Computational Cognitive Science Lab

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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. Proceeding of the Annual Meeting of the Cognitive Science Society, 47.(pdf)
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	McCoy, R. T., & Griffiths, T. L. (2025). Modeling rapid language learning by distilling Bayesian priors into artificial neural networks. Nature Communications, 16(1) (pdf)
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)
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)