Manoj Kumar, et al. (2021). BrainIAK: The brain imaging analysis kit. Aperture Neuro. [full-text link]
Cai, M. B., Shvartsman, M., Wu, A., Zhang, H., & Zhu, X. (2020). Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis. Neuropsychologia, 107500.[link]
Ellis, C., Baldassano, C., Schapiro, A. C., Cai, M. B., & Cohen, J. D. (2020). Facilitating open-science with realistic fMRI simulation: validation and application. PeerJ, 8, e8564 [link]
Cai, M. B., Schuck, N. W., Pillow, J. W., & Niv, Y. (2019). Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias. PLoS computational biology, 15(5), e1006299. [full-text link] [example notebook (GBRSA is more recommended)]
Cai, M. B., Schuck, N. W., Pillow, J. W., & Niv, Y. (2016). A Bayesian method for reducing bias in neural representational similarity analysis. In Advances in Neural Information Processing Systems (pp. 4951-4959). [full-text link]
Lal, T. M., Baldwin, P. R., Cai, M. B., Savjani, R. R., Eagleman, D. M., Ress, D., & Salas, R. (2016). Real time functional MRI training to decrease motion in imaging studies: lack of significant improvement. Bulletin of the Menninger Clinic, 80(4), 348-356. [pdf] [link]
Zheng, T., Cai, M., & Jiang, T. (2010). A novel approach to activation detection in fMRI based on empirical mode decomposition. Journal of integrative neuroscience, 9(04), 407-427. [link]
Learning and Decision Making
Song, M., Baah, P. A., Cai, M. B.*, & Niv, Y.* (2022). Humans combine value learning and hypothesis testing strategically in multi-dimensional probabilistic reward learning. PLOS Computational Biology, 18(11), e1010699. [full-text link]
Song, M., Niv, Y., Cai, M. B. (2021) Using Recurrent Neural Networks to Understand Human Reward Learning . Proceedings of the Annual Meeting of the Cognitive Science Society. Vol. 43. 1388-1394. [full-text link]
Song, M., Niv, Y., Cai, M. B., Learning what is relevant for rewards via value-based serial hypothesis testing. Proceedings of the Annual Conference of Cognitive Science Society 2020. [full-text link]
Schuck, N. W., Cai, M. B., Wilson, R. C., & Niv, Y. (2016). Human orbitofrontal cortex represents a cognitive map of state space. Neuron, 91(6), 1402-1412. [full-text link]
Cai, M. B., & Eagleman, D. M. (2015). Duration estimates within a modality are integrated sub-optimally. Frontiers in psychology, 6, 1041. [full-text link]
Brain-inspired AI
Arora, T., Li, L. E., & Cai, M. B. (2021). Learning to perceive objects by prediction. In SVRHM 2021 Workshop@ NeurIPS. [openreview link]
Duan, Z., Min, M. R., Li, L. E., Cai, M., Xu, Y., & Ni, B. (2019). Disentangled Deep Autoencoding Regularization for Robust Image Classification. arXiv preprint arXiv:1902.11134. [full-text link]
Time Perception
Cai, M. B., Eagleman, D. M., & Ma, W. J. (2015). Perceived duration is reduced by repetition but not by high-level expectation. Journal of vision, 15(13), 19-19. [full-text link]
Cai, M. B., & Eagleman, D. M. (2015). Duration estimates within a modality are integrated sub-optimally. Frontiers in psychology, 6, 1041. [full-text link]
Cai, M., Stetson, C., & Eagleman, D. M. (2012). A neural model for temporal order judgments and their active recalibration: a common mechanism for space and time?. Frontiers in psychology, 3, 470. [full-text link]
