A tutorial introduction to Bayesian models of cognitive development

Cognition

Volumn 120, Issue 3, 2011, Pages 302-321

  Perfors, Amy ^a   Tenenbaum, Joshua B ^b   Griffiths, Thomas L ^c   Xu, Fei ^c  

^a UNIVERSITY OF ADELAIDE   (Australia)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Bayesian models; Cognitive development

Indexed keywords

ARTICLE; BAYESIAN LEARNING; COGNITIVE DEVELOPMENT; CONCEPTUAL FRAMEWORK; HUMAN; NONHUMAN; PRIORITY JOURNAL; PSYCHOLOGY;

ALGORITHMS; BAYES THEOREM; CHILD DEVELOPMENT; CHILD, PRESCHOOL; COGNITION; DATA INTERPRETATION, STATISTICAL; GENERALIZATION (PSYCHOLOGY); HUMANS; INFANT; LANGUAGE DEVELOPMENT; MODELS, STATISTICAL;

EID: 79959810393     PISSN: 00100277     EISSN: 18737838     Source Type: Journal    
DOI: 10.1016/j.cognition.2010.11.015     Document Type: Article

References (152)

1. Alishahi A., Stevenson S. A computational model for early argument structure acquisition. Cognitive Science 2008, 32(5):789-834.
2. Anderson J. The adaptive nature of human categorization. Psychology Review 1991, 98(3):409-429.
3. Andrews M., Vigliocco G. Learning semantic representations with Hidden Markov Topics Models. 31st Annual conference of the cognitive science society 2009, Cognitive Science Society, Austin, TX. N. Taatgen, H. van Rijn, L. Schomaker, J. Nerbonne (Eds.).
4. Andrews M., Vigliocco G., Vinson D. Integrating experiential and distributional data to learn semantic representations. Psychological Review 2009, 116(3):463-498.
5. Ashby F., Alfonso-Reese L. Categorization as probability density estimation. Journal of Mathematical Psychology 1995, 39:216-233.
6. Atran S. Classifying nature across cultures. Thinking: An invitation to cognitive science 1995, Vol. 3:131-174. MIT Press, Cambridge, MA.
7. Baker C., Tenenbaum J.B., Saxe R. Goal inference as inverse planning. Proceedings of the 29th annual conference of the cognitive science society 2007, Cognitive Science Society, Austin, TX. D. McNamara, J. Trafton (Eds.).
8. Bannard C., Lieven E., Tomasello M. Modeling children's early grammatical knowledge. Proceedings of the National Academy of Sciences 2009, 106(41):17284-17289.
9. Berger J. Statistical decision theory and Bayesian analysis 1993, Springer, New York.
10. Berwick R. Learning from positive-only examples: The subset principle and three case studies. Machine Learning 1986, 2:625-645.
11. Bloom P. How children learn the meanings of words 2000, MIT Press, Cambridge, MA.
12. Box G., Tiao G. Bayesian inference in statistical analysis 1992, Wiley, New York.
13. Carey S. The child as word learner. Linguistic theory and psychological reality 1978, 264-293. MIT Press, Cambridge, MA. M. Halle, J. Bresnan, G. Miller (Eds.).
14. Carey S. Conceptual change in childhood 1985, Bradford Books, MIT Press.
15. Carey S. Origin of concepts 2009, Oxford University Press.
16. Carey S., Spelke E. Science and core knowledge. Philosophy of Science 1996, 63:515-533.
17. Cheng P. From covariation to causation: A causal power theory. Psychological Review 1997, 104:367-405.
18. Cox R. Probability, frequency, and reasonable expectation. American Journal of Physics 1946, 14:1-13.
19. Cox R. The algebra of productive inference 1961, Johns Hopkins University Press, Baltimore, MD.
20. de Finetti B. Prevision, its logical laws, its subjective sources. In studies in subjective probability 1937, J. Wiley and Sons, New York. 2nd ed. H. Kyburg, H. Smokler (Eds.).
21. de Finetti B. Theory of probability 1974, J. Wiley and Sons, New York. 2nd ed.
