CCS: Reading List

Course Textbook

• Farrell, Simon and Stephan Lewandowsky (2018). Computational Modeling of Cognition and Behavior. Cambridge University Press, Cambridge.
• The GitHub repo by Farrell and Lewandowsky, in case you want to play with the models in the book

Useful Background

• Sharon Goldwater's Maths Tutorials
• CCS R Cheat Sheet [note that this is LLM generated, use with caution]

Obligatory Reading

• Tenenbaum, Joshua B. (2000). Rules and Similarity in Concept Learning. Advances in Neural Information Processing Systems 12 (NeurIPS).
• Battleday, Ruairidh M., Joshua C. Peterson and Thomas L. Griffiths (2020). Capturing human categorization of natural images by combining deep networks and cognitive models. Nature Communications 11, 5418.
• Baetu, Irina and Andy G. Baker (2016). Human Learning About Causation. In Robin A. Murphy and Robert C. Honey, eds., The Wiley Handbook on the Cognitive Neuroscience of Learning, 380-407.
• Joshua B. Tenenbaum, Thomas L. Griffiths (2001). Structure Learning in Human Causal Induction. Advances in Neural Information Processing Systems 13 (NeurIPS).
• Steyvers, Mark, Joshua B. Tenenbaum, Eric-Jan Wagenmakers, and Ben Blum (2003). Inferring Causal Networks from Observations and Interventions. Cognitive Science, 27: 453-489.
• Frank, Michael C., Noah D. Goodman, and Joshua B. Tenenbaum (2009). Using Speakers' Referential Intentions to Model Early Cross-Situational Word Learning. Psychological Science, 20(5), 578-585.

Optional Reading

• Wasserman, Edward A. and Leyre Castro (2022).  The Rescorla-Wagner Model: The Culmination of Hume's Theory of Causation. Journal of Experimental Psychology: Animal Behavior Processes 48(4), 315-320.
• Charniak, Eugene (1991). Bayesian Networks Without Tears. AI Magazine 12(4), 50-63.
• Pearl, Judea and Dana Mackenzie (2018). The Book of Why: The New Science of Cause and Effect. Allen Lane, London.