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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 Griffiths, T.
F CEIL	Allen, K., Brändle, F., Botvinick, M., Fan, J. E., Gershman, S. J., Gopnik, A., Griffiths, T. L., Hartshorne, J. K., Hauser, T. U., Ho, M., de Leeuw, J. R., Ma, W. J., Murayama, K., Nelson, J. D., van Opheusden, B., Pouncy, T., Rafner, J., Rahwan, I., Rutledge, R. B., Sherson, J., Şimşek, Ö., Spiers, H., Summerfield, C., Thalmann, M., Vélez, N., Watrous, A. J., Tenenbaum, J. B., & Schulz, E. (2024). Using games to understand the mind. Nature Human Behaviour, 1-9. (pdf)
F	Almaatouq, A., Griffiths, T. L., Suchow, J. W., Whiting, M. E., Evans, J., & Watts, D. J. (2024). Beyond playing 20 questions with nature: Integrative experiment design in the social and behavioral sciences. Behavioral and Brain Sciences, 47, e33. (pdf)
F	Alon, N., Cohen, J. D., Griffiths, T. L., Manurangsi, P., Reichman, D., & Shinkar, I. (2024). Erratum: Multitasking Capacity: Hardness Results and Improved Constructions. SIAM Journal on Discrete Mathematics, 38(2), 2001-2003. (pdf)
DMRL	Bai, X., Griffiths, T. L., & Fiske, S. T. (2024). Costly exploration produces stereotypes with dimensions of warmth and competence. Journal of Experimental Psychology: General. (pdf)
S&C SML	Bai, X., Wang, A., Sucholutsky, I., & Griffiths, T. L. (2024). Measuring implicit bias in explicitly unbiased large language models. (preprint)
CEIL	Barretto, D., Marjieh, R., & Griffiths, T. L. (2024). Reaching Consensus through Theory of Mind in Social Networks with Locally Distributed Interactions. 46th Annual Meeting of the Cognitive Science Society. (pdf)
F SML	Binz, M., Akata, E., Bethge, M., Brändle, F., Callaway, F., Coda-Forno, J., Dayan, P., Demircan, C., Eckstein, M. K., Éltető, N., Griffiths, T. L., Haridi, S., Jagadish, A. K., Ji-An, L., Kipnis, A., Kumar, S., Ludwig, T., Mathony, M., Mattar, M., Modirshanechi, A., Nath, S. S., Peterson, J. C., Rmus, M., Russek, E. M., Saanum, T., Scharfenberg, N., Schubert, J. A., Schulze Buschoff, L. M., Singhi, N., Sui, X., Thalmann, M., Theis, F., Truong, V., Udandarao, V., Voudouris, K., Wilson, R., Witte, K., Wu, S., Wulff, D., Xiong, H., & Schulz, E. (2024). Centaur: a foundation model of human cognition. (preprint)
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)
F P	Campbell, D., Rane, S., Giallanza, T., De Sabbata, N., Ghods, K., Joshi, A., Ku, A., Frankland, S. M., Griffiths, T. L., & Cohen, J. D., & Webb, T. W. (2024). Understanding the limits of vision language models through the lens of the binding problem. (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)
RPM DMRL	Cornell, C. A., Norman, K. A., Griffiths, T. L., & Zhang, Q. (2024). Improving memory search through model-based cue selection. Psychological Science, 35 (1). (pdf)
DMRL	Correa, C. G., Griffiths, T. L., & Daw, N. D. (2024). Program-Based Strategy Induction for Reinforcement Learning. 46th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL	Correa, C. G., Sanborn, S., Ho, M. K., Callaway, F., Daw, N. D., & Griffiths, T. L. (2024). Exploring the hierarchical structure of human plans via program generation. Cognition, 255, 105990. (pdf)
S&C	Devraj, A., Griffiths, T. L., & Zhang, Q. (2024). Reconciling categorization and memory via environmental statistics. Psychonomic Bulletin & Review, 1-19. (pdf)
P DMRL	Dubey, R., Hardy, M., Griffiths, T., & Bhui, R. (2024). AI-generated visuals of car-free American cities help increase support for sustainable transport policies. Nature Sustainability, 7, 399–403. (pdf)
F PR	Griffiths, T. L., Zhu, J. Q., Grant, E., & McCoy, R. T. (2024). Bayes in the age of intelligent machines. Current Directions in Psychological Science, 33(5), 283-291. (pdf)
SML CEIL	Guo, X., Huang, K., Liu, J., Fan, W., Vélez, N., Wu, Q., Wang, H., & Griffiths, T. L. (2024). Embodied LLM agents learn to cooperate in organized teams. (preprint)
CI CD	Harootonian, S. K., Niv, Y., Griffiths, T., & Ho, M. K. (2024). Modeling Cognitive Strategies in Teaching: Integrating Theory of Mind and Heuristics. 46th Annual Meeting of the Cognitive Science Society. (pdf)
F SML	Ichien, N., Bhatia, S., Ivanova, A., Webb, T., Griffiths, T., & Binz, M. (2024). Higher cognition in large language models. 46th Annual Meeting of the Cognitive Science Society. (pdf)
P 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)
SML	Kumar, S., Sumers, T. R., Yamakoshi, T., Goldstein, A., Hasson, U., Norman, K. A., Griffiths, T. L., Hawkins, R. D., & Nastase, S. A. (2024). Shared functional specialization in transformer-based language models and the human brain. Nature Communications, 15(1), 5523. (pdf)
DMRL	Kuperwajs, I., van Opheusden, B., Russek, E., & Griffiths, T. L. (2024). Learning from rewards and social information in naturalistic strategic behavior. (preprint)
SML	Liu, R., Sumers, T. R., Dasgupta, I., & Griffiths, T. L. (2024). How do Large Language Models Navigate Conflicts between Honesty and Helpfulness? Proceedings of the 41st International Conference on Machine Learning (ICML). (pdf)
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)
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 CI	Lu, Q., Nguyen, T. T., Zhang, Q., Hasson, U., Griffiths, T. L., & Zacks, J. M. (2024). Reconciling shared versus context-specific information in a neural network model of latent causes. Scientific Reports, 14(1), 16782. (pdf)
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)
DMRL	Mancoridis, M., Sumers, T., & Griffiths, T. (2024). Publish or Perish: Simulating the Impact of Publication Policies on Science. 46th Annual Meeting of the Cognitive Science Society. (pdf)
PR S&C	Marinescu, I. R., Thomas McCoy, R. T., & Griffiths, T. (2024). Distilling Symbolic Priors for Concept Learning into Neural Networks. 46th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL	Marjieh, R., Gokhale, A., Bullo, F. and Griffiths, T. L., (2024). Task Allocation in Teams as a Multi-Armed Bandit. In Proceedings of Collective Intelligence 2024.(pdf)
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)
P PR	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)
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)
PR SML	Meylan, S. C., & Griffiths, T. L. (2024). Word Forms Reflect Trade‐Offs Between Speaker Effort and Robust Listener Recognition. Cognitive Science, 48(7), e13478. (pdf)
F SML	McCoy, R. T., Yao, S., Friedman, D., Hardy, M. D., & Griffiths, T. L. (2024). Embers of autoregression show how large language models are shaped by the problem they are trained to solve. PNAS, 121(41), e2322420121. (pdf)
F SML	McCoy, R. T., Yao, S., Friedman, D., Hardy, M. D., & Griffiths, T. L. (2024). When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1. (preprint)
RPM DMRL	Mieczkowski, E., Turner, C. R., Vélez, N., & Griffiths, T. (2024). Many Hands Don't Always Make Light Work: Explaining Social Loafing via Multiprocessing Efficiency. 46th Annual Meeting of the Cognitive Science Society. (pdf)
RPM DMRL	Mieczkowski, E., Turner, C. R., Vélez, N., & Griffiths, T. (2024). People Evaluate Idle Collaborators Based on their Impact on Task Efficiency. (preprint)
F	Musslick, S., Bartlett, L. K., Chandramouli, S. H., Dubova, M., Gobet, F., Griffiths, T. L., Hullman, J., King, R. D., Kutz, J. N., Lucas, C. G., Mahesh, S., Pestilli, F., Sloman, S. J., & Holmes, W. R. (2024). Automating the Practice of Science--Opportunities, Challenges, and Implications. (preprint)
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)
PR DMRL	Oktar, K., Lombrozo, T., & Griffiths, T. L. (2024). Learning from aggregated opinion. Psychological Science, 35(9), 1010–1024. (pdf)
PR	Oktar, K., Sumers, T., & Griffiths, T. L. (2024). A Rational Model of Vigilance in Motivated Communication. 46th Annual Meeting of the Cognitive Science Society. (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. (preprint)
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)
PR SML	Prabhakar, A., Griffiths, T. L., & McCoy, R. T. (2024). Deciphering the factors influencing the efficacy of chain-of-thought: Probability, memorization, and noisy reasoning. (preprint)
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)
P	Rane, S., Ku, A., Baldridge, J., Tenney, I., Griffiths, T. L., & Kim, B. (2024). Can Generative Multimodal Models Count to Ten? 46th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL	Reichman, D., Peterson, J. C., & Griffiths, T. L. (2024). Machine learning for modeling human decisions. Decision, 11(4), 619. (pdf)
DMRL	Russek, E. M., Callaway, F., & Griffiths, T. L. (2024). Inverting cognitive models with neural networks to infer preferences from fixations. Cognitive Science, 48(11), e70015. (pdf)
CD RPM	Russek, E. M., Turner, C. R., McEwen, E., Miscov, A. M., Seed, A., & Griffiths, T. L. (2024). Modeling the Contributions of Capacity and Control to Working Memory Development. 46th Annual Meeting of the Cognitive Science Society. (pdf)
SML DMRL	De Sabbata, C. N., Sumers, T. R., & Griffiths, T. L. (2024). Rational metareasoning for large language models. (preprint)
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)
SML	Tian, Y., Ravichander, A., Qin, L., Bras, R. L., Marjieh, R., Peng, N., Choi, Y., Griffiths, T. L., & Brahman, F. (2024). MacGyver: Are large language models creative problem solvers? NAACL 2024 (pdf)
CD CEIL	Turner, C. R., Morgan, T. J. H., & Griffiths, T. L. (2024). Environmental complexity and regularity shape the evolution of cognition. Proceedings of the Royal Society B, 291(2033), 20241524. (pdf)
P	Urano, Y., Marjieh, R., Griffiths, T. L., & Jacoby, N. (2024). The Influence of Social Information and Presentation Interface on Aesthetic Evaluations. 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. NeurIPS 2024. (pdf)
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)
SML	Zhang, L., Li, M. Y., & Griffiths, T. L. (2024) What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions. (preprint)
RPM DMRL	Zhao, B., Velez, N., & Griffiths, T. L. (2024). A Rational Model of Innovation by Recombination. 46th Annual Meeting of the Cognitive Science Society. (pdf)
CD DMRL	Zhao, B., Vélez, N., & Griffiths, T. L. (2024). Comparing Human Behavior to an Optimal Policy for Innovation. Proceedings of the AAAI Symposium Series, 3(1), 598-599. (pdf)
PR SML	Zhu, J. Q., & Griffiths, T. L. (2024). Incoherent Probability Judgments in Large Language Models. 46th Annual Meeting of the Cognitive Science Society. (pdf)
PR SML	Zhu, J. Q., & Griffiths, T. L. (2024). Eliciting the Priors of Large Language Models using Iterated In-Context Learning. (preprint)
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)
PR 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)
SML DMRL	Zhu, J. Q., Yan, H., & Griffiths, T. L. (2024). Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice. (preprint)
F P	Zuo, Y., Kayan, K., Wang, M., Jeon, K., Deng, J., & Griffiths, T. L. (2024). Towards Foundation Models for 3D Vision: How Close Are We? (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)
