Learning to Learn Causal Models

Cognitive Science

Volumn 34, Issue 7, 2010, Pages 1185-1243

  Kemp, Charles ^a   Goodman, Noah D ^b   Tenenbaum, Joshua B ^b  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Categorization; Causal learning; Hierarchical Bayesian models; Learning inductive constraints; Learning to learn; Transfer learning

Indexed keywords

EID: 78650147550     PISSN: 03640213     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1551-6709.2010.01128.x     Document Type: Article

References (67)

1. Aldous, D. (1985). Exchangeability and related topics. In P. L. Hennequin (Ed.), École d'Été de Probabilités de Saint-Flour, XIII-1983 (pp. 1-198). Berlin: Springer.
2. Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98(3), 409-429.
3. Austerweil, J., & Griffiths, T. L. (2009). Analyzing human feature learning as nonparametric Bayesian inference. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems 21 (pp. 97-104).
4. Baxter, J. (1998). Theoretical models of learning to learn. In S. Thrun & L. Pratt (Eds.), Learning to learn (pp. 71-94). Norwell, MA: Kluwer.
5. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
6. Bloom, P. (2000). How children learn the meanings of words. Cambridge, MA: MIT Press.
7. Buchsbaum, D., Griffiths, T. L., Gopnik, A., & Baldwin, D. (2009). Learning from actions and their consequences: Inferring causal variables from continuous sequences of human action. In N. A. Taatgen & H. Van Rijn (Eds.), Proceedings of the 31st annual conference of the Cognitive Science Society (pp. 2493-2498). Austin, TX: Cognitive Science Society.
8. Caruana, R. (1997). Multitask learning. Machine Learning, 28, 41-75.
9. Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367-405.
10. Danks, D. (2007). Theory unification and graphical models in human categorization. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation. (pp. 173-189). Oxford, England: Oxford University Press.
11. Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003). Bayesian data analysis (2nd ed.). New York: Chapman & Hall.
12. George, E. I., & McCulloch, R. E. (1993). Variable selection via Gibbs sampling. Journal of the American Statistical Association, 88, 881-889.
13. Geyer, C. J. (1991). Markov chain Monte Carlo maximum likelihood. In E. M. Keramida (Ed.), Computing science and statistics: Proceedings of the 23rd symposium interface (pp. 156-163). Fairfax Station, VA: Interface Foundation.
14. Glymour, C. (2001). The mind's arrows: Bayes nets and graphical causal models in psychology. Cambridge, MA: MIT Press.
15. Good, I. J. (1980). Some history of the hierarchical Bayesian methodology. In J. M. Bernardo, M. H. DeGroot, D. V. Lindley, & A. F. M. Smith (Eds.), Bayesian statistics (pp. 489-519). Valencia, Spain: Valencia University Press.
16. Goodman, N. D., Mansinghka, V. K., & Tenenbaum, J. B. (2007). Learning grounded causal models. In D. S. Mc Namara & J. G. Trafton (Eds.), Proceedings of the 29th annual conference of the Cognitive Science Society (pp. 305-310). Austin, TX: Cognitive Science Society.
17. Gopnik, A., & Glymour, C. (2002). Causal maps and Bayes nets: A cognitive and computational account of theory-formation. In P. Carruthers, S. Stich & M. Siegal (Eds.), The cognitive basis of science (pp. 117-132). Cambridge, England: Cambridge University Press.
18. Gopnik, A., Glymour, C., Sobel, D., Schulz, L., Kushnir, T., & Danks, D. (2004). A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review, 111, 1-31.
19. Gopnik, A., & Sobel, D. (2000). Detecting blickets: How young children use information about novel causal powers in categorization and induction. Child Development, 71, 1205-1222.
20. Gopnik, A., Sobel, D. M., Shulz, L. E., & Glymour, C. (2001). Causal learning mechanisms in very young children: Two, three, and four-year-olds infer causal relations from patterns of variation and covariation. Developmental Psychology, 37, 620-629.
21. Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51, 354-384.
22. Griffiths, T. L., & Tenenbaum, J. B. (2007). Two proposals for causal grammars. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 323-346). Oxford, England: Oxford University Press.
23. Harlow, H. F. (1949). The formation of learning sets. Psychological Review, 56, 51-65.
24. Jain, S., & Neal, R. M. (2004). A split-merge Markov chain Monte Carlo procedure for the Dirichlet Process mixture model. Journal of Computational and Graphical Statistics, 13, 158-182.
25. Jones, S. S., Smith, L. B., & Landau, B. (1991). Object properties and knowledge in early lexical learning. Child Development, 62, 499-516.
26. Keil, F. C. (1979). Semantic and conceptual development. Cambridge, MA: Harvard University Press.
27. Keil, F. C. (1989). Concepts, kinds, and cognitive development. Cambridge, MA: MIT Press.
28. Kelley, H. H. (1972). Causal schemata and the attribution process. In E. E. Jones, D. E. Kanouse, H. H. Kelley, R. S. Nisbett, S. Valins, & B. Weiner (Eds.), Attribution: perceiving the causes of behavior (pp. 151-174). Morristown, NJ: General Learning Press.
29. Kemp, C. (2008). The acquisition of inductive constraints. Unpublished doctoral dissertation, Massachusetts Institute of Technology, Cambridge, MA.
30. Kemp, C., Griffiths, T. L., Stromsten, S., & Tenenbaum, J. B. (2004). Semi-supervised learning with trees. In S. Thrun, L. Saul & B. Schölkopt (Eds.), Advances in neural information processing systems 16 (pp. 257-264). Cambridge, England, MA: MIT Press.
