Estimating human priors on causal strength

Expanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011

Volumn , Issue , 2011, Pages 1709-1714

  Yeung, Saiwing ^a   Griffiths, Thomas L ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Bayesian inference; Causal learning; Iterated learning; Probabilistic judgment

Indexed keywords

INFERENCE ENGINES;

BAYESIAN INFERENCE; BAYESIAN MODELLING; CAUSAL LEARNING; CAUSAL RELATIONSHIPS; EXPERIMENTAL METHODS; HUMAN JUDGMENTS; ITERATED LEARNING; PROBABILISTIC JUDGMENTS;

BAYESIAN NETWORKS;

EID: 85042781930     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Buehner, M., & Cheng, P. (1997). Causal induction: The power PC theory versus the Rescorla-Wagner model. In Proceedings of the nineteenth annual conference of the cognitive science society: August 7-10, 1997, stanford university (p. 55).
2. Cheng, P. (1997). From covariation to causation: A causal power theory. Psychological Review, 104(2), 367–405.
3. Griffiths, T., Christian, B., & Kalish, M. (2008). Using category structures to test iterated learning as a method for identifying inductive biases. Cognitive Science, 32(1), 68–107.
4. Griffiths, T., & Kalish, M. (2007). Language evolution by iterated learning with Bayesian agents. Cognitive Science: A Multidisciplinary Journal, 31(3), 441–480.
5. Griffiths, T., & Tenenbaum, J. (2005). Structure and strength in causal induction. Cognitive Psychology, 51(4), 334–384.
6. Griffiths, T., & Tenenbaum, J. (2009). Theory-based causal induction. Psychological Review, 116(4), 661–716.
7. Hume, D. (1739/2004). An Enquiry Concerning Human Understanding. Dover Publications.
8. Kalish, M., Griffiths, T., & Lewandowsky, S. (2007). Iterated learning: Intergenerational knowledge transmission reveals inductive biases. Psychonomic Bulletin & Review, 14(2), 288.
9. Kirby, S. (2001). Spontaneous evolution of linguistic structure: An iterated learning model of the emergence of regularity and irregularity. IEEE Journal of Evolutionary Computation, 5, 102-110.
10. Lober, K., & Shanks, D. (2000). Is causal induction based on causal power? Critique of Cheng (1997). Psychological Review, 107(1), 195–212.
11. Lombrozo, T. (2007). Simplicity and probability in causal explanation. Cognitive Psychology, 55(3), 232–257.
12. Lu, H., Yuille, A., Liljeholm, M., Cheng, P., & Holyoak, K. (2008). Bayesian generic priors for causal learning. Psychological Review, 115(4), 955–984.
13. Novick, L., & Cheng, P. (2004). Assessing Interactive Causal Influence. Psychological Review, 111(2), 455–485.
14. Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge University Press.
15. Schulz, L. E., & Sommerville, J. (2006). God does not play dice: Causal determinism and children’s inferences about unobserved causes. Child Development, 77, 427-442.
16. Spirtes, P., Glymour, C., & Scheines, R. (2001). Causation, prediction, and search. The MIT Press.
17. Venables, W., & Ripley, B. (2002). Modern applied statistics with S. Springer-Verlag.
18. Ward, W., & Jenkins, H. (1965). The display of information and the judgment of contingency. Canadian Journal of Psychology, 19(3), 231–241.