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Thomas, A. J., Saxe, R. & Spelke, E. S. Infants represent 'like-kin' affiliation . Budapest Conference on Cognitive Development (2020).

Thomas, A. J., Woo, B., Nettle, D., Spelke, E. S. & Saxe, R. Early concepts of intimacy: Young humans use saliva sharing to infer close relationships. Science 375, 311 - 315 (2022).

Thomas, A. J., Saxe, R. & Spelke, E. S. Infants infer potential social partners by observing the interactions of their parent with unknown others. Proceedings of the National Academy of Sciences 119, (2022).

pnas.2121390119.pdf (1.43 MB)

Theurel, D. Modeling brain dynamics using mathematics from quantum mechanics. Peter Chin's Lab, Boston University Boston University, (2017).

Telenczuk, B. et al. Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex. Nature Scientific Reports (2017). doi:10.1038/srep40211

srep40211.pdf (2.53 MB)

Tejwani, R. et al. Incorporating Rich Social Interactions Into MDPs. 2022 IEEE International Conference on Robotics and Automation (ICRA)2022 International Conference on Robotics and Automation (ICRA) (2022). doi:10.1109/ICRA46639.2022.9811991

Tejwani, R. et al. Incorporating Rich Social Interactions Into MDPs. (2022).

CBMM-Memo-133.pdf (1.68 MB)

Tejwani, R. et al. Zero-shot linear combinations of grounded social interactions with Linear Social MDPs. Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI) (2023).

Tejwani, R., Kuo, Y. - L., Shu, T., Katz, B. & Barbu, A. Social Interactions as Recursive MDPs. (2021).

CBMM-Memo-130.pdf (1.52 MB)

Tegmark, M. Improved Measures of Integrated Information. PLOS Computational Biology (2016). doi:10.1371/journal.pcbi.100512310.1371

1601.02626.pdf (3.49 MB)

Tazi, Y., Berger, M. & Freiwald, W. A. Towards an objective characterization of an individual's facial movements using Self-Supervised Person-Specific-Models. arXiv (2022). at <https://arxiv.org/abs/2211.08279>

Tang, H., Kreiman, G. & Zhao, Q. Computational and Cognitive Neuroscience of Vision (Springer Singapore, 2017). at <http://www.springer.com/us/book/9789811002113>

Tang, H. et al. Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences (2018). doi:10.1073/pnas.1719397115

1719397115.full_.pdf (1.1 MB)

Tang, H. et al. Spatiotemporal Dynamics Underlying Object Completion in Human Ventral Visual Cortex. Neuron 83, 736 - 748 (2014).

Tang, H. et al. Predicting episodic memory formation for movie events. Scientific Reports (2016). doi:10.1038/srep30175

Tang, H. et al. Predicting episodic memory formation for movie events [code]. (2016).

Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex. eLIFE (2016). doi:10.7554/eLife.12352

Manuscript (1.83 MB)

Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [dataset]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>

Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [code]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>

Tang, H. et al. A machine learning approach to predict episodic memory formation. 2016 Annual Conference on Information Science and Systems (CISS) 539 - 544 (2016). doi:10.1109/CISS.2016.7460560

Tang, H., Buia, C., Madsen, J., Anderson, W. S. & Kreiman, G. A role for recurrent processing in object completion: neurophysiological, psychophysical and computational evidence. (2014).

CBMM-Memo-009.pdf (4.21 MB)

Tang, H. et al. Predicting episodic memory formation for movie events [dataset]. (2016).

Tan, C. & Poggio, T. Neural tuning size is a key factor underlying holistic face processing. (2014).

CBMM-Memo-021-1406.3793.pdf (387.79 KB)

Tan, C. & Poggio, T. Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing. Public Library of Science | PLoS ONE 1(3): e0150980, (2016).

journal.pone_.0150980.PDF (384.15 KB)

Tacchetti, A., Isik, L. & Poggio, T. Invariant action recognition dataset. (2017). at <https://doi.org/10.7910/DVN/DMT0PG>