F	Allen, K. R., Brändle, F., Botvinick, M., Fan, J., Gershman, S. J., Griffiths, T. L., Hartshorne, J., Hauser, T. U., Ho, M. K., de Leeuw, J., Ma, W. J., Murayama, K., Nelson, J. D., van Opheusden, B., Pouncy, H. T., Rafner, J., Rahwan, I., Rutledge, R., Sherson, J., Simsek, O., Spiers, H., Summerfield, C., Thalmann, M., Vélez, N., Watrous, A., Tenenbaum, J., & Schulz, E. (2023). Using games to understand the mind. (preprint)
NBM	Bencomo, G. M., Snell, J. C., & Griffiths, T. L. (2023). Implicit Maximum a Posteriori Filtering via adaptive optimization. (preprint)
CEIL	Brinkmann, L., Baumann, F., Bonnefon, J., Derex, M., Müller, T. F., Nussberger, A., Czaplicka, A., Acerbi, A., Griffiths, T. L., Henrich, J., Leibo, J. Z., McElreath, R., Oudeyer, P., Stray, J., & Rahwan, I. (2023). Machine culture. Nature Human Behaviour, 7(11), 1855-1868.(pdf)
CD	Buchsbaum, D., Gelpi, R., Whalen, A., Griffiths, T. L., & Xu, F. (2023). Can children balance majority size with information quality in learning about preferences? (preprint)
RPM DMRL	Callaway, F., Griffiths, T. L., & Karreskog, G. (2023). Rational heuristics for one-shot games. (preprint)
RPM DMRL	Callaway, F., Griffiths, T. L., Norman, K. A., & Zhang, Q. (2023). Optimal metacognitive control of memory recall. Psychological Review. (pdf)
RPM DMRL	Callaway, F., Hardy, M., & Griffiths, T. L. (2023). Optimal nudging for cognitively bounded agents: A framework for modeling, predicting, and controlling the effects of choice architectures. Psychological Review. (preprint)
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)
NBM DMRL	Chang, M., Dayan, A. L., Meier, F., Griffiths, T. L., Levine, S., & Zhang, A. (2023). Neural Constraint Satisfaction: Hierarchical abstraction for combinatorial generalization in object rearrangement. Proceedings of the 11th International Conference on Learning Representations. (pdf)
RPM DMRL	Correa, C. G., Ho, M. K., Callaway, F., Daw, N. D., Griffiths, T. L. (2023). Humans decompose tasks by trading off utility and computational cost. PLOS Computational Biology, 19(6), e1011087. (pdf)
P SML	Dedhia, B., Chang, M., Snell, J. C., Griffiths, T. L., & Jha, N. K. (2023). Im-Promptu: In-context composition from image prompts. (preprint)
F	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)
CEIL DMRL	Hardy, M. D., Thompson, B., Krafft, P. M., & Griffiths, T. L. (2023). Resampling reduces bias amplification in experimental social networks. Nature Human Behavior, 7, 2084-2098. (pdf)
SML CEIL	Hawkins, R. D., Franke, M., Frank, M. C., Goldberg, A. E., Smith, K., Griffiths, T. L., & Goodman, N. D. (2023). From partners to populations: A hierarchical Bayesian account of coordination and convention. Psychological Review. (pdf)
DMRL	He, R., Correa, C. G., Griffiths, T. L., & Ho, M. K. (2023). Structurally guided task decomposition in spatial navigation tasks (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38. (pdf)
DMRL	Ho, M. K., Cohen, J. D., & Griffiths, T. L. (2023). Rational simplification and rigidity in human planning. Psychological Science, 34(11), 1281-1292. (pdf)
RPM DMRL	Jain, Y. R., Callaway, F., Griffiths, T. L., Dayan, P., He, R., Krueger, P. M., & Lieder, F. (2023). A computational process-tracing method for measuring people’s planning strategies and how they change over time. Behavior Research Methods, 55, 2037–2079. (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	Kumar, S., Dasgupta, I., Daw, N. D., Cohen, J. D., Griffiths, T. L. (2023). Disentangling abstraction from statistical pattern matching in human and machine learning. PLoS Computational Biology 19(8). (pdf)
PR NBM	Li, M. Y., Callaway, F., Thompson, W. D., Adams, R., & Griffiths, T. L. (2023). Learning to learn functions. Cognitive Science, 47(4), e13262. (pdf)
PR NBM	Li, M. Y., Grant, E., & Griffiths, T. L. (2023). Gaussian process surrogate models for neural networks. Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence. (pdf)
SML	Liu, R., Yen, H., Marjieh, R., Griffiths, T. L., & Krishna, R. (2023). Improving interpersonal communication by simulating audiences with language models. (preprint)
PR	Lu, Q., Nguyen, T. T., Hasson, U., Griffiths, T. L., Zacks, J. M., Gershman, S. J., & Norman, K. A. (2023). Toward a more neurally plausible neural network model of latent cause inference. Computational Cognitive Neuroscience Conference 2023. (pdf)
P S&C	Marjieh, R., Griffiths, T. L., & Jacoby, N. (2023). Musical pitch has multiple psychological geometries. (preprint)
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)
SML	McCoy, R. T., & Griffiths, T. L. (2023). Modeling rapid language learning by distilling Bayesian priors into artificial neural networks. (preprint)
DMRL	Oktar, K., Sucholutsky, I., Lombrozo, T., & Griffiths, T. L. (2023). Dimensions of disagreement: Unpacking divergence and misalignment in cognitive science and artificial intelligence. (preprint)
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)
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)
P CD	Rane, S., Nencheva, M. L., Wang, Z., Lew-Williams, C., Russakovsky, O., & Griffiths, T. L. (2023). Predicting word learning in children from the performance of computer vision systems. 45th Annual Meeting of the Cognitive Science Society. (pdf)
RPM DMRL	Reichman, D., Lieder, F., Bourgin, D. D., Talmon, N., & Griffiths, T. L. (2023). The computational challenges of means selection problems: Network structure of Goal Systems predicts human performance. Cognitive Science, 47(8), e13330. (pdf)
DMRL	Shin, M., Kim, J., van Opheusden, B., & Griffiths, T. L. (2023). Superhuman artificial intelligence can improve human decision-making by increasing novelty. Proceedings of the National Academy of Sciences, 120(12), e2214840120. (pdf)
NBM	Snell, J. C., Bencomo, G. M., Griffiths, T. L. (2023). A metalearned neural circuit for nonparametric Bayesian inference. (preprint)
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)
RPM DMRL	Sukhov, N., Dubey, R., Duke, A., & Griffiths, T. (2023). When to keep trying and when to let go: Benchmarking optimal quitting. (preprint)
SML DMRL	Sumers, T. R., Ho, M. K., Griffiths, T. L., & Hawkins, R. D. (2023). Reconciling truthfulness and relevance as epistemic and decision-theoretic utility. Psychological Review 131 (1), 194. (pdf)
E SML	Sumers, T. R., Ho, M. K., Hawkins, R. D., & Griffiths, T. L. (2023). Show or tell? Exploring when (and why) teaching with language outperforms demonstration. Cognition, 232, 105326. (pdf)
F SML	Sumers, T. R., Yao, S., Narasimhan, K., & Griffiths, T. L. (2023). Cognitive Architectures for Language Agents. (preprint)
P DMRL	Turner, C. R., Morgan, T., & Griffiths, T. (2023). The joint evolution of sensory systems and decision policy allows cognition. 45th Annual Meeting of the Cognitive Science Society. (pdf)
CEIL	Uddenberg, S., Thompson, B. D., Vlasceanu, M., Griffiths, T. L., & Todorov, A. (2023). Iterated learning reveals stereotypes of facial trustworthiness that propagate in the absence of evidence. Cognition, 237, 105452. (pdf)
CEIL	Vélez, N., Christian, B., Hardy, M., Thompson, B. D., & Griffiths, T. L. (2023). How do humans overcome individual computational limitations by working together? Cognitive Science, 47(1), e13232. (pdf)
PR	Wang, Z., Ku, A., Baldridge, J., Griffiths, T. L., & Kim, B. (2023). Gaussian Process Probes (GPP) for uncertainty-aware probing. Advances in Neural Information Processing Systems, 37. (pdf)
DMRL	Xia, F., Zhu, J., & Griffiths, T. (2023). Comparing human predictions from expert advice to on-line optimization algorithms. 45th Annual Meeting of the Cognitive Science Society. (pdf)
SML DMRL	Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023). Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, 37. (pdf)
RPM	Zhang, Q., Griffiths, T. L., & Norman, K. A. (2023). Optimal policies for free recall. Psychological Review, 130(4), 1104. (pdf)
RPM	Zhu, J. Q., Sanborn, A., Chater, N., & Griffiths, T. (2023). Computation-Limited Bayesian updating. 45th Annual Meeting of the Cognitive Science Society. (pdf)
F	Almaatouq, A., Griffiths, T. L., Suchow, J. W., Whiting, M. E., Evans, J., Watts, D. J. Beyond playing 20 questions with nature: Integrative experiment design in the social and behavioral sciences. Behavioral and Brain Sciences. >(pdf)
RPM DMRL	Bai, X., Fiske, S. T., & Griffiths, T. L. (2022). Globally inaccurate stereotypes can result from locally adaptive exploration. Psychological Science, 33(5) 671–684. (pdf)
RPM DMRL	Callaway, F., Jain, Y. R., van Opheusden, B., Das, P., Iwama, G., Gul, S., Krueger, P. M., Becker, F., Griffiths, T. L., & Lieder, F. (2022). Leveraging artificial intelligence to improve people’s planning strategies. Proceedings of the National Academy of Sciences, 119(12), e2117432119. (pdf)
RPM DMRL	Callaway, F., van Opheusden, B., Gul, S., Das, P., Krueger, P. M., Griffiths, T. L., & Lieder, F. (2022). Rational use of cognitive resources in human planning. Nature Human Behaviour, 6, 1–14. (pdf)
S&C	Chang, M., Griffiths, T. L., & Levine, S. (2022). Object representations as fixed points: Training iterative refinement algorithms with implicit differentiation. Advances in Neural Information Processing Systems, 36. (pdf)
S&C	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)
RPM S&C	Dasgupta, I., & Griffiths, T. L. (2022). Clustering and the efficient use of cognitive resources. Journal of Mathematical Psychology, 109, 102675. (pdf)
F DMRL	Dubey, R., Griffiths, T. L., & Dayan, P. (2022). The pursuit of happiness: A reinforcement learning perspective on habituation and comparisons. PLoS Computational Biology, 18(8), e1010316. (pdf)
E PR	Dubey, R., Griffiths, T. L., & Lombrozo, T. (2022). If it’s important, then I’m curious: Increasing perceived usefulness stimulates curiosity. Cognition, 226, 105193. (pdf)
CEIL	Gates, V., Suchow, J. W., & Griffiths, T. L. (2022). Memory transmission in small groups and large networks: An empirical study. Psychonomic Bulletin & Review, 29(2), 581-588. (pdf) (supplementary materials)
RPM CEIL	Hardy, M. D., Krafft, P. M., Thompson, B., & Griffiths, T. L. (2022). Overcoming Individual Limitations Through Distributed Computation: Rational Information Accumulation in Multigenerational Populations. Topics in Cognitive Science, 14(3), 550–573. (pdf)
RPM DMRL	Ho, M. K., Abel, D., Correa, C. G., Littman, M. L., Cohen, J. D., & Griffiths, T. L. (2022). People construct simplified mental representations to plan. Nature, 606(7912), 129-136. (pdf)
F DMRL	Ho, M. K., & Griffiths, T. L. (2022). Cognitive science as a source of forward and inverse models of human decisions for robotics and control. Annual Review of Control, Robotics, and Autonomous Systems, 5, 33-53. (pdf)
SML DMRL	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)
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)
CEIL	Morgan, T. J., Suchow, J. W., & Griffiths, T. L. (2022). The experimental evolution of human culture: flexibility, fidelity and environmental instability. Proceedings of the Royal Society B, 289(1986), 20221614. (pdf)
P SML	Murthy, S. K., Hawkins, R. D., & Griffiths, T. L. (2022). Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task. Cognition, 225, 105152. (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)
RPM DMRL	Russek, E., Acosta-Kane, D., van Opheusden, B., Mattar, M. G., & Griffiths, T. (2022). Time spent thinking in online chess reflects the value of computation. (preprint)
