich F.J., et al. Motor memory is a factor in infant perseverative errors. Dev. Sci. 2000, 3:479-494.
19. Lew A., et al. Postural change effects on infants' AB task performance: Visual, postural, or spatial?. J. Exp. Child Psychol. 2007, 97:1-3.
20. Rogers T.T., McClelland J.L. Semantic cognition: A parallel distributed processing approach 2004, MIT Press.
21. Elman J.L. Finding structure in time. Cogn. Sci. 1990, 14:179-211.
22. Rohde D.L.T., Plaut D.C. Language acquisition in the absence of explicit negative evidence: How important is starting small?. Cognition 1999, 72:67-109.
23. Keil F.C. Constraints on knowledge and cognitive development. Psychol. Rev. 1981, 88:197-227.
24. Gelman R. First principles organize attention to and learning about relevant data: Number and the animate/inanimate distinction as examples. Cogn. Sci. 1990, 14:79-106.
25. Colunga E., Smith L.B. From the lexicon to expectations about kinds: a role for associative learning. Psychol. Rev. 2005, 112:347-382.
26. Gelman S.A., Markman E.M. Categories and induction in young children. Cognition 1986, 23:183-209.
27. Rogers T.T., et al. The structure and deterioration of semantic memory: A neuropsychological and computational investigation. Psychol. Rev. 2004, 111:205-235.
28. McClelland, J.L. et al. (2009) Semantic cognition: Its nature, its development, and its neural basis. In The cognitive neurosciences IV (Gazzaniga, M., ed.), pp. 1047-1066, MIT Press.
29. Rogers T.T., McClelland J.L. A simple model from a powerful framework that spans levels of analysis. Behav. Brain Sci. 2008, 31:729-749.
30. Botvinick M.M., An J. Goal-directed decision making in prefrontal cortex: A computational framework. Advances in Neural Information Processing Systems 2008, 169-176. Curran Associates, Inc. D. Koller (Ed.).
31. McClelland J.L., Chappell M. Familiarity breeds differentiation: A subjective-likelihood approach to the effeccts of experience in recognition memory. Psychol. Rev. 1998, 105:724-760.
32. McCloskey M., Cohen N.J. Catastrophic interference in connectionist networks: The sequential learning problem. The psychology of learning and motivation 1989, 109-165. Academic Press. G.H. Bower (Ed.).
33. Ratcliff R. Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions. Psychol. Rev. 1990, 97:285-308.
34. McClelland J.L., et al. Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychol. Rev. 1995, 102:419-457.
35. Feldman N.H., et al. The influence of categories on perception: Explaining the perceptual magnet effect as optimal statistical inference. Psychol. Rev. 2009, 116:752-782.
36. Hay J. Lexical frequency in morphology: Is everything relative?. Linguistics 2001, 39:1041-1070.
37. Bybee J., McClelland J.L. Alternatives to the combinatorial paradigm of linguistic theory based on domain general principles of human cognition. Ling. Rev. 2005, 22:381-410.
38. Bybee J.L. Morphology: A study of the relation between meaning and form 1985, John Benjamins.
39. Gonnerman L.A., et al. A distributed connectionist approach to morphology: Evidence from graded semantic and phonological effects in lexical priming. J. Exp. Psychol. Gen. 2007, 136:323-345.
40. Culicover P.W. Syntactic nuts: Hard cases in syntax. Volume 1, Foundations of Syntax 1999, Oxford University Press.
41. Prince A., Smolensky P. Optimality theory: Constraint interaction in generative grammar 2004, Blackwell.
42. Smolensky, P. and Legendre, G. (2006) The harmonic mind: From neural computation to Optimality-theoretic grammar, Vol. 1: Cognitive architecture, Vol. 2: Linguistic and philosophical Implications, MIT Press.
43. Rumelhart, D.E. et al. (1986) Schemata and sequential thought processes in PDP models. In Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 2) (McClelland, J.L. et al., eds), pp. 7-57, MIT Press.
44. Plaut D.C., et al. Understanding normal and impaired word reading: Computational principles in quasi-regular domains. Psychol. Rev. 1996, 103:56-115.
