Publications

CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

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Gerstenberg, T., Halpern, J. Y. & Tenenbaum, J. B. 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)

Gerstenberg, T. & Tenenbaum, J. B. Oxford Handbook of Causal Reasoning (Oxford University Press, 2016).

Intuitive Theories (Gerstenberg, Tenenbaum, 2016.pdf (6.06 MB)

Gerstenberg, T., Goodman, N. D., Lagnado, D. A. & Tenenbaum, J. B. 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)

Gerstenberg, T., Peterson, M. F., Goodman, N. D., Lagnado, D. A. & Tenenbaum, J. B. Eye-Tracking Causality. Psychological Science 73, (2017).

Gerstenberg, T. et al. Lucky or clever? From changed expectations to attributions of responsibility. Cognition (2018).

Gershman, S. J. Origin of perseveration in the trade-off between reward and complexity. Cognition 204, 104394 (2020).

Gershman, S. J. How to never be wrong. Psychonomic Bulletin & Review 26, 13 - 28 (2019).

Gershman, S. J., Horvitz, E. J. & Tenenbaum, J. B. Computational rationality: A converging paradigm for intelligence in brains, minds, and machines. Science 349, 273-278 (2015).

Gershman, S. J., Tenenbaum, J. B. & Jaekel, F. Discovering hierarchical motion structure. Vision Research Available online 26 March 2015, (2015).

hierarchical_motion.pdf (582.01 KB)

Gershman, S. J. & Ullman, T. D. Causal implicatures from correlational statements. PLOS ONE 18, e0286067 (2023).

Gershman, S. J. & Burke, T. Mental control of uncertainty. Cognitive, Affective, & Behavioral Neuroscience 23, 465 - 475 (2022).

Gershman, S. J. & Cikara, M. Structure learning principles of stereotype change. Psychonomic Bulletin & Review 30, 1273 - 1293 (2023).

Gershman, S. J. The Generative Adversarial Brain. Frontiers in Artificial Intelligence 2, (2019).

Gershman, S. J. & Daw, N. D. Reinforcement learning and episodic memory in humans and animals: an integrative framework. Annual Review of Psychology 68, (2017).

GershmanDaw17.pdf (422.11 KB)

Gershman, S. J. What have we learned about artificial intelligence from studying the brain?. Biological Cybernetics 118, 1 - 5 (2024).

Gen, C. et al. ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. arXiv (2020). at <https://arxiv.org/abs/2007.04954>

2007.04954.pdf (7.06 MB)

Gaziv, G., Lee, M. J. & DiCarlo, J. J. Robustified ANNs Reveal Wormholes Between Human Category Percepts. arXiv (2023). at <https://arxiv.org/abs/2308.06887>

Gaziv, G., Lee, M. J. & DiCarlo, J. J. Strong and Precise Modulation of Human Percepts via Robustified ANNs. NeurIPS 2023 (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/hash/d00904cebc0d5b69fada8ad33d0f1422-Abstract-Conference.html>

Gartstein, M. A. et al. Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).

Garrote, E., Jhuang, H., Huehne, H., Poggio, T. & Serre, T. A Large Video Database for Human Motion Recognition. (2011).

Kuehne_etal_ICCV2011.pdf (433.27 KB)

Garrote, E. et al. System for Mouse Behavior Recognition. (2010).

Gao, T., Harari, D., Tenenbaum, J. B. & Ullman, S. When Computer Vision Gazes at Cognition. (2014).

CBMM-Memo-025.pdf (3.78 MB)

Gant, J., Banburski, A., Deza, A. & Poggio, T. Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN. NeurIPS 2021 (2021). at <https://nips.cc/Conferences/2021/Schedule?showEvent=21868>

Gan, Y., Galanti, T., Poggio, T. & Malach, E. On the Power of Decision Trees in Auto-Regressive Language Modeling. (2024).

CBMM-Memo-149.pdf (2.11 MB)

Gan, Y. & Poggio, T. For HyperBFs AGOP is a greedy approximation to gradient descent. (2024).

CBMM-Memo-148.pdf (1.06 MB)

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