Publications

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Rational Process Models Rational Process Models
Similarity and Categorization Similarity and Categorization
Statistical Models of Language Statistical Models of Language
Nonparametric Bayesian Models Nonparametric Bayesian Models
Cultural Evolution and Iterated Learning Cultural Evolution and Iterated Learning
Decision Making and Reinforcement Learning Decision Making and Reinforcement Learning

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In Press
Cultural Evolution and Iterated Learning
Whalen, A., Griffiths, T. L., & Buchsbaum, D. (in press). Sensitivity to shared information in social learning. Cognitive Science. (pdf)
Foundations
Paxton, A., & Griffiths, T. L. (in press). Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets. Behavior Research Methods. (pdf)
Probabilistic Reasoning
Rational Process Models
Decision Making and Reinforcement Learning
Lieder, F., & Griffiths, T. L. (in press). Strategy selection as rational metareasoning. Psychological Review. (pdf)
Probabilistic Reasoning
Rational Process Models
Decision Making and Reinforcement Learning
Lieder, F., Griffiths, T. L., & Hsu, M. (in press). Over-representation of extreme events in decision making reflects rational use of cognitive resources. Psychological Review. (pdf)
Probabilistic Reasoning
Rational Process Models
Decision Making and Reinforcement Learning
Lieder, F., Griffiths, T. L., Huys, Q. J. M., & Goodman, N. D. (in press). Empirical evidence for resource-rational anchoring and adjustment. Psychonomic Bulletin & Review. (pdf)
Probabilistic Reasoning
Rational Process Models
Decision Making and Reinforcement Learning
Lieder, F., Griffiths, T. L., Huys, Q. J. M., & Goodman, N. D. (in press). The anchoring bias reflects rational use of cognitive resources. Psychonomic Bulletin & Review. (pdf)

2017
Foundations
Cultural Evolution and Iterated Learning
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)
Cognitive Development
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)
Causal Induction
Griffiths, T. L. (2017). Formalizing prior knowledge in causal induction. The Oxford Handbook of Causal Reasoning. Oxford: Oxford University Press. (book)
Probabilistic Reasoning
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)
Perception
Cultural Evolution and Iterated Learning
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)
Rational Process Models
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)
Education
Probabilistic Reasoning
Dubey, R., & Griffiths, T. L. (2017). A rational analysis of curiosity. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
Cognitive Development
Statistical Models of Language
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)
Statistical Models of Language
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)
Education
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)
Decision Making and Reinforcement Learning
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)
Rational Process Models
Decision Making and Reinforcement Learning
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)
Perception
Causal Induction
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)
Similarity and Categorization
Statistical Models of Language
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)
Cultural Evolution and Iterated Learning
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)
Perception
Similarity and Categorization
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)
Causal Induction
Rational Process Models
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)
Perception
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)
Probabilistic Reasoning
Rational Process Models
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)
Rational Process Models
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)
Statistical Models of Language
Cultural Evolution and Iterated Learning
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)

2016
Probabilistic Reasoning
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)
Perception
Causal Induction
Probabilistic Reasoning
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)
Cognitive Development
Probabilistic Reasoning
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)
Foundations
Cultural Evolution and Iterated Learning
Suchow, J. W., & Griffiths, T. L. (2016). Rethinking experiment design as algorithm design. CrowdML – NIPS '16 Workshop on Crowdsourcing and Machine Learning. (pdf)
Probabilistic Reasoning
Statistical Models of Language
Hsu, A., & Griffiths, T. L. (2016). Sampling assumptions affect use of indirect negative evidence in language learning PLOS One, 11(6). (pdf)
Rational Process Models
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)
Education
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)
Perception
Similarity and Categorization
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)
Foundations
Perception
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)
Probabilistic Reasoning
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)
Cognitive Development
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)
Perception
Cultural Evolution and Iterated Learning
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)
Probabilistic Reasoning
Rational Process Models
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)
Perception
Similarity and Categorization
Peterson, J., 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)
Statistical Models of Language
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)
Perception
Probabilistic Reasoning
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)
Statistical Models of Language
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)
Cognitive Development
Probabilistic Reasoning
Eaves Jr, B. S., Feldman, N. H., Griffiths, T. L., & Shafto, P. (2016). Infant-directed speech is consistent with teaching. Psychological Review (pdf)
Rational Process Models
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)
Causal Induction
Cognitive Development
Bridgers, S., Buchsbaum, D., Seiver, E., Griffiths, T. L., & Gopnik, A. (2016). Children's causal inferences from conflicting testimony and observations. Developmental Psychology. (pdf)

