Patricia Cheng

Professor
Ph.D.: University of Michigan, Ann Arbor
Primary Area: Cognitive Psychology
Address: 7518 Pritzker Hall
Phone: (310) 825-8174
Email: cheng@psych.ucla.edu

Research and Teaching Interests:

My research interests center on the origin of mental representations. How do we humans come to understand the world and its workings, and our place in it? I divide this issue into two related 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?  And what comes to be represented as “things” that cause other “things”? Thus, the second issue is category formation. Objects and events in the world can be partitioned in infinitely many ways (e.g., objects that move in the wind, three-legged objects, sequences of air compression and rarifaction that bring pleasure, repeated actions, etc). Out of the infinitely many logically possible categories, only a minuscule fraction are commonly used (e.g., grass, polar bear, kite, melody, phone, smoking). Why do people have the folk categories that they have, but not the countless others that they don’t? The two issues are intertwined in that folk empirical categories are all causal (have consequences for us) in some way.

I primarily develop theories from a computational perspective and evaluate their implications, in particular, those for rationality. These include rational causal statistics and rational actions to address existential threats to humanity. I evaluate the theories and implications by conducting cognitive experiments on normal humans, mostly adults but preschoolers as well. I also consider other approaches to the underlying issues, including philosophical, artificial intelligence, anthropological, comparative, and neuroscience approaches.

Please visit my lab reasoninglab.psych.ucla.edu

Representative Publications:

• Lee, J., Wong, E.F., Cheng, P.W. (2023). Promoting Climate Actions: A Cognitive-Constraints Approach. Cognitive Psychology, 143, 10156.
• Bye, J.K., Chuang, P.J., Cheng, P.W. (2023). How do humans want causes to combine their effects? the role of causal invariance for generalizable causal knowledge. Cognition, 23, 105303.
• Cheng, P.W., Sandhofer, C.M., Liljeholm, M. (2022). Analytic causal knowledge for constructing useable empirical causal knowledge: Two experiments on preschoolers. Cognitive Science, 46, 13137.
• Park, J., McGillivray, S., Bye, J.K. & Cheng, P.W. (2022). Causal invariance as a tacit aspiration: Analytic knowledge of invariance functions. Cognitive Psychology, 132, 101432.
• Ichien, N. & Cheng, P.W. (2022).Revisiting Hume in the 21st century: The possibility of generalizable causal beliefs given inherently unobservable causal relations.  In A. Wiegmann & P. Willemsen (Eds.), Advances in Experimental Philosophy of Causation.  London, UK: Bloomsbury Press.
• Cheng, P.W. & Lu, H. (2017). Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power. In M.R. Waldmann (Ed). The Oxford Handbook of Causal Reasoning (pp. 65-84). Oxford, England: Oxford University Press.
• 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., Liljeholm, M. & Sandhofer, C. (2013).  Logical consistency and objectivity in causal learning.  In Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 2034-2039).  Austin, TX: Cognitive Science Society.
• 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.