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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Zhao, B., Velez, N., & Griffiths, T. L. (2024). A Rational Model of Innovation by Recombination. 46th Annual Meeting of the Cognitive Science Society.(pdf)
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)
Zhu, J. Q., & Griffiths, T. L. (2024). Incoherent Probability Judgments in Large Language Models. 46th Annual Meeting of the Cognitive Science Society.(pdf)
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)
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)
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)
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)
Jha, A., Peterson, J. C., & Griffiths, T. L. (2023). Extracting low‐dimensional psychological representations from convolutional neural networks. Cognitive Science, 47(1), e13226. (pdf)
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)
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)
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)
Sucholutsky, I., & Griffiths, T. L. (2023). Alignment with human representations supports robust few-shot learning. Advances in Neural Information Processing Systems, 37. (pdf)
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)
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)
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)
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)
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)
Dasgupta, I., & Griffiths, T. L. (2022). Clustering and the efficient use of cognitive resources. Journal of Mathematical Psychology, 109, 102675. (pdf)
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)
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)
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)
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)
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)
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)
Callaway, F., Rangel, A., & Griffiths, T. L. (2021). Fixation patterns in simple choice reflect optimal information sampling. PLOS Computational Biology, 17(3), e1008863. (pdf)
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)
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)
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)
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)
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)
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)
Dubey, R., & Griffiths, T. L. (2020). Reconciling novelty and complexity through a rational analysis of curiosity. Psychological Review, 127(3), 455-476. (pdf)
Dubey, R., & Griffths, T.L. (2020). Understanding exploration in humans and machines by formalizing the function of curiosity. Current Opinion in Behavioral Sciences, 35, 118-124. (pdf)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Thompson, B., & Griffiths, T. L. (2019). Inductive biases constrain cumulative cultural evolution. Proceedings of the 41st Annual Conference of the Cognitive Science Society. (pdf)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Griffiths, T. L. (2017). Formalizing prior knowledge in causal induction. The Oxford Handbook of Causal Reasoning. Oxford: Oxford University Press. (book)
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)
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)
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)
Dubey, R., & Griffiths, T. L. (2017). A rational analysis of curiosity. Proceedings of the 39th Annual Conference of the Cognitive Science Society.(pdf)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Suchow, J. W., & Griffiths, T. L. (2016). Rethinking experiment design as algorithm design. CrowdML – NIPS '16 Workshop on Crowdsourcing and Machine Learning. (pdf)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Rafferty, A. N., & Griffiths, T. L. (2015). Interpreting freeform equation solving. Proceedings of the 17th International Conference on Artificial Intelligence in Education. (pdf)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Griffiths, T. L., & Austerweil, J. L. (2012). Bayesian generalization with circular consequential regions. Journal of Mathematical Psychology, 56, 281-285. (pdf)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Pacer, M., & Griffiths, T. L. (2011). A rational model of causal induction with continuous causes. Advances in Neural Information Processing Systems, 24. (pdf)
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)
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)
Griffiths, T. L., & Ghahramani, Z. (2011). The Indian Buffet Process: An introduction and review. Journal of Machine Learning Research, 12, 1185-1224. (pdf)
Griffiths, T. L. (2011). Rethinking language: How probabilities shape the words we use. Proceedings of the National Academy of Sciences, 108, 3825-3826. (pdf)
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)
Yeung, S., & Griffiths, T. L. (2011). Estimating human priors on causal strength. Proceedings of the 33rd Annual Conference of the Cognitive Science Society.(pdf)
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)
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)
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)
Austerweil, J. L., & Griffiths, T. L. (2010). Learning invariant features using the Transformed Indian Buffet Process. Advances in Neural Information Processing Systems 23.(pdf)
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.
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)
Lucas, C. G., & Griffiths, T. L. (2010). Learning the form of causal relationships using hierarchical Bayesian models. Cognitive Science, 34, 113-147. (pdf)
Xu, J., & Griffiths, T. L. (2010). A rational analysis of the effects of memory biases on serial reproduction. Cognitive Psychology, 60, 107-126. (pdf)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Shi, L., & Griffiths, T. L. (2009). Neural implementation of hierarchical Bayesian inference by importance sampling. Advances in Neural Information Processing Systems 22.(pdf)
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)
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)
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)
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)
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)
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)
Austerweil, J., & Griffiths, T. L. (2009). Analyzing human feature learning as nonparametric Bayesian inference. Advances in Neural Information Processing Systems 21.(pdf)
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)
Beppu, A., & Griffiths, T. L. (2009). Iterated learning and the cultural ratchet. Proceedings of the 31st Annual Conference of the Cognitive Science Society.(pdf)
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)
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)
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)
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)
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)
Navarro, D. J., & Griffiths, T. L. (2008). Latent features in similarity judgment: A nonparametric Bayesian approach. Neural Computation, 20, 2597-2628.(pdf)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Wood, F., & Griffiths, T. L. (2007). Particle filtering for nonparametric Bayesian matrix factorization. Advances in Neural Information Processing Systems 19.(pdf)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Griffiths, T. L., & Ghahramani, Z. (2006). Infinite latent feature models and the Indian buffet process. Advances in Neural Information Processing Systems 18.(pdf)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Griffiths, T. L., & Tenenbaum, J. B. (2002). Using vocabulary knowledge in Bayesian multinomial estimation. Advances in Neural Information Processing Systems, 14. (pdf)
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)
Tenenbaum, J. B., & Griffiths, T. L. (2001). The rational basis of representativeness. Proceedings of the 23rd Annual Conference of the Cognitive Science Society. (pdf)
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)
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)