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  1. Christopher G. Lucas, Sophie Bridgers, Thomas L. Griffiths & Alison Gopnik (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 (2):284-299.
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  2. Anna N. Rafferty, Thomas L. Griffiths & Dan Klein (2014). Analyzing the Rate at Which Languages Lose the Influence of a Common Ancestor. Cognitive Science 38 (2).
    Analyzing the rate at which languages change can clarify whether similarities across languages are solely the result of cognitive biases or might be partially due to descent from a common ancestor. To demonstrate this approach, we use a simple model of language evolution to mathematically determine how long it should take for the distribution over languages to lose the influence of a common ancestor and converge to a form that is determined by constraints on language learning. We show that modeling (...)
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  3. Anna N. Rafferty, Michelle M. LaMar & Thomas L. Griffiths (2014). Inferring Learners' Knowledge From Their Actions. Cognitive Science 38 (6).
    Watching another person take actions to complete a goal and making inferences about that person's knowledge is a relatively natural task for people. This ability can be especially important in educational settings, where the inferences can be used for assessment, diagnosing misconceptions, and providing informative feedback. In this paper, we develop a general framework for automatically making such inferences based on observed actions; this framework is particularly relevant for inferring student knowledge in educational games and other interactive virtual environments. Our (...)
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  4. Edward Vul, Noah Goodman, Thomas L. Griffiths & Joshua B. Tenenbaum (2014). One and Done? Optimal Decisions From Very Few Samples. Cognitive Science 38 (4):599-637.
    In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling-based approximations are a common way to implement Bayesian (...)
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  5. Stephanie Denison, Elizabeth Bonawitz, Alison Gopnik & Thomas L. Griffiths (2013). Rational Variability in Children's Causal Inferences: The Sampling Hypothesis. Cognition 126 (2):285-300.
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  6. Naomi H. Feldman, Emily B. Myers, Katherine S. White, Thomas L. Griffiths & James L. Morgan (2013). Word-Level Information Influences Phonetic Learning in Adults and Infants. Cognition 127 (3):427-438.
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  7. Thomas L. Griffiths, Stephan Lewandowsky & Michael L. Kalish (2013). The Effects of Cultural Transmission Are Modulated by the Amount of Information Transmitted. Cognitive Science 37 (5):953-967.
    Information changes as it is passed from person to person, with this process of cultural transmission allowing the minds of individuals to shape the information that they transmit. We present mathematical models of cultural transmission which predict that the amount of information passed from person to person should affect the rate at which that information changes. We tested this prediction using a function-learning task, in which people learn a functional relationship between two variables by observing the values of those variables. (...)
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  8. Anna N. Rafferty, Thomas L. Griffiths & Marc Ettlinger (2013). Greater Learnability is Not Sufficient to Produce Cultural Universals. Cognition 129 (1):70-87.
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  9. Jay B. Martin, Thomas L. Griffiths & Adam N. Sanborn (2012). Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories. Cognitive Science 36 (1):150-162.
    Exploring how people represent natural categories is a key step toward developing a better understanding of how people learn, form memories, and make decisions. Much research on categorization has focused on artificial categories that are created in the laboratory, since studying natural categories defined on high-dimensional stimuli such as images is methodologically challenging. Recent work has produced methods for identifying these representations from observed behavior, such as reverse correlation (RC). We compare RC against an alternative method for inferring the structure (...)
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  10. Joseph L. Austerweil & Thomas L. Griffiths (2011). Seeking Confirmation Is Rational for Deterministic Hypotheses. Cognitive Science 35 (3):499-526.
    The tendency to test outcomes that are predicted by our current theory (the confirmation bias) is one of the best-known biases of human decision making. We prove that the confirmation bias is an optimal strategy for testing hypotheses when those hypotheses are deterministic, each making a single prediction about the next event in a sequence. Our proof applies for two normative standards commonly used for evaluating hypothesis testing: maximizing expected information gain and maximizing the probability of falsifying the current hypothesis. (...)
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  11. Daphna Buchsbaum, Alison Gopnik, Thomas L. Griffiths & Patrick Shafto (2011). Children's Imitation of Causal Action Sequences is Influenced by Statistical and Pedagogical Evidence. Cognition 120 (3):331-340.
