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

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

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

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

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

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

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

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

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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