2015
Cognitive Development
Probabilistic Reasoning
Hu, J., Lucas, C. G., Griffiths, T. L., & Xu, F. (2015). Preschoolers' understanding of graded preferences. Cognitive Development, 36, 93-102. (pdf)
Education
Similarity and Categorization
Rafferty, A. N., Brunskill, E., Griffiths, T. L., & Shafto, P. (2015). Faster teaching via POMDP planning. Cognitive Science. (pdf)
Foundations
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)
Education
Rafferty, A. N., & Griffiths, T. L. (2015). Interpreting freeform equation solving. Proceedings of the 17th International Conference on Artificial Intelligence in Education. (pdf)
Perception
Rational Process Models
Hamrick, J. B., 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)
Cognitive Development
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)
Probabilistic Reasoning
Rational Process Models
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)
Cognitive Development
Probabilistic Reasoning
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)
Cognitive Development
Statistical Models of Language
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)
Cultural Evolution and Iterated Learning
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)
Causal Induction
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)
Causal Induction
Cognitive Development
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)
Rational Process Models
Statistical Models of Language
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)
Foundations
Rational Process Models
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)
Probabilistic Reasoning
Rafferty, A. N., LaMar, M. M., & Griffiths, T. L. (2015). Inferring learners' knowledge from their actions. Cognitive Science, 39, 584-618. (pdf)
Foundations
Goodman, N. D., Frank, M. C., Griffiths, T. L., Tenenbaum, J. B., Battaglia, P., & Hamrick, J. (2015). Relevant and robust. A response to Marcus and Davis. Psychological Science, 26, 539-541. (pdf)
Foundations
Griffiths, T. L. (2015). Manifesto for a new (computational) cognitive revolution. Cognition, 135, 21-23. (pdf)
Cognitive Development
Rational Process Models
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)
Causal Induction
Nonparametric Bayesian Models
Lucas, C. G., Griffiths, T. L., Williams, J. J., & Kalish, M. L. (2015). A rational model of function learning. Psychonomic Bulletin and Review. (pdf)
Causal Induction
Cultural Evolution and Iterated Learning
Yeung, S., & Griffiths, T. L. (2015). Identifying expectations about the strength of causal relationships. Cognitive Psychology, 76, 1-29. (pdf)
Causal Induction
Statistical Models of Language
Nonparametric Bayesian Models
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)
Probabilistic Reasoning
Similarity and Categorization
Griffiths, T. L. (2015). Revealing ontological commitments by magic. Cognition, 136, 43-48. (pdf)

2014
Rational Process Models
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)
Statistical Models of Language
Cultural Evolution and Iterated Learning
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)
Statistical Models of Language
Cultural Evolution and Iterated Learning
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)
Causal Induction
Cognitive Development
Probabilistic Reasoning
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)
Probabilistic Reasoning
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)
Rational Process Models
Statistical Models of Language
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)
Probabilistic Reasoning
Rational Process Models
Hamrick, J., & 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)
Probabilistic Reasoning
Rational Process Models
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)
Rational Process Models
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)
Rational Process Models
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)
Similarity and Categorization
Cultural Evolution and Iterated Learning
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)
Cultural Evolution and Iterated Learning
Kirby, S., Griffiths, T. L., & Smith, K. (2014). Iterated learning and the evolution of language. Current Opinion in Neurobiology, 28, 108-114. (pdf)
Causal Induction
Rational Process Models
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)
Probabilistic Reasoning
Rational Process Models
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)
Similarity and Categorization
Cultural Evolution and Iterated Learning
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)
Causal Induction
Cognitive Development
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)
Similarity and Categorization
Rafferty, A. N., Zaharia, M., & Griffiths, T. L. (2014). Optimally designing games for behavioural research. Proceedings of the Royal Society Series A, 470. (pdf)
Cognitive Development
Probabilistic Reasoning
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)
Probabilistic Reasoning
Similarity and Categorization
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)

