Resources

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

iCatcher

iCatcher is an open-access automated tool for annotating infant looking behavior from video. So far, it handles annotations of preferential looking (left, right, away) and annotations of duration looking (on vs off). You can choose one of three trained networks to run your data on, including a webcam dataset collected on Lookit.

You can access the tool at: https://github.com/icatcherplus/icatcher_plus. If you have any questions or technical issues, please open an issue here.

For more information, see the following references:

Erel, Y., Potter, C. E., Jaffe-Dax, S., Lew-Williams, C., & Bermano, A. H. (2022). iCatcher: A neural network approach for automated coding of young children’s eye movements. Infancy, 27(4), 765–779. https://doi.org/10.1111/infa.12468
Erel, Y., Shannon, K. A., Chu, J., Scott, K., Struhl, M. K., Cao, P., Tan, X., Hart, P., Raz, G., Piccolo, S., Mei, C., Potter, C., Jaffe-Dax, S., Lew-Williams, C., Tenenbaum, J., Fairchild, K., Bermano, A., & Liu, S. (2023). ICatcher+: Robust and automated annotation of infants’ and young children's gaze behavior from videos collected in laboratory, field, and online studies. Advances in Methods and Practices in Psychological Science, 6(2). https://doi.org/10.1177/25152459221147250

Courses

Feel free to use course materials for other purposes, as long as you give proper attribution. Course materials are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Methods for Studying Infant Minds (AS.200.328, Fall 2023)

Reading list by topic (click to expand)

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.
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

Spelke, E. S. (2023). Précis of What Babies Know. The Behavioral and Brain Sciences, 1–36.
- - And the full book: Spelke, E. S. (2022). What Babies Know: Core Knowledge and Composition Volume 1. Oxford University Press.
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.
Bedny, M. (2017). Evidence from Blindness for a Cognitively Pluripotent Cortex. Trends in Cognitive Sciences, 21(9), 637–648.
Versace, E., & Vallortigara, G. (2015). Origins of knowledge: Insights from precocial species. Frontiers in Behavioral Neuroscience, 9, 338.
Carey, S. (2011). Précis of The Origin of Concepts. The Behavioral and Brain Sciences, 34(3), 113–124.
- - And the full book: Carey, S. (2011). The origin of concepts. Oxford University Press.
Landau, B., Gleitman, L. R., & Landau, B. (2009). Language and experience: Evidence from the blind child. Harvard University Press.

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.