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  • Ahn, W., & Kalish, C. W. (2000). The role of mechanism beliefs in causal reasoning. In F. C. Keil & R. A. Wilson (Eds.), Explanation and cognition (pp. 199226). Cambridge, MA: MIT Press.
  • Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Erlbaum.
  • Bonawitz, E. B., & Griffiths, T. L. (2010). Deconfounding hypothesis generation and evaluation in Bayesian models. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pp. 22602265). Austin, TX: Cognitive Science Society.
  • Bullock, M., Gelman, R., & Baillargeon, R. (1982). The development of causal reasoning. In W. J. Friedman (Ed.), The developmental psychology of time (pp. 209254). New York: Academic Press.
  • Carey, S. (2009). The origin of concepts. Oxford, England: Oxford University Press.
  • Catena, A., Maldonado, A., & Cándido, A. (1998). The effect of the frequency of judgment and the type of trial on covariation learning. Journal of Experimental Psychology: Human Perception and Performance, 24, 481495.
  • Chapman, G. B. (1991). Trial order affects cue interaction in contingency judgments. Journal of Experimental Psychology: Learning, Memory, & Cognition, 17, 837854.
  • Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367405.
  • Cheng, P. W. (2000). Causal reasoning. In R. Wilson & F. Keil (Eds.), The MIT encyclopedia of cognitive sciences (pp. 106108). Cambridge, MA: Bradford, MIT Press.
  • Cole, R. P., Barnet, R. C., & Miller, R. R. (1995). Effect of relative stimulus validity: Learning or performance deficit? Journal of Experimental Psychology: Animal Behavior Processes, 21, 293303.
  • Danks, D. (2003). Equilibria of the Rescorla-Wagner model. Journal of Mathematical Psychology, 47, 109121.
  • Danks, D., Griffiths, T. L., & Tenenbaum, J. B. (2003). Dynamical causal learning. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems 15 (pp. 3542). Cambridge, MA: MIT Press.
  • Dickinson, A., & Burke, J. (1996). Within-compound associations mediate the retrospective revaluation of causality judgements. Quarterly Journal of Experimental Psychology, 49B, 6080.
  • Dickinson, A., & Shanks, D. R. (1995). Instrumental action and causal representation. In D. Sperber, D. Premack, & A. J. Premack (Eds.), Causal cognition: A multidisciplinary debate (pp. 525). Oxford, England: Oxford University Press.
  • Fernbach, P. M., & Sloman, S. A. (2009). Causal learning with local computations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 678693.
  • Friedman, N., & Koller, D. (2003). Being Bayesian about Bayesian network structure: A Bayesian approach to structure discovery in Bayesian networks. Machine Learning, 50, 95125.
  • Glymour, C. (1998). Learning causes: Psychological explanations of causal explanation. Minds and Machines, 8, 3960.
  • Glymour, C. (2001). The mind’s arrows: Bayes nets and graphical causal models in psychology. Cambridge, MA: MIT Press.
  • Glymour, C. (2003). Learning, prediction, and causal Bayes nets. Trends in Cognitive Science, 7, 4348.
  • Glymour, C., & Cheng, P. W. (1998). Causal mechanism and probability: A normative approach. In Y. Oaksford & V. Chater (Eds.), Rational models of cognition (pp. 295313). Oxford, England: Oxford University Press.
  • Goldvarg, E., & Johnson-Laird, P. N. (2001). Naive causality: A mental model theory of causal meaning and reasoning. Cognitive Science, 25, 565610.
  • 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, 130.
  • Gopnik, A., & Schulz, L. E. (2007). Causal learning: Psychology, philosophy, computation. New York: Oxford University Press.
  • Gopnik, A., & Sobel, D. M. (2000). Detecting blickets: How young children use information about causal powers in categorization and induction. Child Development, 71, 12051222.
  • Gopnik, A., Sobel, D. M., Schulz, L., & 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, 620629.
  • Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. New York: Wiley.
  • Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51, 285386.
  • 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. 323345). Oxford, England: Oxford University Press.
  • Griffiths, T. L., & Tenenbaum, J. B. (2009). Theory-based causal induction. Psychological Review, 116, 661716.
  • Hogarth, R. M., & Einhorn, H. J. (1992). Order effects in belief updating: The belief-adjustment model. Cognitive Psychology, 24, 155.
  • Kamin, L. J. (1968). “Attention-like” processes in classical conditioning. In M.R. Jones (Eds.), Miami Symposium on the Prediction of Behavior, 1967: Aversive Stimulation (pp. 931). Coral Gables, FL: University of Miami Press.
  • Kruschke, J. K., & Blair, N. J. (2000). Blocking and backward blocking involve learned inattention. Psychonomic Bulletin & Review, 7, 636645.
  • Kushnir, T., & Gopnik, A. (2007). Conditional probability versus spatial contiguity in causal learning: Preschoolers use new contingency evidence to overcome prior spatial assumptions. Developmental Psychology, 43, 186196.
  • Larkin, M. J. W., Aitken, M. R. F., & Dickinson, A. (1998). Retrospective revaluation of causal judgments under positive and negative contingencies. Journal of Experimental Psychology: Learning, Memory, & Cognition, 24, 13311352.
  • Leslie, A. M., & Keeble, S. (1987). Do six-month-old infants perceive causality? Cognition, 25, 265288.
  • Lovibond, P. F., Been, S., Mitchell, C. J., Bouton, M. E., & Frohardt, R. (2003). Forward and backward blocking of causal judgment is enhanced by additivity of effect magnitude. Memory & Cognition, 31, 133142.
  • Lu, H., Yuille, A., Liljeholm, M., Cheng, P. W., & Holyoak, K. J. (2006). Modeling causal learning using Bayesian generic priors on generative and preventive powers. In R. Sun & N. Miyake (Eds.), Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society (pp. 519524). Mahwah, NJ: Erlbaum.