2013
Statistical Models of Language
Nonparametric Bayesian Models
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)
Statistical Models of Language
Cultural Evolution and Iterated Learning
Rafferty, A. N., Griffiths, T. L., & Ettlinger, M. (2013). Greater learnability is not sufficient to produce cultural universals. Cognition, 129, 70-87. (pdf)
Probabilistic Reasoning
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)
Perception
Similarity and Categorization
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)
Similarity and Categorization
Nonparametric Bayesian Models
Austerweil, J., & Griffiths, T. L. (2013). A nonparametric Bayesian framework for constructing flexible feature representations. Psychological Review, 120, 817-851. (pdf)
Similarity and Categorization
Statistical Models of Language
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)
Perception
Causal Induction
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)
Probabilistic Reasoning
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)
Cultural Evolution and Iterated Learning
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)
Causal Induction
Rational Process Models
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)
Probabilistic Reasoning
Rational Process Models
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)
Causal Induction
Cultural Evolution and Iterated Learning
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)
Probabilistic Reasoning
Cultural Evolution and Iterated Learning
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)
Causal Induction
Probabilistic Reasoning
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)
Similarity and Categorization
Cultural Evolution and Iterated Learning
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)
Statistical Models of Language
Cultural Evolution and Iterated Learning
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)

2012
Rational Process Models
Statistical Models of Language
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)
Probabilistic Reasoning
Rational Process Models
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)
Cultural Evolution and Iterated Learning
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)
Cognitive Development
Rational Process Models
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)
Foundations
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)
Foundations
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)
Similarity and Categorization
Griffiths, T. L., & Austerweil, J. L. (2012). Bayesian generalization with circular consequential regions. Journal of Mathematical Psychology, 56, 281-285. (pdf)
Foundations
Rational Process Models
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)
Causal Induction
Cognitive Development
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)
Similarity and Categorization
Cultural Evolution and Iterated Learning
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)
Similarity and Categorization
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)
Similarity and Categorization
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)
Probabilistic Reasoning
Similarity and Categorization
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)
Foundations
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)
Perception
Similarity and Categorization
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)
Perception
Similarity and Categorization
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)
Causal Induction
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)
Similarity and Categorization
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)
Perception
Similarity and Categorization
Austerweil, J. L., & Griffiths, T. L. (2012). Human feature learning. Encyclopedia of the sciences of learning. N. M. Seel, ed. New York: Springer. (book)

2011
Probabilistic Reasoning
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)
Causal Induction
Cognitive Development
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)
Perception
Nonparametric Bayesian Models
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)
Probabilistic Reasoning
Similarity and Categorization
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)
Causal Induction
Pacer, M., & Griffiths, T. L. (2011). A rational model of causal induction with continuous causes. Advances in Neural Information Processing Systems, 24. (pdf)
Perception
Nonparametric Bayesian Models
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)
Statistical Models of Language
Nonparametric Bayesian Models
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)
Statistical Models of Language
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)
Foundations
Cognitive Development
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)
Causal Induction
Cognitive Development
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)
Probabilistic Reasoning
Austerweil, J. L., & Griffiths, T. L. (2011). Seeking confirmation is rational for deterministic hypotheses. Cognitive Science, 35, 499-526. (pdf)
Nonparametric Bayesian Models
Griffiths, T. L., & Ghahramani, Z. (2011). The Indian Buffet Process: An introduction and review. Journal of Machine Learning Research, 12, 1185-1224. (pdf)
Statistical Models of Language
Cultural Evolution and Iterated Learning
Griffiths, T. L., & Reali, F. (2011). Modelling minds as well as populations. Proceedings of the Royal Society, Series B. (pdf)
Education
Probabilistic Reasoning
Similarity and Categorization
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)
Similarity and Categorization
Nonparametric Bayesian Models
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)
Causal Induction
Cultural Evolution and Iterated Learning
Yeung, S., & Griffiths, T. L. (2011). Estimating human priors on causal strength. Proceedings of the 33rd Annual Conference of the Cognitive Science Society. (pdf)
Similarity and Categorization
Cultural Evolution and Iterated Learning
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)
Causal Induction
Rational Process Models
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)
Probabilistic Reasoning
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)
Perception
Statistical Models of Language
Buchsbaum, D., Canini, K. R., and 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)
Statistical Models of Language
Cultural Evolution and Iterated Learning
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)
Causal Induction
Rational Process Models
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)
Similarity and Categorization
Nonparametric Bayesian Models
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. Wills (Eds.) Formal approaches in categorization. Cambridge, UK: Cambridge University Press. (book)
Statistical Models of Language
Griffiths, T. L. (2011). Rethinking language: How probabilities shape the words we use. Proceedings of the National Academy of Sciences, 108, 3825-3826. (pdf)
Foundations
Cognitive Development
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)

