Here are some papers by others that I really like.

On neuroimaging (analysis)

  • Naselaris, T., Kay, K. N., Nishimoto, S., & Gallant, J. L. (2011). Encoding and decoding in fMRI. Neuroimage, 56(2), 400-410.
  • Hebart, M. N., & Baker, C. I. (2018). Deconstructing multivariate decoding for the study of brain function. Neuroimage, 180, 4-18.
  • Kay, K. N. (2018). Principles for models of neural information processing. NeuroImage, 180, 101-109.
  • Ritchie, J. B., Kaplan, D. M., & Klein, C. (2019). Decoding the brain: Neural representation and the limits of multivariate pattern analysis in cognitive neuroscience. The British journal for the philosophy of science, 70(2), 581-607.

On machine learning (& psychology)

  • Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100-1122.
  • Rocca, R., & Yarkoni, T. (2020). Putting psychology to the test: Rethinking model evaluation through benchmarking and prediction.
  • Geirhos, R., Jacobsen, J. H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., & Wichmann, F. A. (2020). Shortcut learning in deep neural networks. Nature Machine Intelligence, 2(11), 665-673.
  • Cichy, R. M., & Kaiser, D. (2019). Deep neural networks as scientific models. Trends in cognitive sciences, 23(4), 305-317.

On face & facial expression perception

  • Jack, R. E., & Schyns, P. G. (2017). Toward a social psychophysics of face communication. Annual review of psychology, 68, 269-297.

On meta-science

  • Kriegeskorte, N., & Douglas, P. K. (2018). Cognitive computational neuroscience. Nature neuroscience, 21(9), 1148-1160.
  • Yarkoni, T. (2019). The generalizability crisis. Preprint]. PsyArXiv.