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Found 910 results
Author [ Title(Desc)] Type Year
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McCoy, J. P. & Ullman, T. D. A Minimal Turing Test. Journal of Experimental Social Psychology 79, 1 - 8 (2018).
Ben-Yosef, G., Kreiman, G. & Ullman, S. Minimal videos: Trade-off between spatial and temporal information in human and machine vision. Cognition (2020). doi:10.1016/j.cognition.2020.104263
Aghajan, Z. M., Kreiman, G. & Fried, I. Minute-scale periodicity of neuronal firing in the human entorhinal cortex. Cell Reports 42, 113271 (2023).PDF icon 1-s2.0-S2211124723012834-main.pdf (5.33 MB)
Ullman, S., Dorfman, N. & Harari, D. A model for discovering ‘containment’ relations. Cognition 183, 67 - 81 (2019).
Ben-Yosef, G., Assif, L., Harari, D. & Ullman, S. A model for full local image interpretation. Cognitive Science Society (2015).PDF icon Full object interpretation CogSci 2015 Print version.pdf (707.34 KB)
Ben-Yosef, G., Yachin, A. & Ullman, S. A model for interpreting social interactions in local image regions. AAAI Spring Symposium Series, Science of Intelligence (2017). at <http://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15354>PDF icon 2017-Ben-Yosef_Yachin_Ullman-A_model_for_interpreting_social_interactions_in_local_image_regions.pdf (1.53 MB)
Feather, J., Leclerc, G., Mądry, A. & McDermott, J. H. Model metamers reveal divergent invariances between biological and artificial neural networks. Nature Neuroscience (2023). doi:10.1038/s41593-023-01442-0
Winston, P. Henry. Model-based Story Summary. 6th Workshop on Computational Models of Narrative (2015). doi:10.4230/OASIcs.CMN.2015.157
Theurel, D. Modeling brain dynamics using mathematics from quantum mechanics. Peter Chin's Lab, Boston University Boston University, (2017).
Houlihan, S. Dae & Saxe, R. Modeling emotion attributions as inference in an intuitive theory of mind. Mechanisms Underlying Emotion Regulation and Developmental Psychopathology (2017).
Smith, K. A. et al. Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (2019). at <http: //physadept.csail.mit.edu/>PDF icon ADEPT_NeurIPS.pdf (11.07 MB)
Krafft, P., Baker, C., Pentland, A. & Tenenbaum, J. B. Modeling Human Ad Hoc Coordination. AAAI (2016).PDF icon krafft_aaai2016.pdf (247.58 KB)
Nakahashi, R., Baker, C. & Tenenbaum, J. B. Modeling human understanding of complex intentional action with a Bayesian nonparametric subgoal model. AAAI (2016).PDF icon nakahashi_aaai2016.pdf (1.74 MB)
Schiatti, L. et al. Modeling Visual Impairments with Artificial Neural Networks: a Review. International Conference on Computer Vision 2023 (2023). at <https://openaccess.thecvf.com/content/ICCV2023W/ACVR/html/Schiatti_Modeling_Visual_Impairments_with_Artificial_Neural_Networks_a_Review_ICCVW_2023_paper.html>
Marciniak, K., Dicke, P. W. & Thier, P. Monkeys head-gaze following is fast, precise and not fully suppressible. Proc Biol Sci 282, 20151020 (2015).PDF icon Marciniak et al 2015 Proc R Soc B Monkeys head gaze following is fast precise and not fully suppressible.pdf (7.07 MB)
Magid, R. & Schulz, L. Moral alchemy: How love changes norms. Cognition 167, 135 -150 (2017).PDF icon Moral Alchemy_Magid&Schulz.pdf (627.46 KB)
Sosa, F. A., Ullman, T., Tenenbaum, J. B., Gershman, S. J. & Gerstenberg, T. Moral dynamics: Grounding moral judgment in intuitive physics and intuitive psychology. Cognition 217, 104890 (2021).
Scott, K. Moving the lab home: validation of a web-based system for developmental studies. (2015).
Dubach, R., Abdallah, M. S. & Poggio, T. Multiplicative Regularization Generalizes Better Than Additive Regularization. (2025).PDF icon CBMM Memo 158.pdf (4.8 MB)
Yaari, A. Uri et al. Multi-resolution modeling of a discrete stochastic process identifies causes of cancer. International Conference on Learning Representations (2021). at <https://openreview.net/forum?id=KtH8W3S_RE>
Marques, T., Schrimpf, M. & DiCarlo, J. J. Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior. bioRxiv (2021).PDF icon 2021.03.01.433495v2.full_.pdf (3.23 MB)
Shen, W., Wang, B., Jiang, Y., Wang, Y. & Yuille, A. Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection. (2017).PDF icon CBMM-Memo-080.pdf (2.51 MB)
Tomov, M. S., Schulz, E. & Gershman, S. J. Multi-task reinforcement learning in humans. Nature Human Behaviour (2021). doi:10.1038/s41562-020-01035-y
Zhang, C. et al. Musings on Deep Learning: Properties of SGD. (2017).PDF icon CBMM Memo 067 v2 (revised 7/19/2017) (5.88 MB)PDF icon CBMM Memo 067 v3 (revised 9/15/2017) (5.89 MB)PDF icon CBMM Memo 067 v4 (revised 12/26/2017) (5.57 MB)
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The naive utility calculus: Computational principles underlying commonsense psychology. (2016).

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