  • Lu, H., Yuille, A., Liljeholm, M., Cheng, P. W., & Holyoak, K. J. (2007). Bayesian models of judgments of causal strength: A comparison. In D. S. McNammara & G. Trafton (Eds.), Proceedings of the Twenty-ninth Annual Conference of the Cognitive Science Society (pp. 12411246). Mahwah, NJ: Erlbaum.
  • Lu, H., Yuille, A., Liljeholm, M., Cheng, P. W., & Holyoak, K. J. (2008). Bayesian generic priors for causal learning. Psychological Review, 115, 955984.
  • Marr, D. (1982). Vision. San Francisco, CA: W. H. Freeman.
  • McCormack, T., Butterfill, S., Hoerl, C., & Burns, P. (2009). Cue competition effects and young children’s causal and counterfactual inferences. Developmental Psychology, 45, 15631575.
  • Miller, R. R., & Matute, H. (1996). Biological significance in forward and backward blocking: Resolution of a discrepancy between animal conditioning and human causal judgment. Journal of Experimental Psychology: General, 125, 370386.
  • Mitchell, C. J., Killedar, A., & Lovibond, P. F. (2005). Inference-based retrospective revaluation in human causal judgments requires knowledge of within-compound relationships. Journal of Experimental Psychology: Animal Behavior Processes, 31, 418424.
  • Newsome, G. L. (2003). The debate between current versions of covariation and mechanism approaches to causal inference. Philosophical Psychology, 16, 87107.
  • Novick, L. R., & Cheng, P. W. (2004). Assessing interactive causal influence. Psychological Review, 111, 455485.
  • Pearce, J. M. (1987). A model for stimulus generalization in Pavlovian conditioning. Psychology Review, 94, 6173.
  • Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Mateo, CA: Morgan Kaufman.
  • Pearl, J. (2000). Causality. New York: Oxford University Press.
  • Pinker, S. (1979). Formal models of language learning. Cognition, 7, 217282.
  • Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II: Current theory and research (pp. 6499). New York: Appleton-Century-Crofts.
  • Rumelhart, D. E., McClelland, J. L., & The PDP Research Group (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Cambridge, MA: MIT Press.
  • Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010). Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review, 117, 11441167.
  • Schulz, L. E., Bonawitz, E. B., & Griffiths, T. L. (2007). Can being scared make your tummy ache? Naive theories, ambiguous evidence and preschoolers’ causal inferences. Developmental Psychology, 43, 11241139.
  • Seiver, E., Gopnik, A., Lucas, C., & Goodman, N. D. (2007). Causal inference and the origins of social cognition—Preschool children use covariation to make trait attributions. Poster presented at the meeting of the Society for Research in Child Development, Boston, March 29 – April 1.
  • Shanks, D. R. (1985). Forward and backward blocking in human contingency judgment. Quarterly Journal of Experimental Psychology, 37B, 121.
  • Shanks, D. R. (1995). Is human learning rational? Quarterly Journal of Experimental Psychology: Human Experimental Psychology, 48, 257279.
  • Shultz, T. R. (1982). Rules of causal attribution. Monographs of the Society for Research in Child Development, 47(1). Serial No. 194.
  • Sobel, D. M., & Munro, S. A. (2009). Domain generality and specificity in children’s causal inferences about ambiguous data. Developmental Psychology, 45, 511524.
  • Sobel, D. M., Tenenbaum, J. B., & Gopnik, A. (2004). Children’s causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers. Cognitive Science, 28, 303333.
  • Spirtes, P., Glymour, C., & Scheines, R. (2001). Causation, prediction, and search (Springer lecture notes in statistics, 2nd Rev. ed.). Cambridge, MA: MIT Press.
  • Tenenbaum, J. B., & Griffiths, T. L. (2001). Structure learning in human causal induction. In T. Leen, T. Dietterich, & V. Tresp (Eds.), Advances in neural information processing systems 13 (pp. 5965). Cambridge, MA: MIT Press.
  • Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Science, 10, 309318.
  • Tenenbaum, J. B., Griffiths, T. L., Niyogi, S. (2007). Intuitive theories as grammars for causal inference. In A. Gopnik, & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation. Oxford, England: Oxford University Press.
  • Van Hamme, L. J., & Wasserman, E. A. (1994). Cue competition in causality judgments: The role of nonpresentation of compound stimulus elements. Learning and Motivation, 25, 127151.
  • Ward, W. C., & Jenkins, H. M. (1965). The display of information and the judgment of contingency. Canadian Journal of Psychology, 19, 231241.
  • Wasserman, E. A., & Berglan, L. R. (1998). Backward blocking and recovery from overshadowing in human causal judgment: The role of within-compound associations. Quarterly Journal of Experimental Psychology: Comparative & Physiological Psychology, 51, 121138.
  • Wasserman, E. A., Elek, S. M., Chatlosh, D. L., & Baker, A. G. (1993). Rating causal relations: Role of probability in judgments of response-outcome contingency. Journal of Experimental Psychology: Learning, Memory, and Cognition, 19, 174188.
  • Wolff, P. (2007). Representing causation. Journal of Experimental Psychology: General, 136, 82111.
  • Xu, F., & Tenenbaum, J. B. (2007a). Sensitivity to sampling Bayesian word learning. Developmental Science, 10, 288297.
  • Xu, F., & Tenenbaum, J. B. (2007b). Word learning as Bayesian inference. Psychological Review, 114, 245272.
  • Yuille, A., & Kersten, D. (2006). Vision as Bayesian inference: Analysis by synthesis? Trends in Cognitive Science, 10, 301308.