SML DMRL	Sumers, T. R., Hawkins, R. D., Ho, M. K., Griffiths, T. L., & Hadfield-Menell, D. (2022). How to talk so AI will learn: Instructions, descriptions, and autonomy. Advances in Neural Information Processing Systems, 36. (pdf)
CEIL	Thompson, B., van Opheusden, B., Sumers, T., & Griffiths, T. L. (2022). Complex cognitive algorithms preserved by selective social learning in experimental populations. Science, 376(6588), 95-98. (pdf)
SML CEIL	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)
PR SML	Barnett, S. A., Griffiths, T. L., Hawkins, R. D. (2022). A pragmatic account of the weak evidence effect. Open Mind, 6, 169-182. (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)
RPM DMRL	Callaway, F., Rangel, A., & Griffiths, T. L. (2021). Fixation patterns in simple choice reflect optimal information sampling. PLOS Computational Biology, 17(3), e1008863. (pdf)
RPM S&C	Devraj, A., Zhang, Q., & Griffiths, T.L. (2021). The dynamics of exemplar and prototype representations depend on environmental statistics. Proceedings of the 43rd Annual Conference of the Cognitive Science Society. (pdf)
F E DMRL	Dubey, R., Ho, M. K., Mehta, H., & Griffiths, T. L. (2021). Aha! Moments correspond to meta-cognitive prediction errors. (preprint)
PR DMRL	Gates, V., Callaway, F., Ho, M. K., Griffiths, T. (2021). A rational model of people's inferences about others' preferences based on response times. Cognition, 217, 104885. (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)
PR SML	Hawkins, R. D., Liu, I., Goldberg, A. E., Griffiths, T. L. (2021). Respect the code: Speakers expect novel conventions to generalize within but not across social group boundaries. Proceedings of the 43rd Annual Conference of the Cognitive Science Society. (pdf)
PR	Jansen, R. A., Rafferty, A. N., & Griffiths, T.L. (2021) A rational model of the Dunning–Kruger effect supports insensitivity to evidence in low performers. Nature Human Behavior. (pdf)
PR CEIL	Krafft, P. M., Shmueli, E., Griffiths, T. L., & Tenenbaum, J. B. (2021). Bayesian collective learning emerges from heuristic social learning. Cognition, 212, 104469. (pdf)
DMRL	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)
P CEIL	Langlois, T. A., Jacoby, N., Suchow, J. W., & Griffiths, T. L. (2021). Serial reproduction reveals the geometry of visuospatial representations. Proceedings of the National Academy of Sciences, 118(13), e2012938118. (pdf)
P	Langlois, T. A., Zhao, H. C., Grant, E., Dasgupta, I., Griffiths, T. L., & Jacoby, N. (2021). Passive attention in artificial neural networks predicts human visual selectivity. Advances in Neural Information Processing Systems, 34. (pdf)
CD	Lewry, C., Curtis, K., Vasilyeva, N., Xu, F., & Griffiths, T. L. (2021). Intuitions about magic track the development of intuitive physics. Cognition, 214, 104762. (pdf)
SML	Meylan, S. C., & Griffiths, T. L. (2021). The Challenges of Large-Scale, Web-Based Language Datasets: Word Length and Predictability Revisited. Cognitive Science, 45(6), e12983. (pdf)
SML	Meylan, S. C., Nair, S., & Griffiths, T. L. (2021). Evaluating models of robust word recognition with serial reproduction. Cognition, 210, 104553. (pdf)
RPM DMRL	Milli, S., Lieder, F., & Griffiths, T. L. (2021). A rational reinterpretation of dual-process theories. Cognition, 217, 104881. (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)
SML DMRL	Sumers, T. R., Hawkins, R. D., Ho, M. K., & Griffiths, T. L. (2021). Extending rational models of communication from beliefs to actions. Proceedings of the 43rd Annual Meeting of the Cognitive Science Society. (pdf)
SML DMRL	Sumers, T. R., Ho, M. K., Hawkins, R. D., Narasimhan, K. R., & Griffiths, T. L. (2021). Learning rewards from linguistic feedback. Proceedings of the 35th AAAI Conference on Artificial Intelligence. (pdf)
CEIL	Thompson, B., & Griffiths, T. L. (2021). Human biases limit cumulative innovation. Proceedings of the Royal Society B, 288, 20202752. (pdf)
P	Tuli, S., Dasgupta, I., Grant, E., & Griffiths, T. L. (2021). Are Convolutional Neural Networks or Transformers more like human vision?. Proceedings of the 43rd Annual Meeting of the Cognitive Science Society. (link)
PR RPM	Wilson, S., Arora, S., Zhang, Q., & Griffiths, T.L. (2021). A rational account of anchor effects in hindsight bias. Proceedings of the 43th Annual Conference of the Cognitive Science Society. (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)
DMRL	Alon, N., Cohen, J. D., Griffiths, T. L., Manurangsi, P., Reichman, D., Shinkar, I., Wagner, T., Yu, A. (2020). Multitasking capacity: Hardness results and improved constructions. SIAM Journal on Discrete Mathematics, 34(1), 885-903. (pdf)
PR NBM	Battleday, R. M., & Griffiths, T. L. (2020). Analogy as nonparametric Bayesian inference over relational systems. (preprint)
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)
S&C	Bourgin, D., Abbott, J. T., & Griffiths, T. L. (2021). Recommendation as generalization: Using big data to evaluate cognitive models. Journal of Experimental Psychology: General, 150, 1398–1409. (pdf)
RPM DMRL	Callaway, F., Hardy, M., & Griffiths, T. L. (2020). Optimal nudging. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pdf)
DMRL	Chang, M., Kaushik, S., Weinberg, S. M., Griffiths, T., & Levine, S. (2020). Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. Proceedings of the International Conference on Machine Learning. (pdf)
RPM DMRL	Correa, C. G., Ho, M. K., Callaway, F., & Griffiths, T. L. (2020). Resource-rational Task Decomposition to Minimize Planning Costs. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pdf)
E DMRL	Dubey, R., & Griffiths, T. L. (2020). Reconciling novelty and complexity through a rational analysis of curiosity. Psychological Review, 127(3), 455-476. (pdf)
DMRL	Dubey, R., Grant, E., Luo, M., Narasimhan, K. R., & Griffiths, T. L. (2020). Connecting context-specific adaptation in humans to meta-learning. (preprint)
F DMRL	Gates, V., Griffiths, T. L., & Dragan, A. D. (2020). How to be helpful to multiple people at once. Cognitive Science, 44(6), e12841. (pdf)
F RPM	Griffiths, T. L. (2020). Understanding human intelligence via human limitations. Trends in Cognitive Sciences, 24(11), 873-883. (pdf)
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)
SML CEIL	Hawkins, R. D., Goodman, N. D., Goldberg, A. E., & Griffiths, T. L. (2020). Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pdf)
DMRL	Ho, M. K., Abel, D., Cohen, J. D., Littman, M. L., & Griffiths, T. L. (2020). The Efficiency of Human Cognition Reflects Planned Information Processing. Proceedings of the 34th AAAI Conference on Artificial Intelligence. (pdf)
E PR	Jansen, R. A., Rafferty, A. N., & Griffiths, T. L. (2020). A rational model of sequential self-assessment. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (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)
RPM	Lieder, F., & Griffiths, T. L. (2020). Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences, 43, e1. (pdf) (Response to Commentaries)
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	Morgan, T. J., Suchow, J. W., & Griffiths, T. L. (2020). Experimental evolutionary simulations of learning, memory and life history. Philosophical Transactions of the Royal Society B, 375, 20190504. (pdf)
CEIL	Morgan, T. J., Suchow, J. W., & Griffiths, T. L. (2020). What the Baldwin Effect affects depends on the nature of plasticity. Cognition, 197, 104165. (pdf)
DMRL	Mormann, M., Griffiths, T. L., Janiszewski, C., Russo, J. E., Aribarg, A., Ashby, N. J., Bagchi, R., Bhatia, S., Kovacheva, M. M., & Mrkva, K. J. (2020). Time to pay attention to attention: using attention-based process traces to better understand consumer decision-making. Marketing Letters, 31, 381-392. (pdf)
SML	Nematzadeh, A., Shekarchi, Z., Griffiths, T. L., & Stevenson, S. (2020). Competition in Cross-situational Word Learning: A Computational Study. (preprint)
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)
E	Rafferty, A. N, Jansen, R. A., Griffiths, T. L. (2020). Assessing mathematics misunderstandings via Bayesian inverse planning. Cognitive Science. (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 DMRL	Sumers, T. R., Ho, M. K., & Griffiths, T. L. (2020). Show or tell? Demonstration is more robust to changes in shared perception than explanation. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pdf)
PR DMRL	Hardy, M., & Griffiths, T. L. (2019). Demonstrating the impact of prior knowledge in risky choice. (preprint)
PR NBM	Jerfel, G., Grant, E. L., Griffiths, T. L., & Heller, K. (2019). Reconciling meta-learning and continual learning with online mixtures of tasks. Advances in Neural Information Processing Systems, 32. (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)
E	Jupyter, P., Blank, D., Bourgin, D., Brown, A., Bussonnier, M., Frederic, J., Granger, B., Griffiths, T. L., Hamrick, J., Kelley, K., Pacer, M., Page, L., Perez, F., Ragan-Kelley, B., Suchow, J. W., & Willing, C. (2019). nbgrader: A tool for creating and grading assignments in the Jupyter notebook. Journal of Open Source Education, 2(11), 32. (pdf)
DMRL	Lieder, F., Chen, O. X., Krueger, P. M., & Griffiths, T. L. (2019). Cognitive prostheses for goal achievement. Nature Human Behaviour, 3(10), 1096-1106. (pdf)
S&C	Austerweil, J. L., Sanborn, S., & Griffiths, T. L. (2019). Learning how to generalize. Cognitive Science, 43(8), e12777. (pdf)
F DMRL	Ho, M. K., Abel, D., Griffiths, T. L., & Littman, M. L. (2019). The value of abstraction. Current Opinion in Behavioral Sciences, 29, 111-116. (pdf)
F RPM	Griffiths, T. L., Callaway, F., Chang, M. B., Grant, E., Krueger, P. M., & Lieder, F. (2019). Doing more with less: meta-reasoning and meta-learning in humans and machines. Current Opinion in Behavioral Sciences, 29, 24-30. (pdf)
PR S&C	Hsu, A. S., Martin, J. B., Sanborn, A. N., & Griffiths, T. L. (2019). Identifying category representations for complex stimuli using discrete Markov chain Monte Carlo with people. Behavior Research Methods, 51, 1706-1716. (pdf)
DMRL	Carroll, M., Shah, R., Ho, M. K., Griffiths, T., Seshia, S., Abbeel, P., & Dragan, A. (2019). On the Utility of Learning about Humans for Human-AI Coordination. In H. Wallach, H. Larochelle, A. Beygelzimer, F. Alché-Buc, E. Fox, & R. Garnett (Eds.), Advances in Neural Information Processing Systems, 32, 5174–5185. (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)
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)
E	Dubey, R., Griffiths, T. L., & Lombrozo, T. (2019). If it’s important, then I am curious: A value intervention to induce curiosity. Proceedings of the 41st Annual Conference of the Cognitive Science Society. (pdf)
RPM DMRL	Chang, M. B., Gupta, A., Levine, S., & Griffiths, T. L. (2019). Automatically composing representation transformations as a means for generalization. Proceedings of the 7th International Conference on Learning Representations (ICLR) 2019. (pdf)
CEIL	Thompson, B., & Griffiths, T. L. (2019). Inductive biases constrain cumulative cultural evolution. Proceedings of the 41st Annual Conference of the Cognitive Science Society. (pdf)
PR DMRL	Ho, M. K., Korman, J., & Griffiths, T. L. (2019). The computational structure of unintentional meaning. 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)
