Research and Teaching Interests
My research interests center on the origin of mental representations. I divide this issue into two parts. The first is causal induction. How do people come to know that one thing causes another? Some sequences of events are merely associated; others are causal. How do people tell such sequences apart?
The second issue is category formation. Objects in the world can be partitioned in an indefinitely large number of ways (e.g., objects that move in the wind, objects that have legs, objects to be avoided, and so on). Out of these, only a tiny fraction are commonly used (e.g., vehicles, bears, musical instruments). Why do people have the folk categories that they have, rather than the vast array of logically possible categories that they do not have?
The link between the two issues is that most categories are causal. I primarily develop theories from a computational perspective, and conduct cognitive experiments on normal adult humans. But I also consider a wide range of approaches to the underlying issues, including developmental, artificial intelligence, philosophical, and neural approaches. I am also interested in analogous issues involving nonhuman species.
Please visit my lab
- Carroll, C.D., Cheng, P.W., & Lu, H. (2013). Inferential dependencies in causal inference: A comparison of belief-distribution and associative approaches. Journal of Experimental Psychology: General, 142, 845-863.
- Cheng, P. W. & Buehner, M. (2012). Causal learning. In K. J. Holyoak & R. G. Morrison (Eds.), Oxford Handbook of Thinking and Reasoning (pp. 210- 233). New York: Oxford University Press.
- Holyoak, K.J. & Cheng, P.W. (2011). Causal learning and inference as a rational process: The new synthesis. Annual Review of Psychology, 62: 23.1-23.29.
- Carroll, C.D., & Cheng, P.W. (2010). The induction of hidden causes: Causal mediation and violations of independent causal influence. In S. Ohlsson & R. Catrabone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 913-918). Portland, OR: Cognitive Science Society.
- Lu, H., Yuille, A., Liljeholm, M., Cheng, P.W., & Holyoak, K.J. (2008). Bayesian generic priors for causal learning. Psychological Review, 115, 955-984.
- Liljeholm, M. & Cheng, P.W. (2007). When is a cause the “same”? Coherent generalization across contexts. Psychological Science, 18, 1014-1021.
- Cheng, P.W., Novick, L.R., Liljeholm, M. & Ford, C. (2007). In M. O’Rourke (Ed.), Topics in Contemporary Philosophy (Volume 4, pp. 1 – 32): Explanation and Causation. Cambridge, MA: MIT Press.
- Novick, L.R., & Cheng, P.W. (2004). Assessing interactive causal influence. Psychological Review, 111, 455-485.
- Buehner, M., Cheng, P.W., Clifford, D. (2003.) From Covariation to Causation: A Test of the Assumption of Causal Power. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 1119-1140.
- Lien, Y., & Cheng, P.W. (2000). Distinguishing genuine from spurious causes: a coherence hypothesis. Cognitive Psychology, 40, 87-137.
- Cheng, P.W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367-405.
- Cheng, P.W. (1993). Separating causal laws from casual facts: Pressing the limits of statistical relevance. In D.L. Medin (Ed.), The psychology of learning and motivation, vol. 30 (pp. 215-264). New York: Academic Press.
- Cheng, P.W., & Novick, L.R. (1992). Covariation in natural causal induction. Psychological Review, 99, 365-382.
- Cheng, P.W., & Novick, L.R. (1991). Causes versus enabling conditions. Cognition, 40, 83-120.