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  1. Steven T. Piantadosi, Joshua B. Tenenbaum & Noah D. Goodman (forthcoming). Bootstrapping in a Language of Thought: A Formal Model of Conceptual Change in Number Word Learning. Cognition.
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  2. Virginia Savova, Daniel Roy, Lauren Schmidt & Joshua B. Tenenbaum (forthcoming). Discovering Syntactic Hierarchies. Cognitive Science.
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  3. Peter C. Pantelis, Chris L. Baker, Steven A. Cholewiak, Kevin Sanik, Ari Weinstein, Chia-Chien Wu, Joshua B. Tenenbaum & Jacob Feldman (2014). Inferring the Intentional States of Autonomous Virtual Agents. Cognition 130 (3):360-379.
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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. Steven T. Piantadosi, Joshua B. Tenenbaum & Noah D. Goodman (2012). Bootstrapping in a Language of Thought: A Formal Model of Numerical Concept Learning. Cognition 123 (2):199-217.
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  6. 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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  7. Michael C. Frank & Joshua B. Tenenbaum (2011). Three Ideal Observer Models for Rule Learning in Simple Languages. Cognition 120 (3):360-371.
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  8. 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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  9. 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.
  10. Amy Perfors, Joshua B. Tenenbaum & Terry Regier (2011). The Learnability of Abstract Syntactic Principles. Cognition 118 (3):306-338.
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  11. Patrick Shafto, Charles Kemp, Vikash Mansinghka & Joshua B. Tenenbaum (2011). A Probabilistic Model of Cross-Categorization. Cognition 120 (1):1-25.
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  12. Timothy F. Brady & Joshua B. Tenenbaum (2010). Encoding Higher-Order Structure in Visual Working Memory: A Probabilistic Model. In. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. 411--416.
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  13. 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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  14. 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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  15. Leah Henderson, Noah D. Goodman, Joshua B. Tenenbaum & James F. Woodward (2010). The Structure and Dynamics of Scientific Theories: A Hierarchical Bayesian Perspective. Philosophy of Science 77 (2):172-200.
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  16. Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum (2010). Learning to Learn Causal Models. Cognitive Science 34 (7):1185-1243.
    Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the (...)
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  17. 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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  18. Steven T. Piantadosi, Joshua B. Tenenbaum & Noah D. Goodman (2010). Beyond Boolean Logic: Exploring Representation Languages for Learning Complex Concepts. In. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. 859--864.
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  19. Denis M. Walsh, Leah Henderson, Noah D. Goodman, Joshua B. Tenenbaum, James F. Woodward, Hannes Leitgeb, Richard Pettigrew, Brad Weslake & John Kulvicki (2010). 1. Not a Sure Thing: Fitness, Probability, and Causation Not a Sure Thing: Fitness, Probability, and Causation (Pp. 147-171). [REVIEW] Philosophy of Science 77 (2).
     
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  20. Chris L. Baker, Rebecca Saxe & Joshua B. Tenenbaum (2009). Action Understanding as Inverse Planning. Cognition 113 (3):329-349.
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  21. Michael C. Frank, Noah D. Goodman, Peter Lai & Joshua B. Tenenbaum (2009). Informative Communication in Word Production and Word Learning. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  22. Noah D. Goodman, Chris L. Baker & Joshua B. Tenenbaum (2009). Cause and Intent: Social Reasoning in Causal Learning. In. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. 2759--2764.
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  23. Lauren A. Schmidt, Noah D. Goodman, David Barner & Joshua B. Tenenbaum (2009). How Tall is Tall? Compositionality, Statistics, and Gradable Adjectives. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  24. 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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  25. Noah D. Goodman, Joshua B. Tenenbaum, Thomas L. Griffiths & Feldman & Jacob (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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  26. 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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  27. Charles Kemp & Joshua B. Tenenbaum (2008). Structured Models of Semantic Cognition. Behavioral and Brain Sciences 31 (6):717-718.