DMRL	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)
PR	Griffiths, T. L., Daniels, D., Austerweil, J. L., & Tenenbaum, J. B. (2018). Subjective randomness as statistical inference. Cognitive Psychology, 103, 85-109. (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)
RPM DMRL	Reichman, D., Lieder, F., Bourgin, D. D., Talmon, N., & Griffiths, T. L. (2018). The computational challenges of pursuing multiple goals: Network structure of goal systems predicts human performance. (preprint)https://psyarxiv.com/fqh3x/
RPM DMRL	Lieder, F., Shenhav, A., Musslick, S., & Griffiths, T.L. (2018). Rational metareasoning and the plasticity of cognitive control. PLoS Computational Biology, 14, e1006043. (pdf)
PR RPM DMRL	Lieder, F., Griffiths, T. L., Huys, Q. J. M., & Goodman, N. D. (2018). Empirical evidence for resource-rational anchoring and adjustment. Psychonomic Bulletin & Review, 25, 775-784. (pdf)
PR RPM DMRL	Lieder, F., Griffiths, T. L., Huys, Q. J. M., & Goodman, N. D. (2018). The anchoring bias reflects rational use of cognitive resources. Psychonomic Bulletin & Review, 25, 322-349. (pdf)
PR RPM DMRL	Lieder, F., Griffiths, T. L., & Hsu, M (2018). Over-representation of extreme events in decision making reflects rational use of cognitive resources. Psychological Review, 125(1), 1-32. (pdf)
CEIL	Whalen, A., Griffiths, T. L., & Buchsbaum, D. (2018). Sensitivity to shared information in social learning. Cognitive Science, 42(1), 168-187. (pdf)
RPM DMRL	Callaway, F., Gul, S., Krueger, P. M., Griffiths, T. L., & Lieder, F. (2018). Learning to select computations. Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence. (pdf)
DMRL	Burns, K., Nematzadeh, A., Grant, E., Gopnik, A., & Griffiths, T. L. (2018). Exploiting attention to reveal shortcomings in memory models. Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 378-380. (pdf)
DMRL	Dubey, R., Agrawal, P., Pathak, D., Griffiths, T. L., & Efros, A. A. (2018). Investigating human priors for playing video games. In Proceedings of the 35th International Conference on Machine Learning (ICML 2018). (pdf) (project website)
SML	Nematzadeh, A., Burns, K., Grant, E., Gopnik, A., & Griffiths, T. L. (2018). Evaluating theory of mind in question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. (pdf)
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)
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)
DMRL	Krueger, P. M., & Griffiths, T. L. (2018). Shaping model-free habits with model-based goals. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pdf)
S&C	Bourgin, D. D., Abbott, J. T., & Griffiths, T. L. (2018). Recommendation as generalization: Evaluating cognitive models in the wild. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pdf)
F CEIL	Krafft, P. M., & Griffiths, T. L. (2018). Levels of analysis in computational social science. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pdf)
DMRL	Sanborn, S., Bourgin, D. D., Chang, M., & Griffiths, T. L. (2018). Representational efficiency outweighs action efficiency in human program induction. 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)
E	Jansen, R. A., Rafferty, A. N., & Griffiths, T. L. (2018). Modeling the Dunning-Kruger Effect: A rational account of inaccurate self assessment. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pdf)
RPM DMRL	Callaway, F., Lieder, F., Das, P., Gul, S., Krueger, P. M., & Griffiths, T. L. (2018). A resource-rational analysis of human planning. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pdf)
F	Alon, N., Reichman, D., Shinkar, I., Wagner, T., Musslick, S., Cohen, J. D., Griffiths, T. L., Dey, B., & Ozcimder, K. (2017). A graph-theoretic approach to multitasking. Advances in Neural Information Processing Systems, 2100-2109. (pdf)
SML	Meylan, S. C., & Griffiths, T. L. (2017). Word forms - not just their lengths - are optimized for efficient communication. (preprint)
SML	de Heer, W. A., Huth, A. G., Griffiths, T. L., Gallant, J. L., & Theunissen, F. E. (2017). The hierarchical cortical organization of human speech processing. Journal of Neuroscience, 37(27), 6539-6557. (pdf)
PR RPM DMRL	Lieder, F., & Griffiths, T. L. (2017). Strategy selection as rational metareasoning. Psychological Review, 124(6), 762-794. (pdf)
F	Paxton, A., & Griffiths, T. L.(2017). Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets. Behavior Research Methods, 49(5), 1630-1638.(pdf)
F CEIL	Suchow, J. W., Bourgin, D. D., & Griffiths, T. L. (2017). Evolution in mind: Evolutionary dynamics, cognitive processes, and Bayesian inference. Trends in Cognitive Sciences, 21(7), 522-530. (pdf)
CI	Griffiths, T. L. (2017). Formalizing prior knowledge in causal induction. The Oxford Handbook of Causal Reasoning. Oxford: Oxford University Press. (book)
CI RPM	Bramley, N. R., Dayan, P., Griffiths, T. L., & Lagnado, D. A. (2017). Formalizing Neuraths Ship: Approximate algorithms for online causal learning. Psychological Review, 124(3), 301-338. (pdf)
P	Austerweil, J. L., Griffiths, T. L., & Palmer, S. E. (2017). Learning to be (in) variant: Combining prior knowledge and experience to infer orientation invariance in object recognition. Cognitive Science, 41(S5), 1183-1201. (pdf)
RPM	Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T. L., Cohen, J. D., & Botvinick, M. M. (2017). Toward a rational and mechanistic account of mental effort. Annual Review of Neuroscience.(pdf)
SML CEIL	Whalen, A., & Griffiths, T. L. (2017). Adding population structure to models of language evolution by iterated learning. Journal of Mathematical Psychology, 76, 1-6. (pdf)
PR DMRL	Fisac, J. F., Gates, M. A., Hamrick, J. B., Liu, C., Hadfield-Menell, D., Palaniappan, M., Malik, D., Sastry, S. S., Griffiths, T. L., & Dragan, A. D. (2017). Pragmatic-Pedagogic Value Alignment. International Symposium on Robotics Research. (pdf)
CD	Gopnik, A., O'Grady, S., Lucas, C. G., Griffiths, T. L., Wente, A., Bridgers, S., Aboody, R., Fung, H., & Dahl, R. E. (2017). Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood. Proceedings of the National Academy of Sciences, 114(30), 7892-7899. (pdf)
PR	Meng, Y., Griffiths, T. L., & Xu, F. (2017). Inferring intentional agents from violation of randomness. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
P CEIL	Langlois, T. A., Jacoby, N., Suchow, J. W., & Griffiths, T. L. (2017). Uncovering visual priors in spatial memory using serial reproduction. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
RPM	Bourgin, D. D., Lieder, F., Reichman, D., Talmon, N., & Griffiths, T. L. (2017). The structure of goal systems predicts human performance. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
E PR	Dubey, R., & Griffiths, T. L. (2017). A rational analysis of curiosity. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
CD SML	Grant, E., Nematzadeh, A., & Griffiths, T. L. (2017). How can memory-augmented neural networks pass a false-belief task? Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
SML	Nematzadeh, A., Meylan, S. C., & Griffiths, T. L. (2017). Evaluating vector-space models of word representation, or the unreasonable effectiveness of counting words near other words. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
E	Jansen, R. A., Rafferty, A. N., & Griffiths, T. L. (2017). Algebra is not like trivia: Evaluating self-assessment in an online math tutor. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
DMRL	Krueger, P. M., Lieder, F., & Griffiths, T. L. (2017). Enhancing metacognitive reinforcement learning using reward structures and feedback. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
RPM DMRL	Lieder, F., Krueger, P. M., & Griffiths, T. L. (2017). An automatic method for discovering rational heuristics for risky choice. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
P CI	Callaway, F., Hamrick, J. B., & Griffiths, T. L. (2017). Discovering simple heuristics from mental simulation. Proceedings of the 39th 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)
CEIL	Gates, M. A., Suchow, J. W., & Griffiths, T. L. (2017). Empirical tests of large-scale collaborative recall. 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)
PR RPM	Milli, S., Lieder, F., & Griffiths, T. L. (2017) When does bounded-optimal metareasoning favor few cognitive systems? Proceedings of the 31st AAAI Conference on Artificial Intelligence. (pdf)
SML	Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E., & Gallant, J. L. (2016). Natural speech reveals the semantic maps that tile the human cerebral cortex. Nature, 532 453-458. (pdf)
P CI PR	Hamrick, J. B., Battaglia, P. W., Griffiths, T. L., Tenenbaum, J. B. (2016). Inferring mass in complex scenes by mental simulation. Cognition, 157, 61-76. (pdf)
CD PR	Ruggeri, A., Lombrozo, T., Griffiths, T. L., & Xu, F. (2016). Sources of developmental change in the efficiency of information search. Developmental Psychology, 52, 2159-2173. (pdf)
PR SML	Hsu, A., & Griffiths, T. L. (2016). Sampling assumptions affect use of indirect negative evidence in language learning PLOS One, 11(6). (pdf)
F P	Griffiths, T. L., Abbott, J. T., & Hsu, A. S. (2016). Exploring human cognition using large image databases. Topics in Cognitive Science, 8(3), 569-588. (pdf)
SML	Cibelli, E., Xu, Y., Austerweil, J. L., Griffiths, T. L., & Regier, T. (2016). The Sapir-Whorf Hypothesis and probabilistic inference: Evidence from the domain of color. PLOS One, 11(7). (pdf)
CD PR	Eaves Jr, B. S., Feldman, N. H., Griffiths, T. L., & Shafto, P. (2016). Infant-directed speech is consistent with teaching. Psychological Review (pdf)
RPM	Hsu, A. S., Horng, A., Griffiths, T. L., & Chater, N. (2016). When absence of evidence is evidence of absence: Rational inferences from absent data. Cognitive Science, 1-13. (pdf)
CI CD	Bridgers, S., Buchsbaum, D., Seiver, E., Griffiths, T. L., & Gopnik, A. (2016). Children's causal inferences from conflicting testimony and observations. Developmental Psychology. (pdf)
PR	Fisac, J. F., Liu, C., Hamrick, J. B., Sastry, S., Hedrick, J. K., Griffiths, T. L., & Dragan, A. D. (2016). Generating plans that predict themselves. In Proceedings of the 12th International Workshop on the Algorithmic Foundations of Robotics (WAFR 2016). (pdf)
F CEIL	Suchow, J. W., & Griffiths, T. L. (2016). Rethinking experiment design as algorithm design. CrowdML – NIPS '16 Workshop on Crowdsourcing and Machine Learning. (pdf)
RPM	Lieder, F., & Griffiths, T. L. (2016). Helping people make better decisions using optimal gamification Proceedings of the 38th Annual Conference of the Cognitive Science Society. (pdf)
E	Rafferty, A. N., Jansen, R. A, & Griffiths, T. L. (2016). Using inverse planning for personalized feedback. Proceedings of the 9th International Conference on Educational Data Mining, 472-477. (pdf)
P S&C	Abbott, J. T., Griffiths, T. L., & Regier, T. (2016). Focal colors across languages are representative members of color categories. Proceedings of the National Academy of Sciences, 113(40), 11178-11183. (pdf)
PR	O'Grady, S., Griffiths, T. L., & Xu, F. (2016). Do simple probability judgements rely on integer approximation? Proceedings of the 38th Annual Conference of the Cognitive Science Society. (pdf)
