The Northeastern PINE lab has compiled a huge collection of helpful resources for students and researchers at all career stages. Check it out!

Reading list by topic

Intuitive psychology and physics

Ullman, T. D., Spelke, E., Battaglia, P., & Tenenbaum, J. B. (2017). Mind Games: Game Engines as an Architecture for Intuitive Physics. Trends in Cognitive Sciences, 21(9), 649–665.

Jara-Ettinger, J., Gweon, H., Schulz, L. E., & Tenenbaum, J. B. (2016). The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology. Trends in Cognitive Sciences, 20(8), 589–604.

Baillargeon, R., Scott, R. M., & Bian, L. (2016). Psychological Reasoning in Infancy. Annual Review of Psychology, 67(1), 159–186.

Baker, C. L., Saxe, R., & Tenenbaum, J. B. (2009). Action understanding as inverse planning. Cognition, 113(3), 329–349.

Gergely, G., & Csibra, G. (2003). Teleological reasoning in infancy: The naïve theory of rational action. Trends in Cognitive Sciences, 7(7), 287–292.

Spelke, E. S., Breinlinger, K., Macomber, J., & Jacobson, K. (1992). Origins of knowledge. Psychological Review, 99(4), 605–632.

Innateness, learning, and experience

Gweon, H. (2021). Inferential social learning: cognitive foundations of human social learning and teaching. Trends in Cognitive Sciences, 25(10), 896–910.

Smith, L. B., Jayaraman, S., Clerkin, E., & Yu, C. (2018). The Developing Infant Creates a Curriculum for Statistical Learning. Trends in Cognitive Sciences, 22(4), 325–336.

Santolin, C., & Saffran, J. R. (2018). Constraints on Statistical Learning Across Species. Trends in Cognitive Sciences, 22(1), 52–63.

Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. The Behavioral and Brain Sciences, 40, e253.

Versace, E., & Vallortigara, G. (2015). Origins of Knowledge: Insights from Precocial Species. Frontiers in Behavioral Neuroscience, 9, 338.

Landau, B., Gleitman, L. R., & Landau, B. (2009). Language and Experience: Evidence from the Blind Child. Harvard University Press.

Spelke, E. S., & Kinzler, K. D. (2007). Core knowledge. Developmental Science, 10(1), 89–96.

Methods and tools

Freeman, M. A Visual Introduction to Hierarchical Models.

Kominsky, J. F. (2019). PyHab: Open-source real time infant gaze coding and stimulus presentation software. Infant Behavior & Development, 54, 114–119.

Sarnecka, B. W. (2019). The Writing Workshop: Write More, Write Better, Be Happier in Academia.

Scott, K., & Schulz, L. (2017). Lookit (Part 1): A New Online Platform for Developmental Research. Open Mind, 1(1), 4–14.

Goodman, N. D, Tenenbaum, J. B. & The ProbMods Contributors (2016). Probabilistic Models of Cognition (2nd ed.) Retrieved 2021-9-28 from

Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O’Reilly Media, Inc.

Green, P., & MacLeod, C. J. (2016). SIMR : an R package for power analysis of generalized linear mixed models by simulation. Methods in Ecology and Evolution, 7(4), 493–498.

de Leeuw, J. R. (2015). jsPsych: A JavaScript library for creating behavioral experiments in a web browser. Behavior Research Methods, 47(1), 1-12. doi:10.3758/s13428-014-0458-y.

Nieuwenhuis, R., Te Grotenhuis, H. F., & Pelzer, B. J. (2012). Influence. ME: tools for detecting influential data in mixed effects models. The R Journal, 4(2), 38–47.

Aslin, R. N. (2007). What’s in a look? Developmental Science, 10(1), 48–53.

General cognitive science

Gopnik, A., & Meltzoff, A. N. (1998). Words, thoughts, and theories (learning, development, and conceptual change). MIT Press.

Perner, J. (1993). Understanding the representational mind. MIT Press.

Fodor, J. (1983). Modularity of Mind. MIT Press.

Hoftstadter, D. (1979). Gödel, Escher, Bach: An Eternal Golden Braid. Basic Books.

Turing, A. M. (1950). Computing machinery and intelligence. Mind; a Quarterly Review of Psychology and Philosophy, 59(236), 433.