45. Plaut D.C., Gonnerman L.M. Are non-semantic morphological effects incompatible with a distributed connectionist approach to lexical processing?. Lang. Cogn. Proc. 2000, 15:445-485.
46. Pinker S., Ullman M.T. The past and future of the past tense. Trends Cogn. Sci. 2002, 6:456-463.
47. Rumelhart, D.E. and McClelland, J.L. (1986) On learning past tenses of English verbs. In Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 2) (McClelland, J.L. et al., eds), pp. 216-271, MIT press.
48. Joanisse M.F., Seidenberg M.S. Impairments in verb morphology following brain injury: a connectionist model. Proc. Natl. Acad. Sci. 1999, 96:7592-7597.
49. Seidenberg M.S., McClelland J.L. A distributed, developmental model of word recognition and naming. Psychol. Rev. 1989, 96:523-568.
50. Elman J.L. Distributed representations, simple recurrent networks, and grammatical structure. Mach. Learn. 1991, 7:195-224.
51. Servan-Schreiber D., et al. Graded state machines: The representation of temporal contingencies in simple recurrent networks. Mach. Learn. 1991, 7:161-193.
52. St.John M.F., McClelland J.L. Learning and applying contextual constraints in sentence comprehension. Artif. Intell. 1990, 46:217-257.
53. St.John M.F. The story gestalt: A model of knowledge-intensive processes in text comprehension. Cogn. Sci. 1992, 16:271-306.
54. Jansen B.R.J., van der Maas H.L.J. Evidence for the phase transition from Rule I to Rule II on the balance scale task. Dev. Rev. 2001, 21:450-494.
55. Ferretti R.P., Butterfield E.C. Are childrens' rule-assessment classifications invariant across instances of problem types?. Child Dev. 1986, 57:1419-1428.
56. McClelland J.L. Parallel distributed processing: Implications for cognition and development. Parallel distributed processing: Implications for psychology and neurobiology 1989, 8-45. Oxford University Press. R. Morris (Ed.).
57. Schapiro A.C., McClelland J.L. A connectionist model of a continuous developmental transition in the balance scale task. Cognition 2009, 110:395-411.
58. Denny-Brown D. The cerebral control of movement 1966, Charles C. Thomas.
59. Thelen E., Smith L.B. A dynamic systems approach to the development of cognition and action 1994, MIT Press.
60. Farah M.J., McClelland J.L. A computational model of semantic memory impairment: Modality-specificity and emergent category-specificity. J. Exp. Psychol. Gen. 1991, 120:339-357.
61. Schwartz M.F., et al. The quantitative description of action disorganization after brain damage: A case study. Cogn. Neuropsychol. 1991, 8:381-414.
62. Botvinick M., Plaut D.C. Doing without schema hierarchies: A recurrent connectionist approach to normal and impaired routine sequential action. Psychol. Rev. 2004, 111:395-429.
63. Botvinick M., Plaut D.C. Short-term memory for serial order: A recurrent neural network model. Psychol. Rev. 2006, 113:201-233.
64. Hirsh-Pasek, K. et al. (2000) An emergentist coalition model for word learning: mapping words to objects is a product of the interaction of multiple cues. In Becoming a Word Learner: a Debate on Lexical Acquisition (Hirsh-Pasek, K. and Golinkoff, R.M., eds), pp. 136-164, Oxford Press.
65. Mayor J., Plunkett K. A neurocomputational account of taxonomic responding and fast mapping in early word learning. Psychol. Rev. 2010, 117:1-31.
66. McClelland J.L., Patterson K. Rules or connections in past-tense inflections: What does the evidence rule out?. Trends Cogn. Sci. 2002, 6:465-472.
67. Marchman V., Bates E. Continuity in lexical and morphological development: A test of the critical mass hypothesis. J. Child Lang. 1994, 21:339-366.
68. MacWhinney B. Models of the emergence of language. Ann. Rev. Psychol. 1998, 49:199-227.
69. Rumelhart D.E., Todd P.M. Learning and connectionist representations. Attention and performance XIV: Synergies in experimental psychology, artificial intelligence, and cognitive neuroscience 1993, 3-30. MIT Press. D.E. Meyer, S. Kornblum (Eds.).