    Rogers & McClelland (R&M) criticize models that rely on structured representations such as categories, taxonomic hierarchies, and schemata, but we suggest that structured models can account for many of the phenomena that they describe. Structured approaches and parallel distributed processing (PDP) approaches operate at different levels of analysis, and may ultimately be compatible, but structured models seem more likely to offer immediate insight into many of the issues that R&M discuss.
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  28. Laura E. Schulz, Noah D. Goodman, Joshua B. Tenenbaum & Adrianna C. Jenkins (2008). Going Beyond the Evidence: Abstract Laws and Preschoolers' Responses to Anomalous Data. Cognition 109 (2):211-223.
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  29. Patrick Shafto, Charles Kemp, Elizabeth Baraff Bonawitz, John D. Coley & Joshua B. Tenenbaum (2008). Inductive Reasoning About Causally Transmitted Properties. Cognition 109 (2):175-192.
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  30. Thomas L. Griffiths & Joshua B. Tenenbaum (2007). From Mere Coincidences to Meaningful Discoveries. Cognition 103 (2):180-226.
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  31. 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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  32. Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum (2007). Learning Causal Schemata. In. In McNamara D. S. & Trafton J. G. (eds.), Proceedings of the 29th Annual Cognitive Science Society. Cognitive Science Society. 389--394.
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  33. 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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  34. Nick Chater, Joshua B. Tenenbaum & Alan Yuille (2006). Probabilistic Models of Cognition: Where Next? Trends in Cognitive Sciences 10 (7):292-293.
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  35. Nick Chater, Joshua B. Tenenbaum & Alan Yuille (2006). Subjective Probability in a Nutshell. Trends in Cognitive Sciences 10 (7):287-291.
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  36. 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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  37. 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.
  38. Robert L. Goldstone, Steven A. Sloman, David A. Lagnado, Mark Steyvers, Joshua B. Tenenbaum, Saskia Jaarsveld, Cees van Leeuwen, Murray Shanahan, Terry Dartnall & Simon Dennis (2005). Subject Index to Volume 29. Cognitive Science 29:1093-1096.
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  39. Mark Steyvers & Joshua B. Tenenbaum (2005). The Large‐Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth. Cognitive Science 29 (1):41-78.
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  40. 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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  41. Mark Steyvers, Joshua B. Tenenbaum, Eric‐Jan Wagenmakers & Ben Blum (2003). Inferring Causal Networks From Observations and Interventions. Cognitive Science 27 (3):453-489.
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  42. Joshua B. Tenenbaum & Thomas L. Griffiths (2001). Generalization, Similarity, and Bayesian Inference. Behavioral and Brain Sciences 24 (4):629-640.
    Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...)
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  43. Joshua B. Tenenbaum & Thomas L. Griffiths (2001). Some Specifics About Generalization. Behavioral and Brain Sciences 24 (4):762-778.
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  44. Fei Xu & Joshua B. Tenenbaum (2001). Rational Statistical Inference: A Critical Component for Word Learning. Behavioral and Brain Sciences 24 (6):1123-1124.
    In order to account for how children can generalize words beyond a very limited set of labeled examples, Bloom's proposal of word learning requires two extensions: a better understanding of the “general learning and memory abilities” involved, and a principled framework for integrating multiple conflicting constraints on word meaning. We propose a framework based on Bayesian statistical inference that meets both of those needs.
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  45. Fei Xu, Joshua B. Tenenbaum & Cristina M. Sorrentino (1998). Concepts Are Not Beliefs, but Having Concepts is Having Beliefs. Behavioral and Brain Sciences 21 (1):89-89.
    We applaud Millikan's psychologically plausible version of the causal theory of reference. Her proposal offers a significant clarification of the much-debated relation between concepts and beliefs, and suggests positive directions for future empirical studies of conceptual development. However, Millikan's revision of the causal theory may leave us with no generally satisfying account of concept individuation in the mind.
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