2010
Statistical Models of Language
Nonparametric Bayesian Models
Frank, M., Goldwater, S., Griffiths, T. L., & Tenenbaum , J. B. (2010). Modeling human performance in statistical word segmentation. Cognition, 117, 107-125.(pdf)
Rational Process Models
Similarity and Categorization
Nonparametric Bayesian Models
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)
Probabilistic Reasoning
Rational Process Models
Similarity and Categorization
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)
Perception
Similarity and Categorization
Nonparametric Bayesian Models
Austerweil, J. L., & Griffiths, T. L. (2010). Learning invariant features using the Transformed Indian Buffet Process. Advances in Neural Information Processing Systems 23. (pdf)
Probabilistic Reasoning
Hsu, A., Griffiths, T. L., & Schreiber, E. (2010). Subjective randomness and natural scene statistics. Psychonomic Bulletin & Review, 17, 624-629. (pdf)
Foundations
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)
Perception
Similarity and Categorization
Cultural Evolution and Iterated Learning
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)
Similarity and Categorization
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)
Cognitive Development
Statistical Models of Language
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)
Causal Induction
Cognitive Development
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)
Causal Induction
Probabilistic Reasoning
Rational Process Models
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)
Causal Induction
Probabilistic Reasoning
Rational Process Models
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)
Perception
Similarity and Categorization
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)
Causal Induction
Cognitive Development
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)
Similarity and Categorization
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)
Statistical Models of Language
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)
Foundations
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.
Statistical Models of Language
Cultural Evolution and Iterated Learning
Burkett, D., & Griffiths, T. L. (2010). Iterated learning of multiple languages from multiple teachers. Evolang 8. (pdf)
Statistical Models of Language
Nonparametric Bayesian Models
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)
Statistical Models of Language
Cultural Evolution and Iterated Learning
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)
Causal Induction
Nonparametric Bayesian Models
Kemp, C., Tenenbaum, J. B., Niyogi, S., & Griffiths, T. L. (2010). A probabilistic model of theory formation. Cognition, 114, 165-196. (pdf)
Causal Induction
Lucas, C. G., & Griffiths, T. L. (2010). Learning the form of causal relationships using hierarchical Bayesian models. Cognitive Science, 34, 113-147. (pdf)
Similarity and Categorization
Cultural Evolution and Iterated Learning
Xu, J., & Griffiths, T. L. (2010). A rational analysis of the effects of memory biases on serial reproduction. Cognitive Psychology, 60, 107-126. (pdf)
Probabilistic Reasoning
Rational Process Models
Similarity and Categorization
Sanborn, A. N., Griffiths, T. L., & Shiffrin, R. (2010). Uncovering mental representations with Markov chain Monte Carlo. Cognitive Psychology, 60, 63-106. (pdf)

2009
Statistical Models of Language
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)
Nonparametric Bayesian Models
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)
Probabilistic Reasoning
Rational Process Models
Shi, L., & Griffiths, T. L. (2009). Neural implementation of hierarchical Bayesian inference by importance sampling. Advances in Neural Information Processing Systems 22. (pdf)
Probabilistic Reasoning
Cultural Evolution and Iterated Learning
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)
Perception
Similarity and Categorization
Statistical Models of Language
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)
Causal Induction
Cognitive Development
Griffiths, T. L., & Tenenbaum, J. B. (2009). Theory-based causal induction. Psychological Review, 116, 661-716. (pdf)
Cultural Evolution and Iterated Learning
Jaeger, H., Baronchelli, A., Briscoe, T., Christiansen, M. H., Griffiths, T., 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.
Foundations
Griffiths, T. L. (2009). Connecting human and machine learning via probabilistic models of cognition. InterSpeech 2009. (pdf)
Cognitive Development
Statistical Models of Language
Goldwater, S., Griffiths, T. L., & Johnson, M. (2009). A Bayesian framework for word segmentation: Exploring the effects of context. Cognition, 112, 21-54. (pdf)
Statistical Models of Language
Cultural Evolution and Iterated Learning
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)
Causal Induction
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)
Probabilistic Reasoning
Rational Process Models
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)
Perception
Similarity and Categorization
Nonparametric Bayesian Models
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)
Cultural Evolution and Iterated Learning
Beppu, A., & Griffiths, T. L. (2009). Iterated learning and the cultural ratchet. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)
Causal Induction
Statistical Models of Language
Nonparametric Bayesian Models
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)
Statistical Models of Language
Nonparametric Bayesian Models
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)
Cultural Evolution and Iterated Learning
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)
Statistical Models of Language
Cultural Evolution and Iterated Learning
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)
Statistical Models of Language
Canini, K. R., Shi, L., & Griffiths, T. L. (2009). Online inference of topics with Latent Dirichlet Allocation. AISTATS. (pdf)
Cognitive Development
Probabilistic Reasoning
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)
Causal Induction
Similarity and Categorization
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)
Rational Process Models
Statistical Models of Language
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)
Similarity and Categorization
Cultural Evolution and Iterated Learning
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)
Perception
Similarity and Categorization
Nonparametric Bayesian Models
Austerweil, J., & Griffiths, T. L. (2009). Analyzing human feature learning as nonparametric Bayesian inference. Advances in Neural Information Processing Systems 21. (pdf)