22. Deneve S. Bayesian inference in spiking neurons. Advances in Neural Information Processing Systems 2004, 17.
23. Dewar K., Xu F. Induction, overhypotheses, and the origin of abstract knowledge: Evidence from 9-month-old infants. Psychological Science 2010, 21:1871-1877.
24. Doucet A., Freitas N.d., Gordon N. Sequential Monte Carlo in practice 2001, Springer-Verlag.
25. Dowman M. Addressing the learnability of verb subcategorizations with Bayesian inference. Proceedings of the 22nd Annual conference of the cognitive science society 2000, Cognitive Science Society, Austin, TX. L. Gleitman, A. Joshi (Eds.).
26. Doya K., Ishii S., Pouget A., Rao R. Bayesian brain: Probabilistic approaches to neural coding 2007, MIT Press, Cambridge, MA.
27. Feldman J. Minimization of boolean complexity in human concept learning. Nature 2000, 407:630-633.
28. Feldman N., Griffiths T.L. Learning phonetic categories by learning a lexicon. 31st Annual conference of the cognitive science society 2009, Cognitive Science Society, Austin, TX. N. Taatgen, H. van Rijn, L. Schomaker, J. Nerbonne (Eds.).
29. Feldman, J., & Tremoulet, P. (2008). The attribution of mental architecture from motion: Towards a computational theory. Tech. rep. no. RuCCS TR-87, Rutgers University.
30. Feldman N., Morgan J., Griffiths T.L. The influence of categories on perception: Explaining the perceptual magnet effect as optimal statistical inference. Psychological Review 2009, 116(4):752-782.
31. Fisher R. Probability, likelihood, and quantity of information in the logic of uncertain inference. Proceedings of the Royal Society 1933, 146:1-8.
32. Fodor J. The language of thought 1975, Thomas Y. Crowell Company, New York, NY.
33. Fodor J. Representations: Philosophical essays on the foundations of cognitive science 1981, MIT Press, Cambridge, MA.
34. Fodor J., Pylyshyn Z. Connectionism and cognitive architecture: A critical analysis. Cognition 1988, (28):3-71.
35. Frank M., Goldwater S., Griffiths T.L., Tenenbaum J.B. Modeling human performance in statistical word segmentation. Proceedings of the 29th annual conference of the cognitive science society 2007, Cognitive Science Society, Austin, TX. D. McNamara, J. Trafton (Eds.).
36. Frank M., Ichinco D., Tenenbaum J.B. Principles of generalization for learning sequential structure in language. Proceedings of the 30th annual conference of the cognitive science society 2008, Cognitive Science Society, Austin, TX. V. Sloutsky, B. Love, K. McRae (Eds.).
37. Frank M., Goodman N., Tenenbaum J.B. Using speakers' referential intentions to model early cross-situational word learning. Psychological Science 2009, 20(5):578-585.
38. Funahashi K. On the approximate realization of continuous mappings by neural networks. Neural Networks 1989, 2:183-192.
39. Funahashi K. Multilayer neural networks and Bayes decision theory. Neural Networks 1998, 11(2):209-213.
40. Gazzaniga M., Ivry R., Mangun G. Cognitive neuroscience: The biology of the mind 2002, W.W. Norton & Company, New York, NY. 2 ed.
41. Gelman A., Carlin J., Stern H., Rubin D. Bayesian data analysis 2004, Chapman & Hall. 2nd ed.
42. Gergely G., Csibra G. Teleological reasoning in infancy: The naive theory of rational action. Trends in Cognitive Science 2003, 7(7):287-292.
43. Gilks W., Richardson S., Spiegelhalter D. Markov chain Monte Carlo in practice 1996, Chapman & Hall.
44. Goldstein D., Gigerenzer G. Models of ecological rationality: The recognition heuristic. Psychological Review 2002, 109:75-90.
45. Goldwater S., Griffiths T., Johnson M. Interpolating between types and tokens by estimating power law generators. Advances in Neural Information Processing Systems 2006, 18.
