Publications

View By Topic:
All Topics
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

(Click on an author's name to view all papers by that author.)


Filter publications

By Sucholutsky, I.
S&C
SML
Marjieh, R., Veselovsky, V., Griffiths, T. L., & Sucholutsky, I. (2025). What is a number, that a large language model may know it? (preprint)
S&C
SML
Bai, X., Wang, A., Sucholutsky, I., & Griffiths, T. L. (2024). Measuring implicit bias in explicitly unbiased large language models. (preprint)
P
Chen, A., Sucholutsky, I., Russakovsky, O., & Griffiths, T. L. (2024). Analyzing the roles of language and vision in learning from limited data. 46th Annual Meeting of the Cognitive Science Society. (pdf)
F
Collins, K. M., Sucholutsky, I., Bhatt, U., Chandra, K., Wong, L., Lee, M., Zhang, C. E., Zhi-Xuan, T., Ho, M., Mansinghka, V., Weller, A., Tenenbaum, J. B., & Griffiths, T. L. (2024). Building machines that learn and think with people. Nature Human Behaviour, 8(10), 1851-1863. (pdf)
SML
DMRL
Liu, R., Geng, J., Peterson, J. C., Sucholutsky, I., & Griffiths, T. L. (2024). Large language models assume people are more rational than we really are. (preprint)
PR
SML
Liu, R., Geng, J., Wu, A. J., Sucholutsky, I., Lombrozo, T., & Griffiths, T. L. (2024). Mind your step (by step): Chain-of-thought can reduce performance on tasks where thinking makes humans worse. (preprint)
F
PR
Malaviya, M., Sucholutsky, I., & Griffiths, T. L. (2024). Pushing the limits of learning from limited data. Proceedings of the AAAI Symposium Series, 3 (1), 559-561. (pdf)
P
Marjieh, R., van Rijn, P., Sucholutsky, I., Lee, H., Griffiths, T. L., & Jacoby, N. (2024). A rational analysis of the speech-to-song illusion. 46th Annual Meeting of the Cognitive Science Society. (pdf)
P
SML
Marjieh, R., van Rijn, P., Sucholutsky, I., Lee, H., Jacoby, N., & Griffiths, T. L. (2024). Characterizing the large-scale structure of grounded semantic networks. (preprint)
S&C
SML
Marjieh, R., Sucholutsky, I., van Rijn, P., Jacoby, N., & Griffiths, T. L. (2024). Large language models predict human sensory judgments across six modalities. Scientific Reports, 14(1), 21445.(pdf)
P
Niedermann, J. P., Sucholutsky, I., Marjieh, R., Çelen, E., Griffiths, T., Jacoby, N., & van Rijn, P. (2024) Studying the effect of globalization on color perception using multilingual online recruitment and large language models. 46th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL
Oktar, K., Sucholutsky, I., Lombrozo, T., & Griffiths, T. L. (2024). Dimensions of disagreement: Unpacking divergence and misalignment in cognitive science and artificial intelligence. Decision, 11(4), 511–522. (pdf)
SML
DMRL
Peng, A., Sucholutsky, I., Li, B. Z., Sumers, T. R., Griffiths, T. L., Andreas, J., & Shah, J. A. (2024). Learning with language-guided state abstractions. Proceedings of the 12th International Conference on Learning Representations (ICLR). (pdf)
SML
DMRL
Peng, A., Bobu, A., Li, B. Z., Sumers, T. R., Sucholutsky, I., Kumar, N., & Griffiths, T. L. (2024). Preference-conditioned language-guided abstraction. Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. (pdf)
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)
F
S&C
Sucholutsky, I., Collins, K. M., Malaviya, M., Jacoby, N., Liu, W., Sumers, T. R., Korakakis, M., Bhatt, U., Ho, M., Tenenbaum, J. B., Love, B., Pardos, Z. A., Weller, A., & Griffiths, T. L. (2024). Representational alignment supports effective machine teaching. (preprint)
S&C
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)
S&C
Sucholutsky, I., Zhao, B., & Griffiths, T. L. (2024). Using compositionality to learn many categories from few examples. 46th Annual Meeting of the Cognitive Science Society. (pdf)
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)
S&C
Marjieh, R., Van Rijn, P., Sucholutsky, I., Sumers, T., Lee, H., Griffiths, T. L., & Jacoby, N. (2023) Words are all you need? Language as an approximation for human similarity judgments. Proceedings of the 11th International Conference on Learning Representations (ICLR). (preprint)
P
PR
Marjieh, R., Sucholutsky, I., Langlois, T. A., Jacoby, N., & Griffiths, T. L. (2023) Analyzing diffusion as serial reproduction. Proceedings of the 40th International Conference on Machine Learning (ICML), 202 24005-24019. (preprint)
P
SML
Marjieh, R., Sucholutsky, I., van Rijn, P., Jacoby, N., & Griffiths, T. L. (2023). What language reveals about perception: Distilling psychophysical knowledge from large language models. 45th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL
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)
S&C
Sucholutsky, I., Battleday, R., Collins, K., Marjieh, R., Peterson, J. C., Singh, P., Bhatt, U., Jacoby, N., Weller, A., & Griffiths, T. L. (2023). On the informativeness of supervision signals. Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence. (pdf)
P
PR
Sucholutsky, I., & Griffiths, T. L. (2023). Alignment with human representations supports robust few-shot learning. Advances in Neural Information Processing Systems 37. (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
Malaviya, M., Sucholutsky, I., Oktar, K., & Griffiths, T. L. (2022). Can Humans Do Less-Than-One-Shot Learning? Proceedings of the 44th Annual Conference of the Cognitive Science Society. (pdf)
S&C
SML
Marjieh, R., Sucholutsky, I., Sumers, T. R., Jacoby, N., & Griffiths, T. L. (2022). Predicting Human Similarity Judgments Using Large Language Models. Proceedings of the 44th Annual Conference of the Cognitive Science Society. (pdf)

© 2025 Computational Cognitive Science Lab  |  Department of Psychology  |  Princeton University