Publication
] What is changing when: Decoding visual information in movies from human intracranial recordings. Neuroimage (2017). doi:https://doi.org/10.1016/j.neuroimage.2017.08.027
Invariant representations for action recognition in the visual system. Computational and Systems Neuroscience (2015).
Perceiving social interactions in the posterior superior temporal sulcus. Proceedings of the National Academy of Sciences 114, (2017).
What is changing when: decoding visual information in movies from human intracranial recordings. NeuroImage 180, Part A, 147-159 (2018).
Human neurophysiological responses during movies (2.78 MB)
Human neurophysiological responses during movies (2.78 MB) Universal and Non-universal Features of Musical Pitch Perception Revealed by Singing. Current Biology (2019). doi:10.1016/j.cub.2019.08.020
Can Deep Learning Recognize Subtle Human Activities?. CVPR 2020 (2020).
Self-supervised intrinsic image decomposition. Annual Conference on Neural Information Processing Systems (NIPS) (2017). at <https://papers.nips.cc/paper/7175-self-supervised-intrinsic-image-decomposition> intrinsicImg_nips_2017.pdf (5.87 MB)
intrinsicImg_nips_2017.pdf (5.87 MB) Not So Innocent: Toddlers’ Inferences About Costs and Culpability. Psychological Science 26, 633-40 (2015). NotSoInnocent_InPress.pdf (238.53 KB)
NotSoInnocent_InPress.pdf (238.53 KB) Mastery of the logic of natural numbers is not the result of mastery of counting: Evidence from late counters. . Developmental Science (2016). doi:10.1111/desc.12459
Children’s understanding of the costs and rewards underlying rational action. Cognition 140, 14–23 (2015). CM_inPress.pdf (438.5 KB)
CM_inPress.pdf (438.5 KB) Children understand that agents maximize expected utilities. Journal of Experimental Psychology: General 146, 1574 - 1585 (2017). ExpectedUtilities_Final.pdf (950.09 KB)
ExpectedUtilities_Final.pdf (950.09 KB) The naive utility calculus: computational principles underlying social cognition. Trends Cogn Sci. (2016). doi:10.1016/j.tics.2016.05.011
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN. 34th International Conference on Machine Learning 70, 1733-1741 (2017). 1612.05231.pdf (2.3 MB)
1612.05231.pdf (2.3 MB) Discovering Switching Autoregressive Dynamics in Neural Spike Train Recordings. (2015). cosyne2015b.pdf (7.27 MB)
cosyne2015b.pdf (7.27 MB) To find better neural network models of human vision, find better neural network models of primate vision. BioRxiv (2019). at <https://www.biorxiv.org/content/10.1101/688390v1.full>
Large-scale hyperparameter search for predicting human brain responses in the Algonauts challenge. The Algonauts Project: Explaining the Human Visual Brain Workshop 2019 (2019). doi:10.1101/689844
Are topographic deep convolutional neural networks better models of the ventral visual stream?. Conference on Cognitive Computational Neuroscience (2019).
Using child‐friendly movie stimuli to study the development of face, place, and object regions from age 3 to 12 years. Human Brain Mapping (2022). doi:10.1002/hbm.25815
CNNs reveal the computational implausibility of the expertise hypothesis. iScience 26, 105976 (2023).
Using artificial neural networks to ask ‘why’ questions of minds and brains. Trends in Neurosciences 46, 240 - 254 (2023).
Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition . Society for Neuroscience (2019).
Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Nature Neuroscience (2019). doi:10.1038/s41593-019-0392-5 Author's last draft (1.74 MB)
Author's last draft (1.74 MB)