Computational Cognitive Science Lab

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F Foundations
P Perception
E Education
CI Causal Induction
CD Cognitive Development
PR Probabilistic Reasoning
RPM Rational Process Models
S&C Similarity and Categorization
SML Statistical Models of Language
NBM Nonparametric Bayesian Models
CEIL Cultural Evolution and Iterated Learning
DMRL Decision Making and Reinforcement Learning

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By Grant, E.
F	Griffiths, T. L., Zhu, J. Q., Grant, E., & McCoy, R. T. (2023). Bayes in the age of intelligent machines. (preprint)
PR NBM	Li, M. Y., Grant, E., & Griffiths, T. L. (2023). Gaussian process surrogate models for neural networks. Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence. (pdf)
DMRL	Sucholutsky, I., Muttenthaler, L., Weller, A., Peng, A., Bobu, A., Kim, B., Love, B. C., Grant, E., Achterberg, J., Tenenbaum, J. B., Collins, K. M., Hermann, K. L., Oktar, K., Greff, K., Hebart, M. N., Jacoby, N., Marjieh, R., Geirhos, R., Chen, S., Kornblith, S., Rane, S., Konkle, T., O'Connell, T. P., Unterthiner, T., Lampinen, A. K., Müller, K.-R., Toneva, M., & Griffiths, T. L. (2023). Getting aligned on representational alignment. (preprint)
S&C	Dasgupta, I., Grant, E., & Griffiths, T. L. (2022). Distinguishing rule- and exemplar-based generalization in learning systems. Proceedings of the International Conference on Machine Learning. (pdf)
P	Langlois, T. A., Zhao, H. C., Grant, E., Dasgupta, I., Griffiths, T. L., & Jacoby, N. (2021). Passive attention in artificial neural networks predicts human visual selectivity. Advances in Neural Information Processing Systems, 34. (pdf)
P	Tuli, S., Dasgupta, I., Grant, E., & Griffiths, T. L. (2021). Are Convolutional Neural Networks or Transformers more like human vision?. Proceedings of the 43rd Annual Meeting of the Cognitive Science Society. (link)
DMRL	Dubey, R., Grant, E., Luo, M., Narasimhan, K. R., & Griffiths, T. L. (2020). Connecting context-specific adaptation in humans to meta-learning. (preprint)
SML	McCoy, R. T., Grant, E., Smolensky, P., Griffiths, T. L., & Linzen, T. (2020). Universal linguistic inductive biases via meta-learning. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pdf)
PR NBM	Jerfel, G., Grant, E. L., Griffiths, T. L., & Heller, K. (2019). Reconciling meta-learning and continual learning with online mixtures of tasks. Advances in Neural Information Processing Systems, 32. (pdf)
F RPM	Griffiths, T. L., Callaway, F., Chang, M. B., Grant, E., Krueger, P. M., & Lieder, F. (2019). Doing more with less: meta-reasoning and meta-learning in humans and machines. Current Opinion in Behavioral Sciences, 29, 24-30. (pdf)
P S&C	Grant, E., Peterson, J. C., & Griffiths, T. (2019). Learning deep taxonomic priors for concept learning from few positive examples. Proceedings of the 41st Annual Conference of the Cognitive Science Society. (pdf)
DMRL	Burns, K., Nematzadeh, A., Grant, E., Gopnik, A., & Griffiths, T. L. (2018). Exploiting attention to reveal shortcomings in memory models. Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 378-380. (pdf)
SML	Nematzadeh, A., Burns, K., Grant, E., Gopnik, A., & Griffiths, T. L. (2018). Evaluating theory of mind in question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. (pdf)
PR	Grant, E., Finn, C., Levine, S., Darrell, T., & Griffiths, T. L. (2018). Recasting gradient-based meta-learning as hierarchical Bayes. In Proceedings of the 6th International Conference on Learning Representations (ICLR). (pdf)
CD SML	Grant, E., Nematzadeh, A., & Griffiths, T. L. (2017). How can memory-augmented neural networks pass a false-belief task? Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)