[17] Seyed-Mahdi Khaligh-Razavi and Nikolaus Kriegeskorte. Deep supervised, but not unsupervised, models may explain it cortical representation. PLoS computational biology, 10(11):e1003915, 2014.
[18] Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep networks. In International conference on machine learning, pages 3319-3328. PMLR, 2017.
[19] Drew Linsley, Junkyung Kim, Vijay Veerabadran, Charles Windolf, and Thomas Serre. Learning long-range spatial dependencies with horizontal gated recurrent units. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018. URL https://proceedings.neurips.cc/paper_files/paper/2018/file/ec8956637a99787bd197eacd77ace5e-Paper.pdf.
[20] Rishi Rajalingham, Elias B Issa, Pouya Bashivan, Kohitij Kar, Kailyn Schmidt, and James J DiCarlo. Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks. Journal of Neuroscience, 38(33):7255-7269, 2018.
[21] Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A Wichmann, and Wieland Brendel. Partial success in closing the gap between human and machine vision. Advances in Neural Information Processing Systems, 34:23885-23899, 2021.
[22] Joshua C Peterson, Joshua T Abbott, and Thomas L Griffiths. Evaluating (and improving) the correspondence between deep neural networks and human representations. Cognitive science, 42(8):2648-2669, 2018.
[23] Andre M Bastos, W Martin Usrey, Rick A Adams, George R Mangun, Pascal Fries, and Karl J Friston. Canonical microcircuits for predictive coding. Neuron, 76(4):695-711, 2012.
[24] Maxime Cauchoux, Gladys Barragan-Jason, Thomas Serre, and Emmanuel J Barbeau. The neural dynamics of face detection in the wild revealed by mvpa. Journal of Neuroscience, 34(3):846-854, 2014.
[25] Andre Moraes Bastos, Julien Vezoli, Conrado Arturo Bosman, Jan-Mathijs Schoffelen, Robert Oostenveld, Jarrod Robert Dowdall, Peter De Weerd, Henry Kennedy, and Pascal Fries. Visual areas exert feedforward and feedback influences through distinct frequency channels. Neuron, 85(2):390-401, 2015.
[26] Courtney J Spoerer, Tim C Kietzmann, Johannes Mehrer, Ian Charest, and Nikolaus Kriegeskorte. Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision. PLoS computational biology, 16(10):e1008215, 2020.
[27] Jonas Kubilius, Martin Schrimpf, Aran Nayebi, Daniel Bear, Daniel LK Yamins, and James J DiCarlo. Cornet: Modeling the neural mechanisms of core object recognition. BioRxiv, page 408385, 2018.
[28] Kohitij Kar, Jonas Kubilius, Kailyn Schmidt, Elias B. Issa, and James J. DiCarlo. Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior. Nature Neuroscience, 22(6):974-983, April 2019. doi: 10.1038/s41593-019-0392-5. URL https://doi.org/10.1038/s41593-019-0392-5.
[29] Ronald R Yager and Liping Liu. Classic works of the Dempster-Shafer theory of belief functions, volume 219. Springer, 2008.
[30] Murat Sensoy, Lance Kaplan, and Melih Kandemir. Evidential deep learning to quantify classification uncertainty. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018.
[31] Richard P Heitz. The speed-accuracy tradeoff: history, physiology, methodology, and behavior. Frontiers in neuroscience, 8:150, 2014.
[32] Roger Ratcliff and Gail McKoon. The diffusion decision model: theory and data for two-choice decision tasks. Neural computation, 20(4):873-922, 2008.
[33] Alexander Fengler, Lakshmi N Govindarajan, Tony Chen, and Michael J Frank. Likelihood approximation networks (lans) for fast inference of simulation models in cognitive neuroscience. *Elife*, 10:e65074, 2021.
[34] James F Cavanagh and Michael J Frank. Frontal theta as a mechanism for cognitive control. Trends in cognitive sciences, 18(8):414-421, 2014.
[35] Budmonde Duinkharjav, Praneeth Chakravarthula, Rachel Brown, Anjul Patney, and Qi Sun. Image features influence reaction time. ACM Transactions on Graphics, 41(4):1-15, July 2022. doi: 10.1145/3528223.3530055. URL https://doi.org/10.1145/3528223.3530055.