2008
Statistical Models of Language
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)
Cultural Evolution and Iterated Learning
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)
Cultural Evolution and Iterated Learning
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)
Nonparametric Bayesian Models
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)
Probabilistic Reasoning
Similarity and Categorization
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)
Statistical Models of Language
Cultural Evolution and Iterated Learning
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)
Rational Process Models
Similarity and Categorization
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)
Probabilistic Reasoning
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)
Nonparametric Bayesian Models
Cultural Evolution and Iterated Learning
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)
Similarity and Categorization
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)
Statistical Models of Language
Cultural Evolution and Iterated Learning
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)
Similarity and Categorization
Nonparametric Bayesian Models
Navarro, D. J., & Griffiths, T. L. (2008). Latent features in similarity judgment: A nonparametric Bayesian approach. Neural Computation, 20, 2597-2628.(pdf)
Similarity and Categorization
Cultural Evolution and Iterated Learning
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)
Similarity and Categorization
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)
Similarity and Categorization
Nonparametric Bayesian Models
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)
Similarity and Categorization
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)
Statistical Models of Language
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)
Foundations
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)
Foundations
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)

2007
Statistical Models of Language
Griffiths, T. L., Steyvers, M., & Firl, A. (2007). Google and the mind: Predicting fluency with PageRank. Psychological Science, 18, 1069-1076. (pdf)
Similarity and Categorization
Statistical Models of Language
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)
Causal Induction
Cognitive Development
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)
Statistical Models of Language
Cultural Evolution and Iterated Learning
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)
Statistical Models of Language
Frank, M. C., Goldwater, S., Mansinghka, V., Griffiths, T., & Tenenbaum, J. (2007). Modeling human performance in statistical word segmentation. Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. (pdf)
Similarity and Categorization
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)
Similarity and Categorization
Nonparametric Bayesian Models
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)
Similarity and Categorization
Statistical Models of Language
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)
Probabilistic Reasoning
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)
Nonparametric Bayesian Models
Wood, F., & Griffiths, T. L. (2007). Particle filtering for nonparametric Bayesian matrix factorization. Advances in Neural Information Processing Systems 19. (pdf)
Statistical Models of Language
Nonparametric Bayesian Models
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)
Similarity and Categorization
Nonparametric Bayesian Models
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)
Cultural Evolution and Iterated Learning
Griffiths, T. L., & Kalish, M. L. (2007). Language evolution by iterated learning with Bayesian agents. Cognitive Science, 31, 441-480. (pdf)
Cultural Evolution and Iterated Learning
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)
Cognitive Development
Statistical Models of Language
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)
Statistical Models of Language
Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114,211-244. (pdf) (topic modeling toolbox)
Nonparametric Bayesian Models
Ghahramani, Z., Griffiths, T. L., & Sollich, P. (2007). Bayesian nonparametric latent feature models. Bayesian Statistics 8. Oxford University Press. (pdf) (discussion) (rejoinder)
Statistical Models of Language
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)
Cultural Evolution and Iterated Learning
Kirby, S., Dowman, M., & Griffiths, T. (2007). Innateness and culture in the evolution of language. Proceedings of the National Academy of Sciences, 104, 5241-5245. (pdf)
Causal Induction
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)
Causal Induction
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)
Statistical Models of Language
Steyvers, M., & Griffiths, T. (2007). Probabilistic topic models. In T. Landauer, D. S. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of Latent Semantic Analysis. Hillsdale, NJ: Erlbaum. (pdf) (topic modeling toolbox)
Statistical Models of Language
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)
Causal Induction
Probabilistic Reasoning
Griffiths, T. L., & Tenenbaum, J. B. (2007). From mere coincidences to meaningful discoveries. Cognition, 103, 180-226. (pdf)

