Cognitive Science

Humans exhibit impressive (though not infallible) abilities to learn and reason about the world. My cognitive science research uses computational models to better understand human cognition. One early focus of my research was causal learning and reasoning, and particularly arguing that causal graphical models could help us understand many key aspects of causal cognition. I subsequently developed a novel cognitive architecture—described & defended in Unifying the Mind—that unifies multiple types of cognition by modeling them as distinct operations on a shared store of cognitive representations that are structured (approximately) as graphical models. This architecture now includes: causal cognition; concepts; decision-making; goals; and corresponding inferences about other people.

Selected publications:

• Cusimano, C., Zorrilla, N., Danks, D., & Lombrozo, T. (2024). Psychological freedom, rationality, and the naïve theory of reasoning. Journal of Experimental Psychology: General, 153(3), 837-863.

• Danks, D., & Davis, I. (2023). Causal inference in cognitive neuroscience. WIREs Cognitive Science, e1650.

• Danks, D., & Dinh, P. N. (2022). Causal perception and causal inference: An integrated account. In Advances in Experimental Philosophy of Causation.

• Cusimano, C., Zorrilla, N., Danks, D., & Lombrozo, T. (2021). Reason-based constraint in theory of mind. In Proceedings of the 43rd annual conference of the cognitive science society.

• Johnston, L., Hillman, N., & Danks, D. (2021). Individual differences in causal learning. In Proceedings of the 43rd annual conference of the cognitive science society.

• Danks, D. (2018). Privileged (default) causal cognition: A mathematical analysis. Frontiers in Psychology, 9, 498. doi:10.3389/fpsyg.2018.00498

• Danks, D. (2017). Singular causation. In M. R. Waldmann (Ed.), Oxford handbook of causal reasoning (pp. 201-215). Oxford: Oxford University Press.

• Wellen, S., & Danks, D. (2016). Adaptively rational learning. Minds & Machines, 26, 87-102.

• Wellen, S., & Danks, D. (2014). Learning with a purpose: The influence of goals. In P. Bello, M Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th annual conference of the cognitive science society (pp. 1766-1771). Austin, TX: Cognitive Science Society.

• Wellen, S., & Danks, D. (2012). Learning causal structure through local prediction-error learning. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th annual conference of the cognitive science society (pp. 2529-2534). Austin, TX: Cognitive Science Society.

• Danks, D. (2009). The psychology of causal perception and reasoning. In H. Beebee, C. Hitchcock, & P. Menzies (Eds.), Oxford handbook of causation (pp. 447-470). Oxford: Oxford University Press.

• Danks, D. (2007). Causal learning from observations and manipulations. In M. C. Lovett & P. Shah (Eds.), Thinking with data (pp. 359-388). New York: Lawrence Erlbaum Associates.

• Danks, D. (2007). Theory unification and graphical models in human categorization. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 173-189). Oxford: Oxford University Press.

• Nichols, W, & Danks, D. (2007). Decision making using learned causal structures. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th annual meeting of the cognitive science society (pp. 1343-1348). Austin, TX: Cognitive Science Society.

• Zhu, H., & Danks, D. (2007). Task influences on category learning. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th annual meeting of the cognitive science society (pp. 1677-1682). Austin, TX: Cognitive Science Society.

• Danks, D. (2006). (Not) learning a complex (but learnable) category. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th annual meeting of the cognitive science society (pp. 1186-1191). Mahwah, NJ: Lawrence Erlbaum Associates.

• Danks, D., & Schwartz, S. (2006). Effects of causal strength on learning from biased sequences. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th annual meeting of the cognitive science society (pp. 1180-1185). Mahwah, NJ: Lawrence Erlbaum Associates.

• Danks, D., & Schwartz, S. (2005). Causal learning from biased sequences. In B. G. Bara, L. Barsalou, & M. Bucciarelli (Eds.), Proceedings of the 27th annual meeting of the cognitive science society (pp. 542-547). Mahwah, NJ: Lawrence Erlbaum Associates.

• Danks, D. (2004). Constraint-based human causal learning. In M. Lovett, C. Schunn, C. Lebiere, & P. Munro (Eds.), Proceedings of the 6th international conference on cognitive modeling (ICCM-2004) (pp. 342-343). Mahwah, NJ: Lawrence Erlbaum Associates.

• Danks, D. (2004). Psychological theories of categorization as probabilistic models. Technical report CMU-PHIL-157. July 15, 2004.

• 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(1), 3-32.

• Danks, D. (2003). Equilibria of the Rescorla-Wagner model. Journal of Mathematical Psychology, 47, 109-121.

• 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. 67-74). Cambridge, MA: MIT Press.

• Kushnir, T., Gopnik, A., Schulz, L., & Danks, D. (2003). Inferring hidden causes. In R. Alterman & D. Kirsh (Eds.), Proceedings of the 25th annual meeting of the cognitive science society (pp. 699-703). Boston: Cognitive Science Society.