CD	Foushee, R., Griffiths, T. L., & Srinivasan, M. (2016). Lexical complexity of child-directed and overheard speech: Implications for learning. Proceedings of the 38th Annual Conference of the Cognitive Science Society. (pdf)
P CEIL	Suchow, J. W., Pacer, M. D., & Griffiths, T. L. (2016). Design from Zeroth Principles. Proceedings of the 38th Annual Conference of the Cognitive Science Society. (pdf)
PR RPM	Suchow, J. W., & Griffiths, T. L. (2016). Deciding to remember: Memory maintenance as a Markov Decision Process. Proceedings of the 38th 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)
P PR	Liu, C., Hamrick, J. B., Fisac, J. F., Dragan, A. D, Hendrick, J. K., Sastry, S. S, & Griffiths, T. L. (2016). Goal inference improves objective and perceived performance in human-robot collaboration. Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems. (pdf)
CD PR	Hu, J., Lucas, C. G., Griffiths, T. L., & Xu, F. (2015). Preschoolers' understanding of graded preferences. Cognitive Development, 36, 93-102. (pdf)
E S&C	Rafferty, A. N., Brunskill, E., Griffiths, T. L., & Shafto, P. (2015). Faster teaching via POMDP planning. Cognitive Science. (pdf)
F	Sanborn, A. N., & Griffiths, T. L. (2015). Exploring the structure of mental representations by implementing computer algorithms with people. In Raaijmakers, J. G. W., Criss, A. H., Goldstone, R. L., Nosofsky, R. M., & Steyvers, M. (Eds.). Cognitive Modeling in Perception and Memory: A Festschrift for Richard M. Shiffrin. New York: Psychology Press. (pdf)
RPM SML	Abbott, J. T., Austerweil, J. L., & Griffiths, T. L. (2015). Random walks on semantic networks can resemble optimal foraging. Psychological Review, 122, 558-569. (pdf)
F RPM	Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015). Rational use of cognitive resources: Levels of analysis between the computational and the algorithmic. Topics in Cognitive Science, 7, 217-229. (pdf)
PR	Rafferty, A. N., LaMar, M. M., & Griffiths, T. L. (2015). Inferring learners' knowledge from their actions. Cognitive Science, 39, 584-618. (pdf)
F	Goodman, N. D., Frank, M. C., Griffiths, T. L., Tenenbaum, J. B., Battaglia, P., & Hamrick, J. B. (2015). Relevant and robust. A response to Marcus and Davis. Psychological Science, 26, 539-541. (pdf)
F	Griffiths, T. L. (2015). Manifesto for a new (computational) cognitive revolution. Cognition, 135, 21-23. (pdf)
CD RPM	Gopnik, A., Griffiths, T. L., & Lucas, C. G. (2015). When younger learners can be better (or at least more open-minded) than older ones. Current Directions in Psychological Science, 24, 87-92. (pdf)
CI NBM	Lucas, C. G., Griffiths, T. L., Williams, J. J., & Kalish, M. L. (2015). A rational model of function learning. Psychonomic Bulletin and Review. (pdf)
CI CEIL	Yeung, S., & Griffiths, T. L. (2015). Identifying expectations about the strength of causal relationships. Cognitive Psychology, 76, 1-29. (pdf)
CI SML NBM	Buchsbaum, D., Griffiths, T. L., Plunkett, D., Gopnik, A., & Baldwin, D. (2015). Inferring action structure and causal relationships in continuous sequences of human action. Cognitive Psychology, 76, 30-77. (pdf)
PR S&C	Griffiths, T. L. (2015). Revealing ontological commitments by magic. Cognition, 136, 43-48. (pdf)
E	Rafferty, A. N., & Griffiths, T. L. (2015). Interpreting freeform equation solving. Proceedings of the 17th International Conference on Artificial Intelligence in Education. (pdf)
P RPM	Hamrick, J., Smith, K. A., Griffiths, T. L., & Vul, E. (2015). Think again? The amount of mental simulation tracks uncertainty in the outcome. Proceedings of the 37th Annual Conference of the Cognitive Science Society (pdf)
CD	Hu, J., Whalen, A., Buchsbaum, D., Griffiths, T. L., & Xu, F. (2015). Can children balance the size of a majority with the quality of their information? Proceedings of the 37th Annual Conference of the Cognitive Science Society. (pdf)
PR RPM	Lieder, F., & Griffiths, T. L. (2015). When to use which heuristic: A rational solution to the strategy selection problem. Proceedings of the 37th Annual Conference of the Cognitive Science Society. (pdf)
CD PR	Lieder, F., Sim, Z., Hu, J. C., & Griffiths, T. L. (2015). Children and adults differ in their strategies for social learning. Proceedings of the 37th Annual Conference of the Cognitive Science Society. (pdf)
CD SML	Meylan, S. C., & Griffiths, T. L. (2015). A Bayesian framework for learning words from multiword utterances. Proceedings of the 37th Annual Conference of the Cognitive Science Society. (pdf)
CEIL	Morgan, T. J. H, & Griffiths, T. L. (2015). What the Baldwin Effect affects. Proceedings of the 37th Annual Conference of the Cognitive Science Society. (pdf)
CI	Pacer, M. D., & Griffiths, T. L. (2015). Upsetting the contingency table: Causal induction over sequences of point events. Proceedings of the 37th Annual Conference of the Cognitive Science Society. (pdf)
CI CD	Ruggeri, A., Lombrozo, T., Griffiths, T. L., & Xu, F. (2015). Children search for information as efficiently as adults, but seek additional confirmatory evidence. Proceedings of the 37th Annual Conference of the Cognitive Science Society. (pdf)
RPM	Lieder, F., Plunkett, D., Hamrick, J. B., Russell, S. J., Hay, N. J., & Griffiths, T. L. (2014). Algorithm selection by rational metareasoning as a model of human strategy selection. Advances in Neural Information Processing Systems, 27. (pdf)
SML CEIL	Rafferty, A. N., Griffiths, T. L., & Klein, D. (2014). Analyzing the rate at which languages lose the influence of a common ancestor. Cognitive Science, 38, 1406-1431. (pdf)
CI CD PR	Bonawitz, E., Denison, S., Griffiths, T. L., & Gopnik, A. (2014). Probabilistic models, learning algorithms, and response variability: Sampling in cognitive development. Trends in Cognitive Sciences, 18, 497-500. (pdf)
CEIL	Kirby, S., Griffiths, T. L., & Smith, K. (2014). Iterated learning and the evolution of language. Current Opinion in Neurobiology, 28, 108-114. (pdf)
CI RPM	Bonawitz, E., Denison, S., Gopnik, A., & Griffiths, T. L. (2014). Win-stay, lose-sample,: A simple sequential algorithm for approximating Bayesian inference. Cognitive Psychology, 74, 35-65. (pdf)
PR RPM	Vul, E., Goodman, N. D., Tenenbaum, J. B., & Griffiths, T. L. (2014). One and done? Optimal decisions from very few samples. Cognitive Science, 38, 599-637. (pdf)
S&C CEIL	Canini, K. R., Griffiths, T. L., Vanpaemel, W., & Kalish, M. L. (2014). Revealing human inductive biases for category learning by simulating cultural transmission. Psychonomic Bulletin & Review, 21, 785-793. (pdf)
CI CD	Lucas, C. G., Bridgers, S., Griffiths, T. L., & Gopnik, A. (2014). When children are better (or at least more open-minded) learners than adults: Developmental differences in learning the forms of causal relationships. Cognition, 131, 284-299. (pdf)
CD PR	Lucas, C. G., Griffiths, T. L., Xu, F., Fawcett, C., Gopnik, A., Kushnir, T., Markson, L., & Hu, J. (2014). The child as econometrician: A rational model of preference understanding in children. PLOS One, 9(3), e92160. (pdf)
PR S&C	Shafto, P., Goodman, N. D., & Griffiths, T. L. (2014). A rational account of pedagogical reasoning: Teaching by, and learning from, examples. Cognitive Psychology, 71, 55-89. (pdf)
S&C	Rafferty, A. N., Zaharia, M., & Griffiths, T. L. (2014). Optimally designing games for behavioural research. Proceedings of the Royal Society Series A, 470. (pdf)
SML CEIL	Maurits, L., & Griffiths, T. L. (2014). Tracing the roots of syntax with Bayesian phylogenetics. Proceedings of the National Academy of Sciences, 111, 13576-13581. (pdf)
PR	Bertolero, M. A., & Griffiths, T. L. (2014). Is holism a problem for inductive inference? A computational analysis. Proceedings of the 36th Annual Conference of the Cognitive Science Society. (pdf)
RPM SML	Bourgin, D. D., Abbott, J. T., Griffiths, T. L., Smith, K. A., & Vul, E. (2014). Empirical evidence for Markov chain Monte Carlo in memory search. Proceedings of the 36th Annual Conference of the Cognitive Science Society. (pdf)
PR RPM	Hamrick, J. B., & Griffiths, T. L. (2014). What to simulate? Inferring the right direction for mental rotation. Proceedings of the 36th Annual Conference of the Cognitive Science Society. (pdf)
PR RPM	Lieder, F., Hsu, M., & Griffiths, T. L. (2014). The high availability of extreme events serves resource-rational decision-making. Proceedings of the 36th Annual Conference of the Cognitive Science Society. (pdf)
RPM	Neumann, R., Rafferty, A. N., & Griffiths, T. L. (2014). A bounded rationality account of wishful thinking. Proceedings of the 36th Annual Conference of the Cognitive Science Society. (pdf)
RPM	Press, A., Pacer, M., Griffiths, T. L., & Christian, B. (2014). Caching algorithms and rational models of memory. Proceedings of the 36th Annual Conference of the Cognitive Science Society. (pdf)
S&C CEIL	Whalen, A., Maurits, L., Pacer, M., & Griffiths, T. L. (2014). Cultural evolution with sparse testimony: When does the cultural ratchet slip? Proceedings of the 36th Annual Conference of the Cognitive Science Society. (pdf)
SML NBM	Feldman, N. H., Griffiths, T. L., Goldwater, S., & Morgan, J. (2013). A role for the developing lexicon in phonetic category acquisition. Psychological Review, 120, 751-778. (pdf)
SML CEIL	Rafferty, A. N., Griffiths, T. L., & Ettlinger, M. (2013). Greater learnability is not sufficient to produce cultural universals. Cognition, 129, 70-87. (pdf)
PR	Williams, J. J., & Griffiths, T. L. (2013). Why are people bad at detecting randomness? A statistical argument. Journal of Experimental Psychology: Learning, Memory & Cognition, 39, 1473-1490. (pdf)
P S&C	Jia, Y., Abbott, J. T., Austerweil, J. L., Griffiths, T. L., & Darrell, T. (2013). Visual concept learning: Combining machine vision and Bayesian generalization on concept hierarchies. Advances in Neural Information Processing Systems, 26. (pdf)
S&C NBM	Austerweil, J., & Griffiths, T. L. (2013). A nonparametric Bayesian framework for constructing flexible feature representations. Psychological Review, 120, 817-851. (pdf)
S&C SML	Feldman, N. H., Myers, E. B., White, K. S., Griffiths, T. L., & Morgan, J. L. (2013). Word-level information influences phonetic learning in adults and infants. Cognition, 127, 427-438. (pdf)
P CI	Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (2013). Reconciling intuitive physics and Newtonian mechanics for colliding objects. Psychological Review, 120, 411-437. (pdf)
PR	Schlerf, J., Xu, J., Klemfuss, N., Griffiths, T. L., & Ivry, R. B. (2013). Individuals with cerebellar degeneration show similar adaptation deficits with large and small visuomotor errors. Journal of Neurophysiology, 109, 1164-1173. (pdf)
CEIL	Griffiths, T. L., Lewandowsky, S., & Kalish, M. L. (2013). The effects of cultural transmission are modulated by the amount of information transmitted. Cognitive Science, 37, 953-967. (pdf)
CI RPM	Denison, S., Bonawitz, E., Gopnik, A., & Griffiths, T. L. (2013). Rational variability in children's causal inferences: The Sampling Hypothesis. Cognition, 126, 285-300. (pdf)
PR RPM	Abbott, J. T., Hamrick, J. B., & Griffiths, T. L. (2013). Approximating Bayesian inference with a sparse distributed memory system. Proceedings of the 35th Annual Conference of the Cognitive Science Society. (pdf)