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  12. Nick Chater, Noah Goodman, Thomas L. Griffiths, Charles Kemp, Mike Oaksford & Joshua B. Tenenbaum (2011). The Imaginary Fundamentalists: The Unshocking Truth About Bayesian Cognitive Science. Behavioral and Brain Sciences 34 (4):194-196.
    If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.
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  13. Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum & Alison Gopnik (2011). Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults. Cognitive Science 35 (8):1407-1455.
    People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which (...)
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  14. Amy Perfors, Joshua B. Tenenbaum, Thomas L. Griffiths & Fei Xu (2011). A Tutorial Introduction to Bayesian Models of Cognitive Development. Cognition 120 (3):302-321.
  15. Fei Xu & Thomas L. Griffiths (2011). Probabilistic Models of Cognitive Development: Towards a Rational Constructivist Approach to the Study of Learning and Development. Cognition 120 (3):299-301.
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  16. Joseph L. Austerweil & Thomas L. Griffiths (2010). Learning Hypothesis Spaces and Dimensions Through Concept Learning. In. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. 73--78.
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  17. Elizabeth Baraff Bonawitz & Thomas L. Griffiths (2010). Deconfounding Hypothesis Generation and Evaluation in Bayesian Models. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
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  18. Michael C. Frank, Sharon Goldwater, Thomas L. Griffiths & Joshua B. Tenenbaum (2010). Modeling Human Performance in Statistical Word Segmentation. Cognition 117 (2):107-125.
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  19. Thomas L. Griffiths, Nick Chater, Charles Kemp, Amy Perfors & Joshua B. Tenenbaum (2010). Probabilistic Models of Cognition: Exploring Representations and Inductive Biases. Trends in Cognitive Sciences 14 (8):357-364.
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  20. Charles Kemp, Joshua B. Tenenbaum, Sourabh Niyogi & Thomas L. Griffiths (2010). A Probabilistic Model of Theory Formation. Cognition 114 (2):165-196.
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  21. Chris Lucas, Alison Gopnik & Thomas L. Griffiths (2010). Developmental Differences in Learning the Forms of Causal Relationships. In. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. 28--52.
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  22. Christopher G. Lucas & Thomas L. Griffiths (2010). Learning the Form of Causal Relationships Using Hierarchical Bayesian Models. Cognitive Science 34 (1):113-147.
  23. Jing Xu, Thomas L. Griffiths & Mike Dowman (2010). Replicating Color Term Universals Through Human Iterated Learning. In. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
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  24. Aaron Beppu & Thomas L. Griffiths (2009). Iterated Learning and the Cultural Ratchet. In. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. 2089--2094.
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  25. Daphna Buchsbaum, Thomas L. Griffiths, Alison Gopnik & Dare Baldwin (2009). Learning From Actions and Their Consequences: Inferring Causal Variables From Continuous Sequences of Human Action. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. 134.
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  26. Naomi H. Feldman, Thomas L. Griffiths & James L. Morgan (2009). Learning Phonetic Categories by Learning a Lexicon. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  27. Sharon Goldwater, Thomas L. Griffiths & Mark Johnson (2009). A Bayesian Framework for Word Segmentation: Exploring the Effects of Context. Cognition 112 (1):21-54.
  28. Thomas L. Griffiths (2009). The Strengths of – and Some of the Challenges for – Bayesian Models of Cognition. Behavioral and Brain Sciences 32 (1):89-90.
    Bayesian Rationality (Oaksford & Chater 2007) illustrates the strengths of Bayesian models of cognition: the systematicity of rational explanations, transparent assumptions about human learners, and combining structured symbolic representation with statistics. However, the book also highlights some of the challenges this approach faces: of providing psychological mechanisms, explaining the origins of the knowledge that guides human learning, and accounting for how people make genuinely new discoveries.
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  29. Stephan Lewandowsky, Thomas L. Griffiths & Michael L. Kalish (2009). The Wisdom of Individuals: Exploring People's Knowledge About Everyday Events Using Iterated Learning. Cognitive Science 33 (6):969-998.
