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 Peterson, J.
SML	Liu, R., Geng, J., Peterson, J. C., Sucholutsky, I., & Griffiths, T. L. (2024). How do Large Language Models Navigate Conflicts between Honesty and Helpfulness? (preprint)
P S&C	Marjieh, R., Jacoby, N., Peterson, J. C., & Griffiths, T. L. (2024). The Universal Law of Generalization holds for naturalistic stimuli. Journal of Experimental Psychology: General, 153(3), 573–589. (pdf)
DMRL	Reichman, D., Peterson, J. C., & Griffiths, T. L. (2024). Machine learning for modeling human decisions. Decision, 11(4), 619. (pdf)
DMRL	Zhu, J. Q., Peterson, J. C., Enke, B., & Griffiths, T. L. (2024) Capturing the Complexity of Human Strategic Decision-Making with Machine Learning. (preprint)
DMRL	Agrawal, M., Peterson, J. C., Cohen, J. D., & Griffiths, T. L. (2023). Stress, intertemporal choice, and mitigation behavior during the COVID-19 pandemic. Journal of Experimental Psychology: General, 152(9), 2695–2702. (pdf)
P S&C	Jha, A., Peterson, J. C., & Griffiths, T. L. (2023). Extracting low‐dimensional psychological representations from convolutional neural networks. Cognitive Science, 47(1), e13226. (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 S&C	Battleday, R. M., Peterson, J. C., & Griffiths, T. L. (2021). From convolutional neural networks to models of higher-level cognition (and back again). Annals of the New York Academy of Sciences. (pdf)
P S&C	Grewal, K., Peterson, J. C., Thompson, B., & Griffiths, T. L. (2021). Exploring the Structure of Human Adjective Representations. SVRHM 2021 Workshop @ NeurIPS. (pdf)
DMRL	Agrawal, M., Peterson, J. C., & Griffiths, T. L. (2020). Scaling up psychology via Scientific Regret Minimization. Proceedings of the National Academy of Sciences. (pdf)
P S&C	Battleday, R. M., Peterson, J. C., & Griffiths, T. L. (2020). Capturing human categorization of natural images by combining deep networks and cognitive models. Nature Communications, 11(1), 1-14. (pdf)
P S&C	Jha, A., Peterson, J. C., & Griffiths, T. L. (2020). Extracting low-dimensional psychological representations from convolutional neural networks. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pdf)
P S&C	Singh, P., Peterson, J. C., Battleday, R. M., & Griffiths, T. L. (2020). End-to-end deep prototype and exemplar models for predicting human behavior. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (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	Bourgin, D., Peterson, J. C., Reichman, D., Russell, S., & Griffiths, T. L. (2019). Cognitive model priors for predicting human decisions. Proceedings of the 36th International Conference on Machine Learning (ICML). (pdf)
RPM DMRL	Agrawal, M., Peterson, J. C., & Griffiths, T. L. (2019). Using machine learning to guide cognitive modeling: a case study in moral reasoning. Proceedings of the 41st Annual Conference of the Cognitive Science Society . (pdf)
P S&C	Suchow, J. W., Peterson, J. C., & Griffiths, T. L. (2018). Learning a face space for experiments on human identity. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pdf)
S&C SML	Chen, D., Peterson, J. C., & Griffiths, T. L. (2017). Evaluating vector-space models of analogy. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)