CI CEIL	Hu, J.. C, Buchsbaum, D., Griffiths, T. L., & Xu, F. (2013). When does the majority rule? Preschoolers' trust in majority informants varies by task domain. Proceedings of the 35th Annual Conference of the Cognitive Science Society. (pdf)
PR CEIL	Whalen, A., Buchsbaum, D., & Griffiths, T. L. (2013). How do you know that? Sensitivity to statistical dependency in social learning. Proceedings of the 35th Annual Conference of the Cognitive Science Society. (pdf)
CI PR	Pacer, M., Williams, J., Xi, C., Lombrozo, T., & Griffiths, T. L. (2013). Evaluating computational models of explanation using human judgments. Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence. (pdf)
S&C CEIL	Xu, J., Dowman, M., & Griffiths, T. L. (2013) Cultural transmission results in convergence towards colour term universals. Proceedings of the Royal Society, Series B. (pdf)
SML CEIL	Bouchard-Cote, A., Hall, D., Griffiths, T. L., & Klein, D. (2013) Automated reconstruction of ancient languages using probabilistic models of sound change. Proceedings of the National Academy of Sciences. (pdf)
RPM SML	Abbott, J. T., Austerweil, J. L., & Griffiths, T. L. (2012). Human memory search as a random walk in a semantic network. Advances in Neural Information Processing Systems, 25. (pdf)
S&C	Martin, J. B., Griffiths, T. L., & Sanborn, A. N. (2012). Testing the efficiency of Markov chain Monte Carlo with people using facial affect categories. Cognitive Science, 36, 150-162. (pdf)
P S&C	Austerweil, J. L., & Griffiths, T. L. (2012). Human feature learning. Encyclopedia of the sciences of learning. N. M. Seel, ed. New York: Springer. (book)
PR RPM	Lieder, F., Griffiths, T. L., & Goodman, N. D. (2012). Burn-in, bias, and the rationality of anchoring. Advances in Neural Information Processing Systems, 25. (pdf)
CEIL	Bugnyar, T., Boyd, R., Bossan, B., Gächter, S., Griffiths, T., Hammerstein, P., Jensen, K., Mussweiler, T., Nagel, R., & Warneken, F. (2012). Evolutionary perspectives on social cognition. In P. Hammerstein & J. R. Stevens (Eds.) Evolution and the Mechanisms of Decision Making: Toward a Darwinian Decision Theory. Cambridge, MA: MIT Press. (book)
CD RPM	Bonawitz, E., Gopnik, A., Denison, S., & Griffiths, T. L. (2012). Rational randomness: The role of sampling in an algorithmic account of preschoolers' causal learning. In F. Xu (Ed.) Rational constructivism in cognitive development. Waltham, MA: Academic Press. (book)
F	Griffiths, T. L., Tenenbaum, J. B., & Kemp, C. (2012). Bayesian inference. In K. J. Holyoak & R. G. Morrison, (Eds.) Oxford Handbook of Thinking and Reasoning. Oxford: Oxford University Press. (book)
F	Griffiths, T. L., Chater, N., Norris, D., & Pouget, A. (2012). How the Bayesians got their beliefs (and what those beliefs actually are). Psychological Bulletin, 138, 415-422. (pdf)
S&C	Griffiths, T. L., & Austerweil, J. L. (2012). Bayesian generalization with circular consequential regions. Journal of Mathematical Psychology, 56, 281-285. (pdf)
F RPM	Griffiths, T. L., Vul, E., & Sanborn, A. N. (2012). Bridging levels of analysis for probabilistic models of cognition. Current Directions in Psychological Science, 21(4), 263-268. (pdf)
CI CD	Buchsbaum, D., Bridgers, S., Whalen, A., Seiver, E., Griffiths, T. L., & Gopnik, A. (2012). Do I know that you know what you know? Modeling testimony in causal inference. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf)
S&C CEIL	Hsu, A. S, Martin, J. B., Sanborn, A. N., & Griffiths, T. L. (2012). Identifying representations of categories of discrete items using Markov chain Monte Carlo with People. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf)
S&C	Rafferty, A. N., Zaharia, M., & Griffiths, T. L. (2012). Optimally Designing Games for Cognitive Science Research. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf)
S&C	Blundell, C., Sanborn, A. N., & Griffiths, T. L. (2012). Look-ahead Monte Carlo with people. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf)
PR S&C	Little, D., Lewandowsky, S., & Griffiths, T. L. (2012). A Bayesian model of rule induction in Raven's progressive matrices. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf)
F	Griffiths, T. L., Austerweil, J. L., & Berthiaume, V. G. (2012). Comparing the inductive biases of simple neural networks and Bayesian models. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf)
P S&C	Abbott, J. T., Regier, T., & Griffiths, T. L. (2012). Predicting focal colors with a rational model of representativeness. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf)
P S&C	Abbott, J. T., Austerweil, J. L., & Griffiths, T. L. (2012). Constructing a hypothesis space from the Web for large-scale Bayesian word learning. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf)
CI	Pacer, M., & Griffiths, T. L. (2012). Elements of a rational framework for continuous-time causal induction. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf)
PR	Griffiths, T. L., & Tenenbaum, J. B. (2011). Predicting the future as Bayesian inference: People combine prior knowledge with observations when estimating duration and extent. Journal of Experimental Psychology: General, 140, 725-743. (pdf)
CI CD	Griffiths, T. L., Sobel, D., Tenenbaum, J. B., & Gopnik, A. (2011). Bayes and blickets: Effects of knowledge on causal induction in children and adults. Cognitive Science, 35, 1407-1455. (pdf)
P NBM	Austerweil, J. L., Friesen, A. L., & Griffiths, T. L. (2011). An ideal observer model for identifying the reference frame of objects. Advances in Neural Information Processing Systems, 24. (pdf)
PR S&C	Abbott, J. T., Heller, K. A., Ghahramani, Z., & Griffiths, T. L. (2011). Testing a Bayesian measure of representativeness using a large image database. Advances in Neural Information Processing Systems, 24. (pdf)
CI	Pacer, M., & Griffiths, T. L. (2011). A rational model of causal induction with continuous causes. Advances in Neural Information Processing Systems, 24. (pdf)
P NBM	Austerweil, J. L., & Griffiths, T. L. (2011). A rational model of the effects of distributional information on feature learning. Cognitive Psychology, 63, 173-209. (pdf)
SML NBM	Goldwater, S., Griffiths, T. L., Johnson, M. (2011). Producing power-law distributions and damping word frequencies with two-stage language models. Journal of Machine Learning Research, 12, 2335-2382. (pdf)
F CD	Perfors, A., Tenenbaum, J. B., Griffiths, T. L., & Xu, F. (2011). A tutorial introduction to Bayesian models of cognitive development. Cognition, 120, 302-321. (pdf)
CI CD	Buchsbaum, D., Gopnik, A., Griffiths, T. L., & Shafto, P. (2011). Children's imitation of causal action sequences is influenced by statistical and pedagogical evidence. Cognition, 120, 331-340. (pdf)
PR	Austerweil, J. L., & Griffiths, T. L. (2011). Seeking confirmation is rational for deterministic hypotheses. Cognitive Science, 35, 499-526. (pdf)
NBM	Griffiths, T. L., & Ghahramani, Z. (2011). The Indian Buffet Process: An introduction and review. Journal of Machine Learning Research, 12, 1185-1224. (pdf)
S&C NBM	Griffiths, T. L., Sanborn, A. N., Canini, K. R., Navarro, D. J., & Tenenbaum, J. B. (2011). Nonparametric Bayesian models of category learning. In E. M. Pothos & A. J. W.ills (Eds.) Formal approaches in categorization. Cambridge, UK: Cambridge University Press. (book)
F CD	Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279-1285. (pdf)
SML	Feldman, N. H., Myers, E., White, K., Griffiths, T. L., & Morgan, J. L. (2011). Learners use word-level statistics in phonetic category acquisition. Proceedings of the 35th Boston University Conference on Language Development. (pdf)
SML	Griffiths, T. L. (2011). Rethinking language: How probabilities shape the words we use. Proceedings of the National Academy of Sciences, 108, 3825-3826. (pdf)
SML CEIL	Griffiths, T. L., & Reali, F. (2011). Modelling minds as well as populations. Proceedings of the Royal Society, Series B. (pdf)
E PR S&C	Rafferty, A. N., Brunskill, E. B., Griffiths, T. L., & Shafto, P. (2011). Faster teaching by POMDP planning. Proceedings of the 15th International Conference on Artificial Intelligence in Education (AIED2011). (pdf)
S&C NBM	Canini, K. R., & Griffiths, T. L. (2011). A nonparametric Bayesian model of multi-level category learning. Proceedings of the 25th AAAI Conference on Artificial Intelligence.(pdf)
CI CEIL	Yeung, S., & Griffiths, T. L. (2011). Estimating human priors on causal strength. Proceedings of the 33rd Annual Conference of the Cognitive Science Society. (pdf)
S&C CEIL	Canini, K. R., Griffiths, T. L., Vanpaemel, W., & Kalish, M. L. (2011). Discovering inductive biases in categorization through iterated learning. Proceedings of the 33rd Annual Conference of the Cognitive Science Society. (pdf)
CI RPM	Abbott, J. T., & Griffiths, T. L. (2011). Exploring the influence of particle filter parameters on order effects in causal learning. Proceedings of the 33rd Annual Conference of the Cognitive Science Society. (pdf)
PR	Waisman, A. S., Lucas, C. G., Griffiths, T. L., & Jacobs, L. F. (2011). A Bayesian model of navigation in squirrels. Proceedings of the 33rd Annual Conference of the Cognitive Science Society. (pdf)
P SML	Buchsbaum, D., Canini, K. R., & Griffiths, T. L. (2011). Segmenting and recognizing human action using low-level video features. Proceedings of the 33rd Annual Conference of the Cognitive Science Society.(pdf)
SML CEIL	Rafferty, A. N., Griffiths, T. L., & Ettlinger, M. (2011) Exploring the relationship between learnability and linguistic universals. Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics at ACL 2011. (pdf)
CI RPM	Bonawitz, E., Denison, S., Chen, A., Gopnik, A., & Griffiths, T. L. (2011). A simple sequential algorithm for approximating Bayesian inference. Proceedings of the 33rd Annual Conference of the Cognitive Science Society. (pdf)
SML NBM	Frank, M., Goldwater, S., Griffiths, T. L., & Tenenbaum, J. B. (2010). Modeling human performance in statistical word segmentation. Cognition, 117, 107-125.(pdf)
RPM S&C NBM	Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010). Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review, 117 (4), 1144-1167.(pdf)
PR RPM S&C	Shi, L., Griffiths, T. L., Feldman, N. H., & Sanborn, A. N. (2010). Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review, 17 (4), 443-464. (pdf)
P S&C NBM	Austerweil, J. L., & Griffiths, T. L. (2010). Learning invariant features using the Transformed Indian Buffet Process. Advances in Neural Information Processing Systems 23. (pdf)
PR	Hsu, A., Griffiths, T. L., & Schreiber, E. (2010). Subjective randomness and natural scene statistics. Psychonomic Bulletin & Review, 17, 624-629. (pdf)
F	Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences, 14, 357-364. (pdf)
SML	Rosen-Zvi, M., Chemudugunta, C., Griffiths, T. L., Smyth, P., & Steyvers, M. (2010). Learning author-topic models from text corpora. ACM Transactions on Information Systems, 28(1), Article 4. (pdf)
F	Griffiths, T. L. (2010). Bayesian models as tools for exploring inductive biases. In M. Banich & D. Caccamise (Eds.) Generalization of knowledge: Multidisciplinary perspectives. New York: Psychology Press.
