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CI Causal Induction CD Cognitive Development CEIL Cultural Evolution and Iterated Learning DMRL Decision Making and Reinforcement Learning E Education F Foundations IB Inductive Biases NBM Nonparametric Bayesian Models P Perception PR Probabilistic Reasoning RPM Rational Process Models S&C Similarity and Categorization SC Social Cognition SML Statistical Models of Language
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IB Bencomo, G. , Gupta, M. , Marinescu, I. , McCoy, R. T. , & Griffiths, T. L. (2025). Teasing apart architecture and initial weights as sources of inductive bias in neural networks. (preprint)
DMRL IB Correa, C. G. , Sanborn, S. , Ho, M. K. , Callaway, F. , Daw, N. D. , & Griffiths, T. L. (2025). Exploring the hierarchical structure of human plans via program generation. Cognition, 255 , 105990. (pdf)
IB Gupta, M. , Rane, S. , McCoy, R. T. , & Griffiths, T. L. (2025). Convolutional neural networks can (meta-) learn the same-different relation. (preprint)
F IB Ku, A. , Griffiths, T. L. , & Chan, S. (2025). Predictability shapes adaptation: An evolutionary perspective on modes of learning in transformers. (preprint)
IB S&C Marinescu, I. , McCoy, R. T. , & Griffiths, T. L. (2025). Neural networks can capture human concept learning without assuming symbolic representations. (preprint)
IB SML Zhang, L. , Veselovsky, V. , McCoy, R. T. , & Griffiths, T. L. (2025). Identifying and mitigating the influence of the prior distribution in large language models. (preprint)
IB NBM Bencomo, G. M. , Snell, J. C. , & Griffiths, T. L. (2024). Implicit Maximum a Posteriori Filtering via adaptive optimization. Proceedings of the 12th International Conference on Learning Representations (ICLR). (preprint)
IB P Campbell, D. , Kumar, S. , Giallanza, T. , Griffiths, T. L. , & Cohen, J. D. (2024). Human-like geometric abstraction in large pre-trained neural networks. 46th Annual Meeting of the Cognitive Science Society. (pdf)
IB SML Kumar, S. , Marjieh, R. , Zhang, B. , Campbell, D. , Hu, M. Y. , Bhatt, U. , Lake, B. M. , & Griffiths, T. L. (2024). Comparing abstraction in humans and large language models using multimodal serial reproduction. 46th Annual Meeting of the Cognitive Science Society. (pdf)
IB P Marjieh, R. , Kumar, S. , Campbell, D. , Zhang, L. , Bencomo, G. , Snell, J. , & Griffiths, T. L. (2024). Using contrastive learning with generative similarity to learn spaces that capture human inductive biases. (preprint)
IB S&C Rane, S. , Ho, M. , Sucholutsky, I. , & Griffiths, T. L. (2024). Concept alignment as a prerequisite for value alignment. 46th Annual Meeting of the Cognitive Science Society. (pdf)
IB Sucholutsky, I. , & Griffiths, T. L. (2024). Why should we care if machines learn human-like representations? AAAI-24 Spring Symposium on Human-Like Learning . (pdf)
IB S&C Wynn, A. H. , Sucholutsky, I. , Griffiths, T. L. (2024). Learning human-like representations to enable learning human values. Advances in Neural Information Processing Systems 38 . (pdf)
IB S&C Zhang, L. , Nelson, L. , & Griffiths, T. L. (2024). Analyzing the benefits of prototypes for semi-supervised category learning. 46th Annual Meeting of the Cognitive Science Society. (pdf)
IB S&C Zhu, J. Q. , Yan, H. , & Griffiths, T. (2024). Recovering mental representations from large language models with Markov chain Monte Carlo. 46th Annual Meeting of the Cognitive Science Society. (pdf)
IB P Campbell, D. , Kumar, S. , Giallanza, T. , Cohen, J. D. , & Griffiths, T. L. (2023). Relational constraints on neural networks reproduce human biases towards abstract geometric regularity. (preprint)
F IB Griffiths, T. L. , Kumar, S. , & McCoy, R. T. (2023). On the hazards of relating representations and inductive biases. Behavioral and Brain Sciences, 46 , e275. (pdf)
IB SML McCoy, R. T. , & Griffiths, T. L. (2023). Modeling rapid language learning by distilling Bayesian priors into artificial neural networks. (preprint)
DMRL IB Rane, S. , Ho, M. , Sucholutsky, I. , & Griffiths, T. L. (2023). Concept alignment as a prerequisite for value alignment. AAAI 2024 Bridge on Collaborative AI and Modeling of Humans. (pdf)
IB P Sucholutsky, I. , & Griffiths, T. L. (2023). Alignment with human representations supports robust few-shot learning. Advances in Neural Information Processing Systems 37. (pdf)
IB 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)
IB SML Kumar, S. , Correa, C. G. , Dasgupta, I. , Marjieh, R. , Hu, M. Y. , Hawkins, R.D. , Daw, N. D. , Cohen, J. D. , Narasimhan, K. R. , & Griffiths, T. L. (2022). Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines. Advances in Neural Information Processing Systems 36. (preprint)
IB SML Yamakoshi, T. , Griffiths, T.L. , Hawkins, R.D. (2022) Probing BERT's priors with serial reproduction chains. Findings of the Association for Computational Linguistics (ACL). (pdf)
DMRL IB Kumar, S. , Dasgupta, I. , Cohen, J. D. , Daw, N. D. , & Griffiths, T. L. (2021). Meta-learning of structured task distributions in humans and machines. Proceedings of the 9th International Conference on Learning Representations (ICLR) . (pdf)
IB SML Hawkins, R. D.* , Yamakoshi, T.* , Griffiths, T. L. , & Goldberg, A. E. (2020). Investigating representations of verb bias in neural language models. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . (pdf)
IB 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)
CEIL IB Thompson, B. , & Griffiths, T. L. (2019). Inductive biases constrain cumulative cultural evolution. Proceedings of the 41st Annual Conference of the Cognitive Science Society . (pdf)
DMRL IB Lieder, F. , Callaway, F. , Jain, Y. R. , Krueger, P. M. , Das, P. , Gul, S. , & Griffiths, T. L. (2019). A cognitive tutor for helping people overcome present bias. Proceedings of the Fourth Multidisciplinary Conference on Reinforcement Learning and Decision Making . (pdf)
IB S&C Peterson, J. C. , Abbott, J. T. , & Griffiths, T. L. (2018). Evaluating (and improving) the correspondence between deep neural networks and human representations. Cognitive Science, 42 , 2648-2669. (pdf)
IB 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)