2006
Probabilistic Reasoning
Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17, 767-773. (pdf) (article in The Economist)
Foundations
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)
Foundations
Causal Induction
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)
Statistical Models of Language
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)
Foundations
Probabilistic Reasoning
Griffiths, T. L., & Tenenbaum, J. B. (2006). Statistics and the Bayesian mind. Significance, 3, 130-133. (pdf)
Statistical Models of Language
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)
Statistical Models of Language
Nonparametric Bayesian Models
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)
Rational Process Models
Similarity and Categorization
Nonparametric Bayesian Models
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)
Causal Induction
Cognitive Development
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)
Similarity and Categorization
Cultural Evolution and Iterated Learning
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)
Nonparametric Bayesian Models
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)
Causal Induction
Nonparametric Bayesian Models
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)
Causal Induction
Nonparametric Bayesian Models
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)
Cognitive Development
Statistical Models of Language
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)
Nonparametric Bayesian Models
Griffiths, T. L., & Ghahramani, Z. (2006). Infinite latent feature models and the Indian buffet process. Advances in Neural Information Processing Systems 18. (pdf)
Nonparametric Bayesian Models
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)

2005
Causal Induction
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)
Cultural Evolution and Iterated Learning
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)
Nonparametric Bayesian Models
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)
Nonparametric Bayesian Models
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)
Statistical Models of Language
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)
Statistical Models of Language
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)
Causal Induction
Griffiths, T. L. (2005). Causes, coincidences, and theories. Unpublished doctoral dissertation, Stanford University, Stanford CA. (pdf)

2004
Causal Induction
Kemp, C., Griffiths, T. L., & Tenenbaum, J. B. (2004). Discovering latent classes in relational data. AI Memo 2004-019 (pdf)
Statistical Models of Language
Steyvers, M., Smyth, P., Rosen-Zvi, M., & Griffiths, T. (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)
Statistical Models of Language
Rosen-Zvi, M., Griffiths, T., 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)
Statistical Models of Language
Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101, 5228-5235. (pdf) (topic modeling toolbox)
Similarity and Categorization
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)
Statistical Models of Language
Nonparametric Bayesian Models
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)
Probabilistic Reasoning
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)
Causal Induction
Cognitive Development
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)

2003
Causal Induction
Danks, D., Griffiths, T. L., & Tenenbaum, J. B. (2003). Dynamical causal learning. Advances in Neural Information Processing Systems 15. (pdf)
Causal Induction
Tenenbaum, J. B., & Griffiths, T. L. (2003). Theory-based causal inference. Advances in Neural Information Processing Systems 15. (pdf)
Statistical Models of Language
Griffiths, T. L., & Steyvers, M. (2003). Prediction and semantic association. Advances in Neural Information Processing Systems 15. (pdf) (topic modeling toolbox)
Probabilistic Reasoning
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)

2002
Similarity and Categorization
Griffiths, T. L., & Kalish, M. L. (2002). A multidimensional scaling approach to mental multiplication. Memory and Cognition, 30, 97-106. (pdf)
Statistical Models of Language
Griffiths, T. L., & Tenenbaum, J. B. (2002). Using vocabulary knowledge in Bayesian multinomial estimation. Advances in Neural Information Processing Systems 14. (pdf)
Statistical Models of Language
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)

2001
Causal Induction
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)
Probabilistic Reasoning
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)
Probabilistic Reasoning
Tenenbaum, J. B., & Griffiths, T. L. (2001). The rational basis of representativeness. Proceedings of the 23rd Annual Conference of the Cognitive Science Society. (pdf)
Similarity and Categorization
Tenenbaum, J. B., & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24,629-641. (pdf)
Similarity and Categorization
Tenenbaum, J. B., & Griffiths, T. L. (2001). Some specifics about generalization. Behavioral and Brain Sciences, 24, 772-778. (html)

2000
Probabilistic Reasoning
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)
Similarity and Categorization
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)

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