SML CEIL	Burkett, D., & Griffiths, T. L. (2010). Iterated learning of multiple languages from multiple teachers. Evolang 8. (pdf)
SML NBM	Blei, D. M., Griffiths, T. L., & Jordan, M. I. (2010). The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. Journal of the ACM, 57, 1-30.(pdf)
CI NBM	Kemp, C., Tenenbaum, J. B., Niyogi, S., & Griffiths, T. L. (2010). A probabilistic model of theory formation. Cognition, 114, 165-196. (pdf)
CI	Lucas, C. G., & Griffiths, T. L. (2010). Learning the form of causal relationships using hierarchical Bayesian models. Cognitive Science, 34, 113-147. (pdf)
S&C CEIL	Xu, J., & Griffiths, T. L. (2010). A rational analysis of the effects of memory biases on serial reproduction. Cognitive Psychology, 60, 107-126. (pdf)
PR RPM S&C	Sanborn, A. N., Griffiths, T. L., & Shiffrin, R. (2010). Uncovering mental representations with Markov chain Monte Carlo. Cognitive Psychology, 60, 63-106. (pdf)
SML CEIL	Reali, F., & Griffiths, T. L. (2010). Words as alleles: Connecting language evolution with Bayesian learners to models of genetic drift. Proceedings of the Royal Society, Series B, 277, 429-436. (pdf)
P S&C CEIL	Xu, J., Griffiths, T. L., & Dowman, M. (2010). Replicating color term universals through human iterated learning. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf)
S&C	Hsu, A. S., & Griffiths, T. L. (2010). Effects of generative and discriminative learning on use of category variability. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf)
CD SML	Rafferty, A. N., & Griffiths, T. L. (2010). Optimal language learning: The importance of starting representative. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf)
CI CD	Buchsbaum, D., Gopnik, A., & Griffiths, T. L. (2010). Children's imitation of action sequences is influenced by statistical evidence and inferred causal structure. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf)
CI PR RPM	Bonawitz, E. B., & Griffiths, T. L. (2010). Deconfounding hypothesis generation and evaluation in Bayesian models. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf)
CI PR RPM	Denison, S., Bonawitz, E. B., Gopnik, A., & Griffiths, T. L. (2010). Preschoolers sample from probability distributions. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf)
P S&C	Austerweil, J. L., & Griffiths, T. L. (2010). Learning hypothesis spaces and dimensions through concept learning. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf)
CI CD	Lucas, C. G., Gopnik, A., & Griffiths, T. L. (2010). Developmental differences in learning the forms of causal relationships. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf)
S&C	Canini, K. R., Shashkov, M. M., & Griffiths, T. L. (2010). Modeling transfer learning in human categorization with the hierarchical Dirichlet process. Proceedings of the 27th International Conference on Machine Learning. (pdf)
SML	Hsu, A., & Griffiths, T. L. (2009). Differential use of implicit negative evidence in generative and discriminative language learning. Advances in Neural Information Processing Systems 22. (pdf)
NBM	Miller, K. T, Griffiths, T. L., & Jordan, M. I. (2009). Nonparametric latent feature models for link prediction. Advances in Neural Information Processing Systems 22. (pdf)
PR RPM	Shi, L., & Griffiths, T. L. (2009). Neural implementation of hierarchical Bayesian inference by importance sampling. Advances in Neural Information Processing Systems 22. (pdf)
PR CEIL	Lewandowsky, S., Griffiths, T. L., & Kalish, M. L. (2009). The wisdom of individuals: Exploring peoples knowledge about everyday events using iterated learning. Cognitive Science, 33, 969-998. (pdf)
P S&C SML	Feldman, N. H., Griffiths, T. L., & Morgan, J. L. (2009). The influence of categories on perception: Explaining the perceptual magnet effect as optimal statistical inference. Psychological Review, 116, 752-782. (pdf)
CI CD	Griffiths, T. L., & Tenenbaum, J. B. (2009). Theory-based causal induction. Psychological Review, 116, 661-716. (pdf)
CEIL	Jaeger, H., Baronchelli, A., Briscoe, T., Christiansen, M. H., Griffiths, T. L., Jager, G., Kirby, S., Komarova, N. L., Richerson, P. J., Steels, L., & Triesch, J (2009). What can mathematical, computational and robotic models tell us about the origins of syntax? In D. Bickerton & E. Szathmary (Eds.) Biological foundations and origins of syntax. Cambridge, MA: MIT Press.
F	Griffiths, T. L. (2009). Connecting human and machine learning via probabilistic models of cognition. InterSpeech 2009. (pdf)
CD SML	Goldwater, S., Griffiths, T. L., & Johnson, M. (2009). A Bayesian framework for word segmentation: Exploring the effects of context. Cognition, 112, 21-54. (pdf)
SML CEIL	Reali, F., & Griffiths, T. L. (2009). The evolution of linguistic frequency distributions: Relating regularization to inductive biases through iterated learning. Cognition, 111, 317-328. (pdf)
SML	Canini, K. R., Shi, L., & Griffiths, T. L. (2009). Online inference of topics with Latent Dirichlet Allocation. AISTATS. (pdf)
CD PR	Lucas, C., Griffiths, T. L., Xu, F., & Fawcett, C. (2009). A rational model of preference learning and choice prediction by children. Advances in Neural Information Processing Systems 21. (pdf)
CI S&C	Griffiths, T. L., Lucas, C., Williams, J. J., & Kalish, M. L. (2009). Modeling human function learning with Gaussian processes. Advances in Neural Information Processing Systems 21. (pdf)
RPM SML	Levy, R., Reali, F., & Griffiths, T. L. (2009). Modeling the effects of memory on human online sentence processing with particle filters. Advances in Neural Information Processing Systems 21. (pdf)
S&C CEIL	Xu, J., & Griffiths, T. L. (2009). How memory biases affect information transmission: A rational analysis of serial reproduction. Advances in Neural Information Processing Systems 21. (pdf)
P S&C NBM	Austerweil, J., & Griffiths, T. L. (2009). Analyzing human feature learning as nonparametric Bayesian inference. Advances in Neural Information Processing Systems 21. (pdf)
CI	Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (2009). A Bayesian framework for modeling intuitive dynamics. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)
PR RPM	Vul, E., Goodman, N. D., Griffiths, T. L., & Tenenbaum, J. B. (2009). One and done? Optimal decisions from very few samples. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)
P S&C NBM	Austerweil, J. L., & Griffiths, T. L. (2009). The effect of distributional information on feature learning. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)
CEIL	Beppu, A., & Griffiths, T. L. (2009). Iterated learning and the cultural ratchet. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)
CI SML NBM	Buchsbaum, D., Griffiths, T. L., Gopnik, A., & Baldwin, D. (2009). Learning from actions and their consequences: Inferring causal variables from continuous sequences of human action. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)
SML NBM	Feldman, N. H., Griffiths, T. L., & Morgan, J. L. (2009). Learning phonetic categories by learning a lexicon. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)
CEIL	Rafferty, A., Griffiths, T. L., & Klein, D. (2009). Convergence bounds for language evolution by iterated learning. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)
SML CEIL	Bouchard-Cote, A., Griffiths, T. L., & Klein, D. (2009). Improved reconstruction of protolanguage word forms. Proceedings of the North American Conference on Computational Linguistics (NAACL'09). (pdf)
SML	Dowman, M., Savova, V., Griffiths, T. L., Kording, K. P., Tenenbaum, J. B., & Purver, M. (2008). A probabilistic model of meetings that combines words and discourse features. IEEE Transactions on Audio, Speech, and Language Processing, 16, 1238-1248. (pdf)
CEIL	Griffiths, T. L., Kalish, M. L., & Lewandowsky, S. (2008). Theoretical and experimental evidence for the impact of inductive biases on cultural evolution. Philosophical Transactions of the Royal Society, 363, 3503-3514. (pdf)
CEIL	Smith, K., Kalish, M. L., Griffiths, T. L., & Lewandowsky, S. (2008). Cultural transmission and the evolution of human behaviour. Philosophical Transactions of the Royal Society, 363, 3469-3476. (pdf)
S&C	Sanborn, A. N., & Griffiths, T. L. (2008). Markov chain Monte Carlo with people. Advances in Neural Information Processing Systems, 20. (pdf) (winner of the Outstanding Student Paper prize)
SML CEIL	Bouchard-Cote, A., Liang, P., Griffiths, T. L., & Klein, D. (2008). A probabilistic approach to language change. Advances in Neural Information Processing Systems 20. (pdf)
S&C NBM	Navarro, D. J., & Griffiths, T. L. (2008). Latent features in similarity judgment: A nonparametric Bayesian approach. Neural Computation, 20, 2597-2628.(pdf)
S&C CEIL	Griffiths, T. L., Christian, B. R., & Kalish, M. L. (2008). Using category structures to test iterated learning as a method for revealing inductive biases. Cognitive Science, 32, 68-107. (pdf)
S&C	Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L. (2008). A rational analysis of rule-based concept learning. Cognitive Science, 32, 108-154. (pdf)
S&C NBM	Griffiths, T. L., Sanborn, A. N., Canini, K. R., & Navarro, D. J. (2008). Categorization as nonparametric Bayesian density estimation. In M. Oaksford and N. Chater (Eds.). The probabilistic mind: Prospects for rational models of cognition. Oxford: Oxford University Press. (pdf)
S&C	Goodman, N. D., Tenenbaum, J. B., Griffiths, T. L., & Feldman, J. (2008). Compositionality in rational analysis: Grammar-based induction for concept learning. In M. Oaksford and N. Chater (Eds.). The probabilistic mind: Prospects for rational models of cognition. Oxford: Oxford University Press. (pdf)
SML	Steyvers, M., & Griffiths, T. L. (2008). Rational analysis as a link between human memory and information retrieval. In M. Oaksford and N. Chater (Eds.). The probabilistic mind: Prospects for rational models of cognition. Oxford: Oxford University Press. (pdf)
F	Griffiths, T. L., & Yuille, A. (2008). A primer on probabilistic inference. In M. Oaksford and N. Chater (Eds.). The probabilistic mind: Prospects for rational models of cognition. Oxford: Oxford University Press. (pdf)
F	Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. (2008). Bayesian models of cognition. In Ron Sun (ed.), The Cambridge handbook of computational cognitive modeling. Cambridge University Press. (pdf)
NBM	Miller, K. T, Griffiths, T. L., & Jordan, M. I. (2008). The phylogenetic Indian buffet process: A non-exchangeable nonparametric prior for latent features.Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI 2008). (pdf)
PR S&C	Austerweil, J., & Griffiths, T. L. (2008). A rational analysis of confirmation with deterministic hypotheses. Proceedings of the 30th Annual Conference of the Cognitive Science Society. (pdf)
SML CEIL	Reali, F., & Griffiths, T. L. (2008). The evolution of frequency distributions: Relating regularization to inductive biases through iterated learning. Proceedings of the 30th Annual Conference of the Cognitive Science Society. (pdf)
RPM S&C	Shi, L., Feldman, N. H., & Griffiths, T. L. (2008). Performing Bayesian inference with exemplar models. Proceedings of the 30th Annual Conference of the Cognitive Science Society. (pdf)
PR	Williams, J. J., & Griffiths, T. L. (2008). Why are people bad at detecting randomness? Because it is hard. Proceedings of the 30th Annual Conference of the Cognitive Science Society. (pdf)
NBM CEIL	Xu, J., Reali, F., & Griffiths, T. L. (2008). A formal analysis of cultural evolution by replacement. Proceedings of the 30th Annual Conference of the Cognitive Science Society. (pdf)
SML	Griffiths, T. L., Steyvers, M., & Firl, A. (2007). Google and the mind: Predicting fluency with PageRank. Psychological Science, 18, 1069-1076. (pdf)
S&C SML	Iwata, T., Saito, K., Ueda, N., Stromsten, S., Griffiths, T. L., & Tenenbaum, J. B. (2007). Parametric embedding for class visualization. Neural Computation, 19, 2536-2556. (pdf)
CI CD	Schulz, L. E., Bonawitz, E. B., & Griffiths, T. L. (2007). Can being scared make your tummy ache? Naive theories, ambiguous evidence and preschoolers' causal inferences. Developmental Psychology, 43, 1124-1139. (pdf)