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  30. Florencia Reali & Thomas L. Griffiths (2009). The Evolution of Frequency Distributions: Relating Regularization to Inductive Biases Through Iterated Learning. Cognition 111 (3):317-328.
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  31. Adam N. Sanborn, Vikash Mansinghka & Thomas L. Griffiths (2009). A Bayesian Framework for Modeling Intuitive Dynamics. In. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  32. Noah D. Goodman, Joshua B. Tenenbaum, Jacob Feldman & Thomas L. Griffiths (2008). A Rational Analysis of Rule‐Based Concept Learning. Cognitive Science 32 (1):108-154.
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  33. Noah D. Goodman, Joshua B. Tenenbaum, Thomas L. Griffiths & Jacob Feldman (2008). Compositionality in Rational Analysis: Grammar-Based Induction for Concept Learning. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oup Oxford.
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  34. Thomas L. Griffiths, Brian R. Christian & Michael L. Kalish (2008). Using Category Structures to Test Iterated Learning as a Method for Identifying Inductive Biases. Cognitive Science 32 (1):68-107.
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  35. Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini & Daniel J. Navarro (2008). Categorization as Nonparametric Bayesian Density Estimation. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oup Oxford.
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  36. Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini & Navarro & J. Daniel (2008). Categorization as Nonparametric Bayesian Density Estimation. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oup Oxford.
     
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  37. Thomas L. Griffiths & Alan Yuille (2008). A Primer on Probabilistic Inference. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oup Oxford. 33--57.
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  38. Lei Shi, Naomi H. Feldman & Thomas L. Griffiths (2008). Performing Bayesian Inference with Exemplar Models. In. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. 745--750.
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  39. Mark Steyvers & Thomas L. Griffiths (2008). Rational Analysis as a Link Between Human Memory and Information Retrieval. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oup Oxford. 329--349.
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  40. Joseph Jay Williams & Thomas L. Griffiths (2008). Why Are People Bad at Detecting Randomness? Because It is Hard. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society.
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  41. Jing Xu, Florencia Reali & Thomas L. Griffiths (2008). A Formal Analysis of Cultural Evolution by Replacement. In. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. 1435--1400.
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  42. Thomas L. Griffiths & Michael L. Kalish (2007). Language Evolution by Iterated Learning With Bayesian Agents. Cognitive Science 31 (3):441-480.
    Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute a posterior distribution over languages by combining a prior (representing their inductive biases) with the evidence provided by linguistic data. We show that when learners sample languages from this posterior (...)
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  43. Thomas L. Griffiths & Joshua B. Tenenbaum (2007). From Mere Coincidences to Meaningful Discoveries. Cognition 103 (2):180-226.
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  44. Thomas L. Griffiths & Joshua B. Tenenbaum (2007). Two Proposals for Causal Grammars. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. 323--345.
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  45. Joshua B. Tenenbaum, Thomas L. Griffiths & Sourabh Niyogi (2007). Intuitive Theories as Grammars for Causal Inference. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. 301--322.
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  46. Mark Steyvers, Thomas L. Griffiths & Simon Dennis (2006). Probabilistic Inference in Human Semantic Memory. Trends in Cognitive Sciences 10 (7):327-334.
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  47. Mark Steyvers, Thomas L. Griffiths & Simon Dennis (2006). Retrieving Effectively From Memory (REM). Trends in Cognitive Sciences 10 (7):327-334.
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  48. Joshua B. Tenenbaum, Thomas L. Griffiths & Charles Kemp (2006). Questions for Future Research. Trends in Cognitive Sciences 10 (7):309-318.
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  49. Joshua B. Tenenbaum, Thomas L. Griffiths & Charles Kemp (2006). Theory-Based Bayesian Models of Inductive Learning and Reasoning. Trends in Cognitive Sciences 10 (7):309-318.
  50. David Danks, Thomas L. Griffiths & Joshua B. Tenenbaum, Dynamical Causal Learning.
    Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets (though for different parameterizations), and a third through structural learning. This paper focuses on people’s short-run behavior by examining dynamical versions of these three theories, and comparing their predictions to a real-world dataset.
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