46. Goldwater S., Griffiths T.L., Johnson M. Bayesian framework for word segmentation: Exploring the effects of context. Cognition 2007, 112:21-54.
47. Goodman N. Fact, fiction, and forecast 1955, Harvard University Press, Cambridge, MA.
48. Goodman N., Baker C., Bonawitz E., Manisinghka V., Gopnik A., Wellman H., et al. Intuitive theories of mind: A rational approach to false belief. Proceedings of the 28th annual conference of the cognitive science society 2006, Cognitive Science Society, Austin, TX. R. Sun, N. Miyake (Eds.).
49. Goodman N., Griffiths T.L., Feldman J., Tenenbaum J.B. A rational analysis of rule-based concept learning. Proceedings of the 29th annual conference of the cognitive science society 2007, Cognitive Science Society, Austin, TX. D. McNamara, J. Trafton (Eds.).
50. Gopnik A. The scientist as child. Philosophy of Science 1996, 63:485-514.
51. Gopnik A., Glymour C., Sobel D., Schulz L., Kushnir T., Danks D. A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review 2004, 111(1):1-30.
52. Gopnik A., Meltzoff A.N. Words, thoughts, and theories 1997, MIT Press, Cambridge, MA.
53. Griffiths T.L., Chater N., Kemp C., Perfors A., Tenenbaum J.B. Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences 2010, 14(8):357-364.
54. Griffiths T.L., Kalish M. Language evolution by iterated learning with Bayesian agents. Cognitive Science 2007, 31:441-480.
55. Griffiths T.L., Sanborn A., Canini K., Navarro D. Categorization as nonparameteric Bayesian density estimation. The probabilistic mind: Prospects for Bayesian cognitive science 2008, Oxford University Press, Oxford. M. Oaksford, N. Chater (Eds.).
56. Griffiths T.L., Steyvers M., Tenenbaum J.B. Topics in semantic representation. Psychological Review 2007, 114(2):211-244.
57. Griffiths T.L., Tenenbaum J.B. Structure and strength in causal induction. Cognitive Psychology 2005, 51:354-384.
58. Griffiths T.L., Tenenbaum J.B. Theory-based causal induction. Psychological Review 2009, 116:661-716.
59. Grünwald P., Myung J., Pitt M. Advances in minimum description length: Theory and applications 2005, MIT Press, Cambridge, MA.
60. Heller K., Sanborn A., Chater N. Hierarchical learning of dimensional biases in human categorization. Advances in neural information processing systems 2009, Vol. 22. MIT Press, Cambridge, MA.
61. Henderson L., Goodman N., Tenenbaum J.B., Woodward J. The structure and dynamics of scientific theories: A hierarchical Bayesian perspective. Philosophy of Science 2010, 77(2).
62. Hertz J., Krogh A., Palmer R. Introduction to the theory of neural computation: Santa Fe Institute studies in the science of complexity 1991, Vol. 1. Perseus Books, Reading, MA.
63. Hsu A., Griffiths T.L. Differential use of implicit negative evidence in generative and discriminative language learning. Advances in Neural Information Processing Systems 2009, Vol. 22. MIT Press, Cambridge, MA.
64. Inagaki K., Hatano G. Young children's thinking about biological world 2002, Psychology press, New York.
65. Jaynes E. Probability theory: The logic of science 2003, Cambridge University Press, Cambridge.
66. Jeffreys H. Theory of probability 1939, Clarendon Press, Oxford.
67. Karmiloff-Smith A. The child is a theoretician, not an inductivist. Mind and Language 1988, 3:183-195.
68. Keil F.C. Semantic and conceptual development: An ontological perspective 1979, Harvard University Press, Cambridge, MA.
69. Keil F.C. Concepts, kinds, and cognitive development 1989, MIT Press, Cambridge, MA.
70. Kemp C., Perfors A., Tenenbaum J.B. Learning domain structures. Proceedings of the 26th Annual Conference of the Cognitive Science Society 2004, 672-677. Cognitive Science Society, Austin, TX. K. Forbus, D. Gentner, T. Regier (Eds.).