NBM	Wood, F., & Griffiths, T. L. (2007). Particle filtering for nonparametric Bayesian matrix factorization. Advances in Neural Information Processing Systems 19. (pdf)
SML NBM	Johnson, M., Griffiths, T. L., & Goldwater, S (2007). Adaptor grammars: A framework for specifying compositional nonparametric Bayesian models. Advances in Neural Information Processing Systems 19. (pdf)
S&C NBM	Navarro, D. J., & Griffiths, T. L. (2007). A nonparametric Bayesian method for inferring features from similarity judgments. Advances in Neural Information Processing Systems 19. (pdf)
CEIL	Griffiths, T. L., & Kalish, M. L. (2007). Language evolution by iterated learning with Bayesian agents. Cognitive Science, 31, 441-480. (pdf)
CEIL	Kalish, M. L., Griffiths, T. L., & Lewandowsky, S. (2007). Iterated learning: Intergenerational knowledge transmission reveals inductive biases. Psychonomic Bulletin and Review, 14, 288-294. (pdf)
SML	Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114,211-244. (pdf) (topic modeling toolbox)
NBM	Ghahramani, Z., Griffiths, T. L., & Sollich, P. (2007). Bayesian nonparametric latent feature models. Bayesian Statistics 8. Oxford University Press. (pdf) (discussion) (rejoinder)
CI	Tenenbaum, J. B., Griffiths, T. L., & Niyogi, S. (2007). Intuitive theories as grammars for causal inference. In A. Gopnik, & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation. Oxford: Oxford University Press. (pdf)
CI	Griffiths, T. L., & Tenenbaum, J. B. (2007). Two proposals for causal grammars. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation. Oxford: Oxford University Press. (pdf)
SML	Steyvers, M., & Griffiths, T. L. (2007). Probabilistic topic models. In T. L.andauer, D. S. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of Latent Semantic Analysis. Hillsdale, NJ: Erlbaum. (pdf) (topic modeling toolbox)
CI PR	Griffiths, T. L., & Tenenbaum, J. B. (2007). From mere coincidences to meaningful discoveries. Cognition, 103, 180-226. (pdf)
SML	Goldwater, S., Griffiths, T. L., & Johnson, M. (2007). Distributional cues to word segmentation: Context is important. Proceedings of the 31st Boston University Conference on Language Development. (pdf)
SML CEIL	Bouchard, A., Liang, P., Griffiths, T., & Klein, D. (2007). A probabilistic approach to diachronic phonology. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL). (pdf)
SML	Frank, M. C., Goldwater, S., Mansinghka, V., Griffiths, T., & Tenenbaum, J. B. (2007). Modeling human performance in statistical word segmentation. Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. (pdf)
S&C	Goodman, N. D., Griffiths, T. L., Feldman, J., & Tenenbaum, J. B. (2007). A rational analysis of rule-based concept learning. Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. (pdf)
S&C NBM	Griffiths, T. L., Canini, K. R., Sanborn, A. N., & Navarro, D. J (2007) Unifying rational models of categorization via the hierarchical Dirichlet process. Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. (pdf)
S&C SML	Feldman, N. H., & Griffiths, T. L. (2007). A rational account of the perceptual magnet effect. Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. (pdf)
PR	Schreiber, E., & Griffiths, T. L. (2007) Subjective randomness and natural scene statistics. Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. (pdf)
CD SML	Goldwater, S., & Griffiths, T. L. (2007). A fully Bayesian approach to unsupervised part-of-speech tagging. Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL'07). (pdf)
SML	Johnson, M., Griffiths, T. L., & Goldwater, S. (2007). Bayesian inference for PCFGs via Markov chain Monte Carlo. Proceedings of the North American Conference on Computational Linguistics (NAACL'07). (pdf)
CEIL	Kirby, S., Dowman, M., & Griffiths, T. L. (2007). Innateness and culture in the evolution of language. Proceedings of the National Academy of Sciences, 104, 5241-5245. (pdf)
PR	Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17, 767-773. (pdf) (article in The Economist)
F	Griffiths, T. L., & Yuille, A. (2006). A primer on probabilistic inference. Trends in Cognitive Sciences. Supplement to special issue on Probabilistic Models of Cognition (volume 10, issue 7). (pdf)
F CI	Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Science, 10, 309-318. (pdf)
SML	Steyvers, M., Griffiths, T. L., & Dennis, S. (2006). Probabilistic inference in human semantic memory. Trends in Cognitive Science, 10, 327-334. (pdf) (topic modeling toolbox)
F PR	Griffiths, T. L., & Tenenbaum, J. B. (2006). Statistics and the Bayesian mind. Significance, 3, 130-133. (pdf)
CD SML	Goldwater, S., Griffiths, T. L., & Johnson, M. (2006). Interpolating between types and tokens by estimating power law generators. Advances in Neural Information Processing Systems 18. (pdf) (note: this version of the paper is slightly modified from the hardcopy proceedings)
NBM	Griffiths, T. L., & Ghahramani, Z. (2006). Infinite latent feature models and the Indian buffet process. Advances in Neural Information Processing Systems 18. (pdf)
NBM	Navarro, D. J., Griffiths, T. L., Steyvers, M., & Lee, M. D. (2006). Modeling individual differences using Dirichlet processes. Journal of Mathematical Psychology, 50, 101-122. (pdf)
SML	Purver, M., Kording, K. P., Griffiths, T. L., & Tenenbaum, J. B. (2006). Unsupervised topic modelling for multi-party spoken discourse. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics. (pdf)
SML NBM	Goldwater, S., Griffiths, T. L., & Johnson, M. (2006). Contextual dependencies in unsupervised word segmentation. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics.(pdf)
RPM S&C NBM	Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2006). A more rational model of categorization. Proceedings of the 28th Annual Conference of the Cognitive Science Society. (pdf)
CI CD	Bonawitz, E. B., Griffiths, T. L., & Schulz, L. (2006). Modeling cross-domain causal learning in preschoolers as Bayesian inference. Proceedings of the 28th Annual Conference of the Cognitive Science Society. (pdf) (winner of the Marr Prize for best student paper)
S&C CEIL	Griffiths, T. L., Christian, B. R., & Kalish, M. L. (2006). Revealing priors on category structures through iterated learning. Proceedings of the 28th Annual Conference of the Cognitive Science Society. (pdf)
NBM	Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T., & Ueda, N. (2006). Learning systems of concepts with an infinite relational model. Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI '06). (pdf) (IRM code)
CI NBM	Mansinghka, V. K., Kemp, C., Tenenbaum, J. B., & Griffiths, T. L. (2006). Structured priors for structure learning. Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI 2006). (pdf)
CI NBM	Wood, F., Griffiths, T. L., & Ghahramani, Z. (2006). A non-parametric Bayesian method for inferring hidden causes. Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI 2006). (pdf)
CI	Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51, 354-384. (pdf) (Matlab code for computing causal support)
NBM	Griffiths, T. L., & Ghahramani, Z. (2005). Infinite latent feature models and the Indian buffet process. Gatsby Computational Neuroscience Unit Technical Report GCNU TR 2005-001. (pdf)
SML	Iwata, T., Saito, K., Ueda, N., Stromsten, S., Griffiths, T. L., & Tenenbaum, J. B. (2005). Parametric embedding for class visualization. Advances in Neural Information Processing Systems 17. (pdf)
SML	Griffiths, T. L., Steyvers, M., Blei, D. M., & Tenenbaum, J. B. (2005). Integrating topics and syntax. Advances in Neural Information Processing Systems 17. (pdf) (topic modeling toolbox)
CI	Griffiths, T. L. (2005). Causes, coincidences, and theories. Unpublished doctoral dissertation, Stanford University, Stanford CA. (pdf)
CEIL	Griffiths, T. L., & Kalish, M. L. (2005). A Bayesian view of language evolution by iterated learning. Proceedings of the 27th Annual Conference of the Cognitive Science Society. (pdf)
NBM	Navarro, D. J., Griffiths, T. L., Steyvers, M., & Lee, M. D. (2005). Modeling individual differences with Dirichlet processes. Proceedings of the 27th Annual Conference of the Cognitive Science Society. (pdf)
S&C	Kemp, C. S, Griffiths, T. L., Stromsten, S., & Tenenbaum, J. B. (2004). Semi-supervised learning with trees. Advances in Neural Information Processing Systems 16. (pdf)
SML NBM	Blei, D. M., Griffiths, T. L., Jordan, M. I., & Tenenbaum, J. B. (2004). Hierarchical topic models and the nested Chinese restaurant process. Advances in Neural Information Processing Systems 16. (pdf) (winner of the Best Student Paper prize)
PR	Griffiths, T. L., & Tenenbaum, J. B. (2004). From algorithmic to subjective randomness. Advances in Neural Information Processing Systems 16. (pdf) (winner of the Best Student Paper prize)
CI	Kemp, C., Griffiths, T. L., & Tenenbaum, J. B. (2004). Discovering latent classes in relational data. AI Memo 2004-019 (pdf)
CI CD	Griffiths, T. L., Baraff, E. R., & Tenenbaum, J. B. (2004). Using physical theories to infer hidden causal structure. Proceedings of the 26th Annual Conference of the Cognitive Science Society. (pdf)
SML	Steyvers, M., Smyth, P., Rosen-Zvi, M., & Griffiths, T. L. (2004). Probabilistic Author-Topic models for information discovery. The Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (pdf) (demo) (topic modeling toolbox)
SML	Rosen-Zvi, M., Griffiths, T. L., Steyvers, M., & Smyth, P. (2004). The Author-Topic Model for authors and documents. 20th Conference on Uncertainty in Artificial Intelligence. (pdf) (demo) (topic modeling toolbox)
SML	Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101, 5228-5235. (pdf) (topic modeling toolbox)
CI	Danks, D., Griffiths, T. L., & Tenenbaum, J. B. (2003). Dynamical causal learning. Advances in Neural Information Processing Systems 15. (pdf)
CI	Tenenbaum, J. B., & Griffiths, T. L. (2003). Theory-based causal inference. Advances in Neural Information Processing Systems 15. (pdf)
SML	Griffiths, T. L., & Steyvers, M. (2003). Prediction and semantic association. Advances in Neural Information Processing Systems 15. (pdf) (topic modeling toolbox)
PR	Griffiths, T. L., & Tenenbaum, J. B. (2003). Probability, algorithmic complexity, and subjective randomness. Proceedings of the 25th Annual Conference of the Cognitive Science Society. (pdf)
S&C	Griffiths, T. L., & Kalish, M. L. (2002). A multidimensional scaling approach to mental multiplication. Memory and Cognition, 30, 97-106. (pdf)
SML	Griffiths, T. L., & Tenenbaum, J. B. (2002). Using vocabulary knowledge in Bayesian multinomial estimation. Advances in Neural Information Processing Systems, 14. (pdf)
SML	Griffiths, T. L., & Steyvers, M. (2002). A probabilistic approach to semantic representation. Proceedings of the 24th Annual Conference of the Cognitive Science Society. (pdf) (topic modeling toolbox)
CI	Tenenbaum, J. B., & Griffiths, T. L. (2001). Structure learning in human causal induction. Advances in Neural Information Processing Systems 13. (pdf) (Matlab code for computing causal support)
S&C	Tenenbaum, J. B., & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24,629-641. (pdf)
S&C	Tenenbaum, J. B., & Griffiths, T. L. (2001). Some specifics about generalization. Behavioral and Brain Sciences, 24, 772-778. (html)
PR	Griffiths, T. L., & Tenenbaum, J. B. (2001). Randomness and coincidences: Reconciling intuition and probability theory. Proceedings of the 23rd Annual Conference of the Cognitive Science Society. (pdf)
PR	Tenenbaum, J. B., & Griffiths, T. L. (2001). The rational basis of representativeness. Proceedings of the 23rd Annual Conference of the Cognitive Science Society. (pdf)
S&C	Lewandowsky, S., Kalish, M., & Griffiths, T. L. (2000). Competing strategies in categorization: Expediency and resistance to knowledge restructuring. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 1666-1684. (pdf)
PR	Griffiths, T. L., & Tenenbaum, J. B. (2000). Teacakes, trains, toxins, and taxicabs: A Bayesian account of predicting the future. Proceedings of the 22nd Annual Conference of the Cognitive Science Society. (pdf)