nips nips2011 nips2011-15 nips2011-15-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Thomas L. Griffiths, Michael James
Abstract: Rational models of causal induction have been successful in accounting for people’s judgments about causal relationships. However, these models have focused on explaining inferences from discrete data of the kind that can be summarized in a 2× 2 contingency table. This severely limits the scope of these models, since the world often provides non-binary data. We develop a new rational model of causal induction using continuous dimensions, which aims to diminish the gap between empirical and theoretical approaches and real-world causal induction. This model successfully predicts human judgments from previous studies better than models of discrete causal inference, and outperforms several other plausible models of causal induction with continuous causes in accounting for people’s inferences in a new experiment. 1
[1] J. R. Anderson. The adaptive character of thought. Erlbaum, Hillsdale, NJ, 1990.
[2] D. Marr. Vision. W. H. Freeman, San Francisco, CA, 1982.
[3] P. Cheng. From covariation to causation: A causal power theory. Psychological Review, 104:367–405, 1997.
[4] T. L. Griffiths and J. B. Tenenbaum. Structure and strength in causal induction. Cognitive Psychology, 51:354–384, 2005.
[5] T. L. Griffiths and J. B. Tenenbaum. Theory-based causal induction. Psychological review, 116(4):661, 2009.
[6] H. Lu, A. L. Yuille, M. Liljeholm, P. W. Cheng, and K. J. Holyoak. Bayesian generic priors for causal learning. Psychological review, 115(4):955, 2008.
[7] J. R. Busemeyer, E. Byun, E. L. DeLosh, and M. A. McDaniel. Learning functional relations based on experience with input-output pairs by humans and artificial neural networks. In K. Lamberts and D. Shanks, editors, Concepts and Categories, pages 405–437. MIT Press, Cambridge, 1997.
[8] T. L. Griffiths, C. G. Lucas, J. J. Williams, and M. L. Kalish. Modeling human function learning with gaussian processes. In Daphne Koller, Yoshua Bengio, Dale Schuurmans, and L´ on e Bottou, editors, Advances in Neural Information Processing Systems, volume 21, Cambridge, MA, 2009. MIT Press.
[9] J. K. Marsh and W. Ahn. Spontaneous assimilation of continuous values and temporal information in causal induction. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(2):334, 2009.
[10] P. W. Cheng and L. R. Novick. A probabilistic contrast model of causal induction. Journal of Personality and Social Psychology, 58:545–567, 1990.
[11] P. A. White. Making causal judgments from the proportion of confirming instances: the pCI rule. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29:710–727, 2003.
[12] J. Pearl. Probabilistic reasoning in intelligent systems. Morgan Kaufmann, San Francisco, CA, 1988.
[13] M. R. Waldmann and Y. Hagmayer. Categories and causality: The neglected direction. Cognitive Psychology, 53(1):27–58, 2006.
[14] M. R. Waldmann, K. J. Holyoak, and A. Fratianne. Causal models and the acquisition of category structure. Journal of Experimental Psychology: General, 124:181–206, 1995.
[15] C. I. Bliss. The calculation of the dosage-mortality curve. 22(1):134–167, 1935. 9 Annals of Applied Biology,