Publications

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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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Inductive Biases
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)

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