71. Kemp C., Goodman N., Tenenbaum J. Learning causal schemata. Proceedings of the 29th annual conference of the cognitive science society 2007, Cognitive Science Society, Austin, TX. D. McNamara, J. Trafton (Eds.).
72. Kemp C., Perfors A., Tenenbaum J.B. Learning overhypotheses with hierarchical Bayesian models. Developmental Science 2007, 10(3):307-321.
73. Kemp C., Tenenbaum J.B., Niyogi S., Griffiths T.L. A probabilistic model of theory formation. Cognition 2010, 114(2):165-196.
74. Kirby S., Dowman M., Griffiths T.L. Innateness and culture in the evolution of language. Proceedings of the National Academy of Sciences 2007, 104(12):5241-5245.
75. Kolmogorov A. Three approaches to the quantitative definition of information. Problems of Information and Transmission 1965, 1(1):1-7.
76. Landau B., Smith L., Jones S. The importance of shape in early lexical learning. Cognitive Development 1988, 3:299-321.
77. Laplace P.S. A philosophical essay on probabilities 1816, Dover Publications.
78. Lee P. Bayesian statistics 1997, Wiley, New York.
79. Lee M. A hierarchical Bayesian model of human decision-making on an optimal stopping problem. Cognitive Science 2006, 30(3):555-580.
80. Lee M., Fuss I., Navarro D. A Bayesian approach to diffusion models of decision-making and response time. Advances in Neural Information Processing Systems 2007, Vol. 19:809-815. MIT Press, Cambridge, MA.
81. Lee T., Mumford D. Hierarchical Bayesian inference in the visual cortex. Journal of the Optical Society of America A 2003, 20:1434-1448.
82. Lucas, C.G., Griffiths, T.L., Xu, F., Fawcett, C., Gopnik, A., Kushnir, T., et al. (submitted for publication). The child as econometrician: A rational model of preference understanding in children.
83. Lu H., Yuille A., Liljeholm M., Cheng P., Holyoak K. Bayesian generic priors for causal learning. Psychological Review 2008, 115:955-984.
84. Ma W., Beck J., Latham P., Pouget A. Bayesian inference with probabilistic population codes. Nature Neuroscience 2006, 9(11):1432-1438.
85. MacKay D. Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks. Network: Computation in Neural Systems 1995, 6:469-505.
86. MacKay D. Introduction to Monte Carlo methods. Learning in graphical models 1998, 175-204. MIT Press, Cambridge, MA. M. Jordan (Ed.).
87. MacKay D. Information theory, inference, and learning algorithms 2003, Cambridge University Press, Cambridge, UK.
88. Mandler J., McDonough L. Concept formation in infancy. Cognitive Development 1993, 9:291-318.
89. Marcus G., Pinker S., Ullman M., Hollander M., Rosen T., Xu F., et al. Overregularization in language acquisition. Monographs of the Society for Research in Child Development 1992, 57:1-178.
90. Markman E. Categorization and naming in children 1989, MIT Press.
91. Markman E. Constraints children place on word meanings. Cognitive Science 1990, 14:57-77.
92. Marr D. Vision: A computational investigation into the human representation and processing of visual information 1982, Henry Holt & Company.
93. McClelland J. Connectionist models and Bayesian inference. Rational models of cognition 1998, 21-53. Oxford University Press, Oxford. M. Oaksford, N. Chater (Eds.).
94. McClelland J., Votvinick M., Noelle D., Plaut D., Rogers T., Seidenberg M., et al. Letting structure emerge: Connectionist and dynamic systems approaches to cognition. Trends in Cognitive Sciences 2010, 14(8):348-356.
95. McCloskey M. Intuitive physics. Scientific American 1983, 284:114-123.
96. Folkbiology 1999, Bradford Books, Cambridge, MA. D.L. Medin, S. Atran (Eds.).
97. Murphy G.L., Medin D.L. The role of theories in conceptual coherence. Psychological Review 1985, 92:289-316.
98. Navarro D. From natural kinds to complex categories. Proceedings of the 28th annual conference of the cognitive science society 2006, 621-626. Cognitive Science Society, Austin, TX. R. Sun, N. Miyake (Eds.).
