Resources

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

Funding opportunities

I do not allow people, at any career stage, to volunteer in my lab, because I believe that it puts people who cannot afford to do so at a disadvantage. For a list of undergraduate, graduate and postdoctoral funding opportunities, both internal to and external to Johns Hopkins, please refer to this page. All lab members should expect to apply for funding as part of their training– it will look great on your resume or CV, prepare you for writing proposals in the future, and alleviate cost on the lab’s end.

Reading list by topic

Being productive and happy in academia

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.

Methods and tools

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

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

  • 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 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/.

  • 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.

  • 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.