Publication
] Critical Cues in Early Physical Reasoning. SRCD (2017).
Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects. Cognitive Science Society (2019). at <https://mindmodeling.org/cogsci2019/papers/0506/index.html>
Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects.pdf (2.62 MB)
Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects.pdf (2.62 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) Image interpretation by iterative bottom-up top- down processing. (2021). CBMM-Memo-120.pdf (2.83 MB)
CBMM-Memo-120.pdf (2.83 MB) 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).
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).
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) 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) 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) Word-level Invariant Representations From Acoustic Waveforms. INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association (International Speech Communication Association (ISCA), 2014). at <http://www.isca-speech.org/archive/interspeech_2014/i14_2385.html>
Do Deep Neural Networks Suffer from Crowding?. (2017). CBMM-Memo-069.pdf (6.47 MB)
CBMM-Memo-069.pdf (6.47 MB)