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Modeling Human Ad Hoc Coordination. AAAI (2016). krafft_aaai2016.pdf (247.58 KB)
Modeling human understanding of complex intentional action with a Bayesian nonparametric subgoal model. AAAI (2016). nakahashi_aaai2016.pdf (1.74 MB)
The naive utility calculus: computational principles underlying social cognition. Trends Cogn Sci. (2016). doi:10.1016/j.tics.2016.05.011
Natural science: Active learning in dynamic physical microworlds. 38th Annual Meeting of the Cognitive Science Society (2016). Natural Science (Bramley, Gerstenberg, Tenenbaum, 2016).pdf (5.39 MB)
Probing the compositionality of intuitive functions. (2016). CBMM-Memo-048.pdf (815.72 KB)
Rapid Physical Predictions from Convolutional Neural Networks. Neural Information Processing Systems, Intuitive Physics Workshop (2016). at <http://phys.csail.mit.edu/papers/9.pdf> Rapid Physical Predictions - NIPS Physics Workshop Poster (1.47 MB)
Understanding "almost": Empirical and computational studies of near misses. 38th Annual Meeting of the Cognitive Science Society (2016). Understanding almost (Gerstenberg, Tenenbaum, 2016).pdf (4.08 MB)
The causes and consequences explicit in verbs. Language, Cognition and Neuroscience 30, 716-734 (2015).
Children’s understanding of the costs and rewards underlying rational action. Cognition 140, 14–23 (2015). CM_inPress.pdf (438.5 KB)
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines. Science 349, 273-278 (2015).
Discovering hierarchical motion structure. Vision Research Available online 26 March 2015, (2015). hierarchical_motion.pdf (582.01 KB)
Efficient and robust analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations. Annual Conference of the Cognitive Science Society (2015). yildirimetal_cogsci15.pdf (3.22 MB)
Galileo: Perceiving physical object properties by integrating a physics engine with deep learning. NIPS 2015 (2015). at <https://papers.nips.cc/paper/5780-galileo-perceiving-physical-object-properties-by-integrating-a-physics-engine-with-deep-learning>
How, whether, why: Causal judgments as counterfactual contrasts. Annual Meeting of the Cognitive Science Society (CogSci) 782-787 (2015). at <https://mindmodeling.org/cogsci2015/papers/0142/index.html> GerstenbergEtAl2015-Cogsci.pdf (2.16 MB)
Human-level concept learning through probabilistic program induction. Science 350, 1332-1338 (2015).
Hypothesis-Space Constraints in Causal Learning. Annual Meeting of the Cognitive Science Society (CogSci) (2015). at <https://mindmodeling.org/cogsci2015/papers/0418/index.html> hypothesis_space_constraints (1).pdf (1.54 MB)
Information Selection in Noisy Environments with Large Action Spaces. 9th Biennial Conference of the Cognitive Development Society Columbus, OH, (2015).
Not So Innocent: Toddlers’ Inferences About Costs and Culpability. Psychological Science 26, 633-40 (2015). NotSoInnocent_InPress.pdf (238.53 KB)
Perceiving Fully Occluded Objects with Physical Simulation. Cognitive Science Conference (CogSci) (2015).
Picture: An Imperative Probabilistic Programming Language for Scene Perception. Computer Vision and Pattern Recognition (2015).
Responsibility judgments in voting scenarios. Annual Meeting of the Cognitive Science Society (CogSci) 788-793 (2015). at <https://mindmodeling.org/cogsci2015/papers/0143/index.html> Gerstenberg_paper0143.pdf (651.82 KB)
Concepts in a Probabilistic Language of Thought. (2014). CBMM-Memo-010.pdf (902.53 KB)
Explaining Monkey Face Patch System as Efficient Analysis-by-Synthesis. (2014). yildirimetal_cosyne15.pdf (313.57 KB)
When Computer Vision Gazes at Cognition. (2014). CBMM-Memo-025.pdf (3.78 MB)