The following papers may not be directly related to the lab's research directions. However, they represent a wide variety of research methods in psychology research, which we find inspiring.

  • Uleman, J. S., Adil Saribay, S., & Gonzalez, C. M. (2008). Spontaneous inferences, implicit impressions, and implicit theories. Annual Review of Psychology.
  • Oeberst, A., & Imhoff, R (2023). Toward Parsimony in Bias Research: A Proposed Common Framework of Belief-Consistent Information Processing for a Set of Biases. Perspectives on Psychological Science.
  • Zaki, J. Cue integration: A common framework for social cognition and physical perception. Perspectives on Psychological Science, 8, no. 3 (2013): 296-312.
  • Brooks, J.A. & Freeman, J.B. (2018). Conceptual Knowledge Predicts the Representational Structure of Facial Emotion Perception. Nature Human Behaviour, 2, 581-591.
  • Lin, C., Keles, U., & Adolphs, R. (2021). Four dimensions characterize attributions from faces using a representative set of English trait words. Nature Communications, 12(1), 1-15.
  • Khosla, M., Ngo, G. H., Jamison, K., Kuceyeski, A., & Sabuncu, M. R., (2021). Cortical response to naturalistic stimuli is largely predictable with deep neural networks. Science Advances, 7(22), eabe7547.
  • Templeton, E. M., Chang, L. J., Reynolds, E. A., Cone LeBeaumont, M. D., & Wheatley, T. (2022). Fast response times signal social connection in conversation. Proceedings of the National Academy of Sciences, 119(4), e2116915119.
  • Dobs, K., Martinez, J., Kell, A. J., & Kanwisher, N. (2022). Brain-like functional specialization emerges spontaneously in deep neural networks. Science advances, 8(11), eabl8913.
  • Dobs, K., Isik, L., Pantazis, D., & Kanwisher, N. (2019). How face perception unfolds over time. Nature communications, 10(1), 1-10.
  • Eisenberg, I. W., Bissett, P. G., Zeynep Enkavi, A., Li, J., MacKinnon, D. P., Marsch, L. A., & Poldrack, R. A. (2019). Uncovering the structure of self-regulation through data-driven ontology discovery. Nature communications, 10(1), 1-13.
  • Heusser, A.C., Fitzpatrick, P.C. & Manning, J.R. (2021). Geometric models reveal behavioural and neural signatures of transforming experiences into memories. Nature Human Behaviour, 5, 905–919 .
  • Vlasceanu, M., & Amodio, D. M. (2022). Propagation of Societal Gender Inequality by Internet Search Algorithms. Proceedings of the National Academy of Sciences, 119(29), e2204529119.
  • Thornton, M. A., Rmus, M., & Tamir, D., (2020). Transition dynamics shape mental state concepts. Journal of Experimental Psychology: General.
  • Yarkoni, T. (2022). The generalizability crisis. Behavioral and Brain Sciences, 45.
  • Jolly, E., & Chang, L. J. (2019). The flatland fallacy: Moving beyond low–dimensional thinking. Topics in cognitive science, 11(2), 433-454.
  • Houlihan, S. D., Ong, D., Cusimano, M., & Saxe, R. (2022). Reasoning about the antecedents of emotions: Bayesian causal inference over an intuitive theory of mind. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 44, No. 44).


The following tutorials may be helpful to get you started with state-of-the-art computational approaches to social psychology research.

  • Open-source materials from the Summer Institutes in Computational Social Science [Tutorials]
  • Learning Python, data science, and machine learning with Google Colab [Tutorials]
  • Learning machine learning and deep learning with TensorFlow [Tutorials] and Hugging Face [Tutorials]
  • Chen Center for Data Science and Artificial Intelligence at Caltech [Videos] [Slides]
  • Learning statistics, computational modeling, and more with Neuromatch Academy [Tutorials]