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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Kim, D. et al. The ability to predict actions of others from distributed cues is still developing in children. PsyArXiv Preprints (2020). doi:10.31234/osf.io/pu3tf

Action_prediction_in_children.pdf (427.84 KB)

Amir, N. et al. Abstracts of the 2014 Brains, Minds, and Machines Summer Course. (2014).

CBMM-Memo-024.pdf (2.86 MB)

A del Molino, G., Boix, X., Lim, J. & Tan, A. Active Video Summarization: Customized Summaries via On-line Interaction. AAAI Conference on Artificial Intelligence (2017).

21-Garcia-del-Molino-14856.pdf (413.77 KB)

Xiang, Y., Landy, J., Cushman, F. A., Vélez, N. & Gershman, S. J. Actual and counterfactual effort contribute to responsibility attributions in collaborative tasks. Cognition 241, 105609 (2023).

Tomova, L. et al. Acute social isolation evokes midbrain craving responses similar to hunger. Nature Neuroscience 23, 1597 - 1605 (2020).

s41593-020-00742-z.pdf (5.47 MB)

Mlynarski, W. & Hermundstad, A. M. Adaptive Coding for Dynamic Sensory Inference. eLife (2018).

Mlynarski, W. & McDermott, J. H. Adaptive Compression of Statistically Homogenous Sensory Signals. Computational and Systems Neuroscience (COSYNE) (2017).

Melloni, L. et al. An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory. PLOS ONE 18, e0268577 (2023).

journal.pone_.0268577.pdf (1.99 MB)

Consortium, C. et al. An adversarial collaboration to critically evaluate theories of consciousness. bioRxiv (2023). doi:https://doi.org/10.1101/2023.06.23.546249

Guo, C. et al. Adversarially trained neural representations may already be as robust as corresponding biological neural representations. arXiv (2022).

Shu, T. et al. AGENT: A Benchmark for Core Psychological Reasoning. Proceedings of the 38th International Conference on Machine Learning (2021).

Udrescu, S. - M. et al. AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) (2020).

2006.10782.pdf (2.62 MB)

Yaari, A. et al. The Aligned Multimodal Movie Treebank: An audio, video, dependency-parse treebank. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (2022).

Dapello, J. et al. Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. bioRxiv (2022).

Zhang, Y., Marciniak, K. & Freiwald, W. A. Analysis of Macaque Monkeys’ Social and Physical Interaction Processing with Eye tracking Data. The Rockefeller University 2019 Summer Science Research Program (SSRP) (2019).

Mhaskar, H. & Poggio, T. An analysis of training and generalization errors in shallow and deep networks. (2018).

CBMM-Memo-076.pdf (772.61 KB)

CBMM-Memo-076v2.pdf (2.67 MB)

Mhaskar, H. & Poggio, T. An analysis of training and generalization errors in shallow and deep networks. (2019).

CBMM-Memo-098.pdf (687.36 KB)

CBMM Memo 098 v4 (08/2019) (2.63 MB)

Mhaskar, H. & Poggio, T. An analysis of training and generalization errors in shallow and deep networks. Neural Networks 121, 229 - 241 (2020).

Dasgupta, I., Guo, D., Gershman, S. J. & Goodman, N. D. Analyzing Machine‐Learned Representations: A Natural Language Case Study. Cognitive Science 44, (2020).

Berzak, Y., Huang, Y., Barbu, A., Korhonen, A. & Katz, B. Anchoring and Agreement in Syntactic Annotations. (2016).

CBMM-Memo-055.pdf (768.54 KB)

Nilchian, P., Wilson, M. A. & Sanders, H. Animal-to-Animal Variability in Partial Hippocampal Remapping in Repeated Environments. The Journal of Neuroscience 42, 5268 - 5280 (2022).

5268.full_.pdf (2.97 MB)

O'Connell, T. P. et al. Approaching human 3D shape perception with neurally mappable models. arXiv (2023). at <https://arxiv.org/abs/2308.11300>

Li, Y. et al. An approximate representation of objects underlies physical reasoning. psyArXiv (2022). at <https://psyarxiv.com/vebu5/>

Jozwik, K. M., Lee, H., Kanwisher, N. & DiCarlo, J. J. Are topographic deep convolutional neural networks better models of the ventral visual stream?. Conference on Cognitive Computational Neuroscience (2019).

Wirtshafter, H. S. & Wilson, M. A. Artificial intelligence insights into hippocampal processing. Frontiers in Computational Neuroscience 16, (2022).

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