[18] J. V. Haxby, J. S. Guntupalli, A. C. Connolly, Y. O. Halchenko, B. R. Conroy, M. I. Gobbini, M. Hanke, and P. J. Ramadge. A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron, 72(2):404-416, Oct. 2011. doi: 10.1016/j.neuron.2011.08.026. URL https://doi.org/10.1016/j.neuron.2011.08.026.
[19] J. V. Haxby, J. S. Guntupalli, S. A. Nastase, and M. Feilong. Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies. *Elife*, 9, June 2020.
[20] T. Horikawa and Y. Kamitani. Generic decoding of seen and imagined objects using hierarchical visual features. Nature Communications, 8(1), May 2017. doi: 10.1038/ncomms15037. URL https://doi.org/10.1038/ncomms15037.
[21] A. G. Huth, S. Nishimoto, A. T. Vu, and J. L. Gallant. A continuous semantic space describes the representation of thousands of object and action categories across the human brain. Neuron, 76(6):1210-1224, Dec. 2012. doi: 10.1016/j.neuron.2012.10.014. URL https://doi.org/10.1016/j.neuron.2012.10.014.
[22] G. Jiahui, M. Feilong, M. V. di Oleggio Castello, J. S. Guntupalli, V. Chauhan, J. V. Haxby, and M. I. Gobbini. Predicting individual face-selective topography using naturalistic stimuli. NeuroImage, 216:116458, Aug. 2020. doi: 10.1016/j.neuroimage.2019.116458. URL https://doi.org/10.1016/j.neuroimage.2019.116458.
[23] J.-R. King, F. Charton, D. Lopez-Paz, and M. Oquab. Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations. NeuroImage, 220:117028, Oct. 2020. doi: 10.1016/j.neuroimage.2020.117028. URL https://doi.org/10.1016/j.neuroimage.2020.117028.
[24] D. Kozinska, F. Carducci, and K. Nowinski. Automatic alignment of eeg/meg and mri data sets. Clinical neurophysiology, 112(8):1553-1561, 2001.
[25] N. Kriegeskorte, M. Mur, D. A. Ruff, R. Kiani, J. Bodurka, H. Esteky, K. Tanaka, and P. A. Bandettini. Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron, 60(6):1126-1141, 2008. ISSN 0896-6273.
[26] M. Kumar, M. J. Anderson, J. W. Antony, C. Baldassano, P. P. Brooks, M. B. Cai, P.-H. C. Chen, C. T. Ellis, G. Henselman-Petrusek, D. Huberdeau, J. B. Hutchinson, Y. P. Li, Q. Lu, J. R. Manning, A. C. Mennen, S. A. Nastase, H. Richard, A. C. Schapiro, N. W. Schuck, M. Shvartsman, N. Sundaram, D. Suo, J. S. Turek, D. Turner, V. A. Vo, G. Wallace, Y. Wang, J. A. Williams, H. Zhang, X. Zhu, M. Capota $^{\text{一}}$ , J. D. Cohen, U. Hasson, K. Li, P. J. Ramadge, N. B. Turk-Browne, T. L. Willke, and K. A. Norman. BrainIAK: The brain imaging analysis kit. Aperture Neuro, 2021(4), Feb. 2022. doi: 10.52294/31bb5b68-2184-411b-8c00-a1dac61e1da. URL https://doi.org/10.52294/31bb5b68-2184-411b-8c00-a1dac61e1da.
[27] C. S. Lee, M. Aly, and C. Baldassano. Anticipation of temporally structured events in the brain. Elife, 10, Apr. 2021.
[28] H. Lee and J. Chen. Predicting memory from the network structure of naturalistic events. Nature Communications, 13(1), July 2022. doi: 10.1038/s41467-022-31965-2. URL https://doi.org/10.1038/s41467-022-31965-2.
[29] H. Lee, B. Bellana, and J. Chen. What can narratives tell us about the neural bases of human memory? Current Opinion in Behavioral Sciences, 32:111-119, Apr. 2020. doi: 10.1016/j.cobeha.2020.02.007. URL https://doi.org/10.1016/j.cobeha.2020.02.007.
[30] M. Meshulam, L. Hasenfratz, H. Hillman, Y.-F. Liu, M. Nguyen, K. A. Norman, and U. Hasson. Neural alignment predicts learning outcomes in students taking an introduction to computer science course. Nature communications, 12(1):1-14, 2021.
[31] J. Millet, C. Caucheteux, Y. Boubenec, A. Gramfort, E. Dunbar, C. Pallier, J.-R. King, et al. Toward a realistic model of speech processing in the brain with self-supervised learning. Advances in Neural Information Processing Systems, 35:33428-33443, 2022.