31. Kemp, C., Perfors, A., & Tenenbaum, J. B. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10(3), 307-321.
32. Kemp, C., & Tenenbaum, J. B. (2008). The discovery of structural form. Proceedings of the National Academy of Sciences, 105(31), 10687-10692.
33. Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T., & Ueda, N. (2006). Learning systems of concepts with an infinite relational model. In Y. Gil & R. J. Mooney (Eds.), Proceedings of the 21st national conference on artificial intelligence (pp. 381-388). Menlo park, CA: AAAI Press.
34. Kemp, C., Tenenbaum, J. B., Niyogi, S., & Griffiths, T. L. (2010). A probabilistic model of theory formation. Cognition, 114(2), 165-196.
35. Kushnir, T., & Gopnik, A. (2005). Children infer causal strength from probabilities and interventions. Psychological Science, 16, 678-683.
36. Lagnado, D., & Sloman, S. A. (2004). The advantage of timely intervention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 856-876.
37. Lien, Y., & Cheng, P. W. (2000). Distinguishing genuine from spurious causes: A coherence hypothesis. Cognitive Psychology, 40, 87-137.
38. Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: A network model of category learning. Psychological Review, 111, 309-332.
39. Lu, H., Yuille, A. L., Liljeholm, M., Cheng, P. W., & Holyoak, K. J. (2008). Bayesian generic priors for causal learning. Psychological Review, 115(4), 955-984.
40. Lucas, C. G., & Griffiths, T. L. (2010). Learning the form of causal relationships using hierarchical Bayesian models. Cognitive Science 34, 113-147.
41. Mandler, J. M. (2004). The foundations of mind: origins of conceptual thought. New York: Oxford University Press.
42. Massey, C., & Gelman, R. (1988). Preschoolers' ability to decide whether pictured unfamiliar objects can move themselves. Developmental Psychology, 24, 307-317.
43. Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207-238.
44. Medin, D. L., Wattenmaker, W. D., & Hampson, S. E. (1987). Family resemblance, conceptual cohesiveness and category construction. Cognitive Psychology, 19, 242-279.
45. Nazzi, T., & Gopnik, A. (2000). A shift in children's use of perceptual and causal cues to categorization. Developmental Science, 3(4), 389-396.
46. Neal, R. M. (1996). Bayesian learning for neural networks (No. 118). New York: Springer-Verlag.
47. Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39-57.
48. Novick, L. R., & Cheng, P. W. (2004). Assessing interactive causal inference. Psychological Review, 111, 455-485.
49. Pearl, J. (2000). Causality: Models, reasoning and inference. Cambridge, UK: Cambridge University Press.
50. Perfors, A. F., & Tenenbaum, J. B. (2009). Learning to learn categories. In N. A. Taatqer & H. Van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 136-141). Austin, TX: Cognitive Science Society.
51. Sakamoto, Y., & Love, B. C. (2004). Schematic influences on category learning and recognition memory. Journal of Experimental Psychology: General, 133(4), 534-553.
52. Schmidt, L. A., Kemp, C., & Tenenbaum, J. B. (2006). Nonsense and sensibility: Discovering unseen possibilities. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th annual conference of the Cognitive Science Society (pp. 744-749). Mahwah, NJ: Erlbaum.
53. Schulz, L. E., & Gopnik, A. (2004). Causal learning across domains. Developmental Psychology, 40(2), 162-176.
54. Schulz, L. E., Goodman, N. D., Tenenbaum, J. B., & Jenkins, A. (2008). Going beyond the evidence: abstract laws and preschoolers' responses to anomalous data. Cognition, 109(2), 211-223.
55. Shanks, D. R., & Darby, R. J. (1998). Feature- and rule-based generalization in human associative learning. Journal of Experimental Psychology: Animal Behavior Processes, 24(4), 405-415.
56. Smith, L. B., Jones, S. S., Landau, B., Gershkoff-Stowe, L., & Samuelson, L. (2002). Object name learning provides on-the-job training for attention. Psychological Science, 13(1), 13-19.
57. Sobel, D. M., Sommerville, J. A., Travers, L. V., Blumenthal, E. J., & Stoddard, E. (2009). The role of probability and intentionality in preschoolers' causal generalizations. Journal of Cognition and Development, 10(4), 262-284.
58. Sommers, F. (1963). Types and ontology. Philosophical Review, 72, 327-363.
59. Spelke, E. (1994). Initial knowledge: Six suggestions. Cognition, 50, 431-445.
60. Stevenson, H. W. (1972). Children's learning. New York: Appleton-Century-Crofts.
61. Steyvers, M., Tenenbaum, J. B., Wagenmakers, E. J., & Blum, B. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27, 453-489.
62. Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Science, 10(7), 309-318.
63. Thorndike, E. L., & Woodworth, R. S. (1901). The influence of improvement in one mental function upon the efficiency of other functions. Psychological Review, 8, 247-261.
64. Thrun, S. (1998). Lifelong learning algorithms. In S. Thrun & L. Pratt (Eds.), Learning to learn (pp. 181-209). Norwell, MA: Kluwer.
65. Thrun, S., & Pratt, L. (Eds.) (1998). Learning to learn. Norwell, MA: Kluwer.
66. Waldmann, M. R., & Hagmayer, Y. (2006). Categories and causality: The neglected direction. Cognitive Psychology, 53, 27-58.
67. Yerkes, R. M. (1943). Chimpanzees: A laboratory colony. New Haven, CT: Yale University Press.