99. Navarro D., Griffiths T.L. A nonparametric Bayesian method for inferring features from similarity judgments. Advances in Neural Information Processing Systems 2007, 19.
100. Navarro D., Griffiths T.L., Steyvers M., Lee M. Modeling individual differences using Dirichlet processes. Journal of Mathematical Psychology 2006, 50:101-122.
101. Neal, R. (1993). Probabilistic inference using Markov chain Monte Carlo methods. Tech. rep. no. CRG-TR-93-1, University of Toronto.
102. Neal, R. (1994). Priors for infinite networks. Tech. rep. no. CRG-TR-94-1, University of Toronto.
103. Neal R. Bayesian learning for neural networks 1996, Springer.
104. Nichols S., Stich S. Mindreading: An integrated account of pretense, self-awareness and understanding other minds 2003, Oxford University Press, Oxford.
105. Nosofsky J. Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General 1986, 115(1):39-57.
106. Pearl J. Causality: Models, reasoning, and inference 2000, Cambridge University Press.
107. Perfors A., Tenenbaum J.B. Learning to learn categories. Proceedings of the 31st annual conference of the cognitive science society 2009, 136-141. Cognitive Science Society, Austin, TX. N. Taatgen, H. van Rijn, L. Schomaker, J. Nerbonne (Eds.).
108. Perfors A., Tenenbaum J.B., Regier T. Poverty of the stimulus? A rational approach. Proceedings of the 28th annual conference of the cognitive science society 2006, 663-668. Cognitive Science Society, Austin, TX.
109. Perfors, A., Tenenbaum, J.B., & Regier, T. (in press). The learnability of abstract syntactic principles. Cognition.
110. Perfors A., Tenenbaum J.B., Wonnacott E. Variability, negative evidence, and the acquisition of verb argument constructions. Journal of Child Language 2010, 37:607-642.
111. Pinker S. Learnability and cognition: The acquisition of argument structure 1989, MIT Press, Cambridge, MA.
112. Pouget A., Dayan P., Zemel R. Inference and computation with population codes. Annual Reviews in Neuroscience 2003, 26:381-410.
113. Rao R. Bayesian computation in recurrent neural circuits. Neural Computation 2004, 16:1-38.
114. Rao R. Neural models of Bayesian belief propagation. Bayesian brain: Probabilistic approaches to neural coding 2007, 239-267. MIT Press, Cambridge, MA. K. Doya, S. Ishii, A. Pouget, R. Rao (Eds.).
115. Rao R., Olshausen B., Lewicki M. Probabilistic models of the brain: Perception and neural function 2002, MIT Press, Cambridge, MA.
116. Repacholi B.M., Gopnik A. Early reasoning about desires: Evidence from 14- and 18-month-olds. Developmental Psychology 1997, 33(1):12-21.
117. Rissanen J. Modeling by shortest data description. Automatica 1978, 14(5):465-471.
118. Rosch E. Principles of categorization. Cognition and categorization 1978, 27-48. Lawrence Erlbaum. E. Rosch, B.B. Lloyd (Eds.).
119. Rosch E., Mervis C., Gray W., Johnson D., Boyes-Braem P. Basic objects in natural categories. Cognitive Psychology 1976, 8:382-439.
120. Roy D., Kemp C., Mansinghka V., Tenenbaum J.B. Learning annotated hierarchies from relational data. Advances in Neural Information Processing Systems 2006, 19.
121. Rumelhart D., McClelland J. Parallel distributed processing: Explorations in the microstructure of cognition 1986, Vol. 1. MIT Press, Cambridge, MA.
122. Russell S., Norvig P. Artificial intelligence: A modern approach 2010, Prentice Hall. 3rd ed.
123. Sanborn A., Griffiths T.L., Navarro D. Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review 2010.
124. Savage L. Foundations of statistics 1954, J. Wiley & Sons, New York, NY.
125. Schmidt L., Kemp C., Tenenbaum J.B. Nonsense and sensibility: Inferring unseen possibilities. Proceedings of the 27th annual conference of the cognitive science society 2006, Cognitive Science Society, Austin, TX. R. Sun, N. Miyake (Eds.).
