Resources

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

Courses

All materials from courses taught by Prof. Liu will be shared openly here. Check back soon!

Reading list by topic

Being productive and happy in academia

• Sarnecka, B. W. (2019). The writing workshop: Write more, write better, be happier in academia. https://osf.io/n8pc3/.

• Silvia, P. J. (2007). How to write a lot: A practical guide to productive academic writing. American Psychological Association.

• Growing up in Science, events and resources: https://www.cns.nyu.edu/events/growingupinscience/resources.html

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.

• Isik, L., Koldewyn, K., Beeler, D., & Kanwisher, N. (2017). Perceiving social interactions in the posterior superior temporal sulcus. Proceedings of the National Academy of Sciences, 114(43), E9145-E9152.

• Fischer, J., Mikhael, J. G., Tenenbaum, J. B., & Kanwisher, N. (2016). Functional neuroanatomy of intuitive physical inference. Proceedings of the National Academy of Sciences, 113(34), E5072-E5081.

• 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. Koster-Hale, J., & Saxe, R. (2013). Theory of mind: a neural prediction problem. Neuron, 79(5), 836-848.

• Koster-Hale, J., & Saxe, R. (2013). Theory of mind: a neural prediction problem. Neuron, 79(5), 836-848.

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

• Saxe, R., Carey, S., & Kanwisher, N. (2004). Understanding other minds: Linking developmental psychology and functional neuroimaging. Annu. Rev. Psychol., 55, 87-124.

• 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.

• Gopnik, A., & Wellman, H. M. (1992). Why the child’s theory of mind really is a theory. Mind & Language, 7(1-2), 145–171.

• Heider, F., & Simmel, M. (1944). An experimental study of social behavior. The American Journal of Psychology, 57(2), 243–259.

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.

Statistical tools and resources

• Freeman, M. A visual introduction to hierarchical models. http://mfviz.com/hierarchical-models/.

• Wilber. J. The permutation test: a visual explanation of statistical testing. https://www.jwilber.me/permutationtest/ 

• Lüdecke, Patil, Ben-Shachar, Wiernik, Bacher, Thériault, & Makowski (2022). easystats: Framework for Easy Statistical Modeling, Visualization, and Reporting. CRAN. Available from https://easystats.github.io/easystats/

• Goodman, N. D, Tenenbaum, J. B. & The ProbMods Contributors (2016). Probabilistic models of cognition (2nd ed.) https://probmods.org/.

• Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O’Reilly Media, Inc. https://r4ds.had.co.nz/.

• 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. https://www.jspsych.org/.

• 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.

fMRI methods, argument, and logic

• Francken, J. C., Slors, M., & Craver, C. F. (2022). Cognitive ontology and the search for neural mechanisms: three foundational problems. Synthese, 200(5), 378.

• Nili, H., Wingfield, C., Walther, A., Su, L., Marslen-Wilson, W., & Kriegeskorte, N. (2014). A toolbox for representational similarity analysis. PLoS computational biology, 10(4), e1003553.

• Vul, E., & Kanwisher, N. (2010). Begging the question: The non-independence error in fMRI data analysis. Foundational Issues in Human Brain Mapping, 71–91.

• Saxe, R., Brett, M., & Kanwisher, N. (2006). Divide and conquer: a defense of functional localizers. Neuroimage, 30(4), 1088–1096.

• Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences, 10(2), 59–63.

Infant looking time methods, argument, and logic

• Stahl, A. E., & Kibbe, M. M. (2022). Great expectations: The construct validity of the violation‐of‐expectation method for studying infant cognition. Infant and Child Development. https://doi.org/10.1002/icd.2359

• Kominsky, J. F., Lucca, K., Thomas, A. J., Frank, M. C., & Hamlin, J. K. (2022). Simplicity and validity in infant research. Cognitive Development, 63, 101213.

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

• Frank, M. C., Bergelson, E., Bergmann, C., Cristia, A., Floccia, C., Gervain, J., Hamlin, J. K., Hannon, E. E., Kline, M., Levelt, C., Lew-Williams, C., Nazzi, T., Panneton, R., Rabagliati, H., Soderstrom, M., Sullivan, J., Waxman, S., & Yurovsky, D. (2017). A collaborative approach to infant research: Promoting reproducibility, best practices, and theory-building. Infancy, 22(4), 421–435.

• Scott, K., & Schulz, L. (2017). Lookit (Part 1): A new online platform for developmental research. Open Mind, 1(1), 4–14.

• 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.