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)
DMRL	Peterson, J., Mancoridis, M., & Griffiths, T. (2023). To each their own theory: Exploring the limits of individual differences in decisions under risk. 45th Annual Meeting of the Cognitive Science Society. (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	Peterson, J. C., Uddenberg, S., Griffiths, T. L., Todorov, A., & Suchow, J. W. (2022). Deep models of superficial face judgments. Proceedings of the National Academy of Sciences, 119(17), e2115228119. (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	Peterson, J. C., Bourgin, D., Agrawal, M., Reichman, D., & Griffiths, T. (2021). Using large-scale experiments and machine learning to discover theories of human decision-making. Science, 372(6547), 1209-1214. (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)
S&C SML	Peterson, J. C., Chen, D., & Griffiths, T. L. (2020). Parallelograms revisited: Exploring the limitations of vector space models for simple analogies. Cognition, 205, 104440. (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)
S&C	Peterson, J. C., Soulos, P., Nematzadeh, A., & Griffiths, T. L. (2019). Learning to generalize like humans using basic-level object labels. Journal of Vision, 19(10), 60a-60a. (link)
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)
P S&C	Peterson, J. C., Battleday, R., Griffiths, T. L., & Russakovsky, O. (2019). Human uncertainty makes classification more robust. Proceedings of the IEEE International Conference on Computer Vision. (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	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)
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	Peterson, J. C., Suchow, J. W., Aghi, K., Ku, A. Y., & Griffiths, T. L. (2018). Capturing human category representations by sampling in deep feature spaces. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pdf)
S&C SML	Peterson, J. C., Soulos, P., Nematzadeh, A., & Griffiths, T. L. (2018). Learning hierarchical visual representations in deep neural networks using hierarchical linguistic labels. 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)
P S&C	Peterson, J. C., & Griffiths, T. L. (2017). Evidence for the size principle in semantic and perceptual domains. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
P S&C	Peterson, J. C., Abbott, J. T., & Griffiths, T. L. (2016). Adapting deep network features to capture psychological representations. Proceedings of the 38th Annual Conference of the Cognitive Science Society. (pdf) (Winner of the Computational Modeling Prize in Perception/Action)