126. Shafto P., Kemp C., Mansinghka V., Gordon M., Tenenbaum J.B. Learning cross-cutting systems of categories. Proceedings of the 28th annual conference of the cognitive science society 2006, Cognitive Science Society, Austin, TX. R. Sun, N. Miyake (Eds.).
127. Shultz, T., & Vogel, A. (2004). A connectionist model of the development of transitivity. In K. Forbus, D. Gentner, & T. Regier (Eds.), Proceedings of the 26th annual conference of the cognitive science society (pp. 1243-1248).
128. Siegler R. U-shaped interest in U-shaped development - A nd what it means. Journal of Cognition and Development 2004, 5(1):1-10.
129. Simons D., Keil F. An abstract to concrete shift in the development of biological thought: The insides story. Cognition 1995, 56:129-163.
130. Sivia D. Data analysis: A Bayesian tutorial 1996, Oxford University Press, Oxford.
131. Smith K. Iterated learning in populations of Bayesian agents 2009, Cognitive Science Society, Austin, TX.
132. Smith L., Jones S., Landau B., Gershkoff-Stowe L., Samuelson L. Object name learning provides on-the-job training for attention. Psychological Science 2002, 13(1):13-19.
133. Sobel D., Tenenbaum J.B., Gopnik A. Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers. Cognitive Science 2004, 28:303-333.
134. Solomonoff R. A formal theory of inductive inference, parts 1 and 2. Information and Control 1964, 7(1-22):224-254.
135. Sommers F. Structural ontology. Philosophia 1971, 1:21-42.
136. Spelke E., Kinzler K. Core knowledge. Developmental Science 2007, 10(1):89-96.
137. Spelke E., Phillips A., Woodward A. Infants' knowledge of object motion and human action. Causal cognition: A multidisciplinary debate 1995, 44-78. Oxford University Press, Oxford.
138. Steyvers M., Tenenbaum J.B., Wagenmakers E., Blum B. Inferring causal networks from observations and interventions. Cognitive Science 2003, 27:453-489.
139. Stinchcombe M., White H. Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions. Proceedings of the International Joint Conference on Neural Networks 1989, 607-611.
140. Tenenbaum J.B., Griffiths T.L. Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences 2001, 24:629-641.
141. Tversky A., Kahneman D. Judgment under uncertainty: Heuristics and biases. Science 1974, 135:1124-1131.
142. Vallabha G., McClelland J., Pons F., Werker J., Amano S. Unsupervised learning of vowel categories from infant-directed speech. Proceedings of the National Academy of Sciences 2007, 104(33):13273-13278.
143. van Dantzig D. Statistical priesthood (savage on personal probabilities). Statistica Neerlandica 1957, 2:1-16.
144. Verma D., Rao R. Goal-based imitation as probabilistic inference over graphical models. Advances in Neural Information Processing Systems 2006, 18.
145. Vitànyi P., Li M. Minimum description length induction, Bayesianism, and Kolmogorov complexity. IEEE Transactions on Information Theory 2000, 46(2):446-464.
146. Waldmann M.R. Knowledge-based causal induction. The psychology of learning and motivation 1996, Vol. 34:47-88. Academic Press, San Diego.
147. Wellman H.M. The child's theory of mind 1990, MIT Press, Cambridge, MA.
148. Wexler K., Culicover P. Formal principles of language acquisition 1980, MIT Press, Cambridge, MA.
149. Woodward A. Infants' ability to distinguish between purposeful and non-purposeful behaviors. Infant Behavior and Development 1998, 22(2):145-160.
150. Xu F., Tenenbaum J.B. Word learning as Bayesian inference. Psychological Review 2007, 114(2):245-272.
151. Xu F., Tenenbaum J.B. Sensitivity to sampling in Bayesian word learning. Developmental Science 2007, 10:288-297.
152. Zemel R., Huys Q., Natarajan R., Dayan P. Probabilistic computation in spiking populations. Advances in Neural Information Processing Systems 2005, 17.