Publication
] Human Learning in Atari. AAAI Spring Symposium Series (2017).
Tsividis et al - Human Learning in Atari.pdf (844.47 KB)
Tsividis et al - Human Learning in Atari.pdf (844.47 KB) Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions. PLOS Biology 21, e3002366 (2023).
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) (2020). 2006.10782.pdf (2.62 MB)
2006.10782.pdf (2.62 MB) Image interpretation by iterative bottom-up top- down processing. (2021). CBMM-Memo-120.pdf (2.83 MB)
CBMM-Memo-120.pdf (2.83 MB) Atoms of recognition in human and computer vision. PNAS 113, 2744–2749 (2016). mirc_author_manuscript_with_figures_and_SI-2.pdf (1.65 MB)
mirc_author_manuscript_with_figures_and_SI-2.pdf (1.65 MB) Critical Cues in Early Physical Reasoning. SRCD (2017).
Learning physical parameters from dynamic scenes. Cognitive Psychology 104, 57-82 (2018). T-Ullman-etal_CogPsych_LearningPhysicalParametersFromDynamicScenes.pdf (3.15 MB)
T-Ullman-etal_CogPsych_LearningPhysicalParametersFromDynamicScenes.pdf (3.15 MB) Bayesian Models of Conceptual Development: Learning as Building Models of the World. Annual Review of Developmental Psychology 2, 533 - 558 (2020).
Effort as a bridging concept across action and action understanding: Weight and Physical Effort in Predictions of Efficiency in Other Agents. International Conference on Infant Studies (ICIS) (2016).
Mind Games: Game Engines as an Architecture for Intuitive Physics. Trends in Cognitive Science 21, 649 - 665 (2017). Preprint submitted to Trends in Cognitive Science (17.64 MB)
Preprint submitted to Trends in Cognitive Science (17.64 MB) No evidence for prolactin’s involvement in the post-ejaculatory refractory periodAbstract. Communications Biology 4, (2021).
Thalamic contribution to CA1-mPFC interactions during sleep. Society for Neuroscience's Annual Meeting - SfN 2017 (2017).
AbstractSFNfinal.docx (13.14 KB)
AbstractSFNfinal.docx (13.14 KB) Predicting actions before they occur. (2015). PredictingActions (1.43 MB)
Supplemental Video 1: Experimental set up and task (16.38 MB) Supplemental Video 2: An example FullVid and CutVid trial clips from experiment 4 (5.47 MB)
PredictingActions (1.43 MB) Supplemental Video 1: Experimental set up and task (16.38 MB) Supplemental Video 2: An example FullVid and CutVid trial clips from experiment 4 (5.47 MB) Neural mechanisms supporting facial expressions . unknown (2019).
Fast iterative regularization by reusing dataAbstract. Journal of Inverse and Ill-posed Problems (2023). doi:10.1515/jiip-2023-0009
Teachers recruit mentalizing regions to represent learners’ beliefs. Proceedings of the National Academy of Sciences 120, (2023).
Empirical Inference 59 - 69 (Springer Berlin Heidelberg, 2013). doi:10.1007/978-3-642-41136-610.1007/978-3-642-41136-6_7 Author's Version (147.25 KB)
Author's Version (147.25 KB) Implicit regularization with strongly convex bias: Stability and acceleration. Analysis and Applications 21, 165 - 191 (2023).
Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?. (2018). CBMM-Memo-095.pdf (1.96 MB)
CBMM-Memo-095.pdf (1.96 MB) Do Neural Networks for Segmentation Understand Insideness?. (2020). CBMM-Memo-105.pdf (4.63 MB) CBMM Memo 105 v2 (July 2, 2020) (3.2 MB) CBMM Memo 105 v3 (January 25, 2022) (8.33 MB)
CBMM-Memo-105.pdf (4.63 MB) CBMM Memo 105 v2 (July 2, 2020) (3.2 MB) CBMM Memo 105 v3 (January 25, 2022) (8.33 MB) Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception. Science Advances 6, eabd4205 (2020). gk7967.pdf (3.07 MB)
gk7967.pdf (3.07 MB)