57 found
Order:
  1.  42
    Probabilistic Models of Cognition: Exploring Representations and Inductive Biases.Thomas L. Griffiths, Nick Chater, Charles Kemp, Amy Perfors & Joshua B. Tenenbaum - 2010 - Trends in Cognitive Sciences 14 (8):357-364.
    Direct download (3 more)  
     
    Export citation  
     
    My bibliography   56 citations  
  2.  37
    Theory-Based Bayesian Models of Inductive Learning and Reasoning.Joshua B. Tenenbaum, Thomas L. Griffiths & Charles Kemp - 2006 - Trends in Cognitive Sciences 10 (7):309-318.
  3.  12
    The Learnability of Abstract Syntactic Principles.Amy Perfors, Joshua B. Tenenbaum & Terry Regier - 2011 - Cognition 118 (3):306-338.
    Direct download (3 more)  
     
    Export citation  
     
    My bibliography   24 citations  
  4. A Tutorial Introduction to Bayesian Models of Cognitive Development.Amy Perfors, Joshua B. Tenenbaum, Thomas L. Griffiths & Fei Xu - 2011 - Cognition 120 (3):302-321.
  5.  20
    Action Understanding as Inverse Planning.Chris L. Baker, Rebecca Saxe & Joshua B. Tenenbaum - 2009 - Cognition 113 (3):329-349.
    Direct download (4 more)  
     
    Export citation  
     
    My bibliography   30 citations  
  6.  22
    One and Done? Optimal Decisions From Very Few Samples.Edward Vul, Noah Goodman, Thomas L. Griffiths & Joshua B. Tenenbaum - 2014 - 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 (...)
    Direct download (5 more)  
     
    Export citation  
     
    My bibliography   11 citations  
  7.  29
    Generalization, Similarity, and Bayesian Inference.Joshua B. Tenenbaum & Thomas L. Griffiths - 2001 - 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 (...)
    Direct download (5 more)  
     
    Export citation  
     
    My bibliography   47 citations  
  8.  6
    Inferring Causal Networks From Observations and Interventions.Mark Steyvers, Joshua B. Tenenbaum, Eric‐Jan Wagenmakers & Ben Blum - 2003 - Cognitive Science 27 (3):453-489.
    Direct download (7 more)  
     
    Export citation  
     
    My bibliography   36 citations  
  9.  6
    The Large‐Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth.Mark Steyvers & Joshua B. Tenenbaum - 2005 - Cognitive Science 29 (1):41-78.
    Direct download (6 more)  
     
    Export citation  
     
    My bibliography   29 citations  
  10.  8
    Modeling Human Performance in Statistical Word Segmentation.Michael C. Frank, Sharon Goldwater, Thomas L. Griffiths & Joshua B. Tenenbaum - 2010 - Cognition 117 (2):107-125.
    Direct download (4 more)  
     
    Export citation  
     
    My bibliography   16 citations  
  11. The Structure and Dynamics of Scientific Theories: A Hierarchical Bayesian Perspective.Henderson Leah, D. Goodman Noah, B. Tenenbaum Joshua & F. Woodward James - 2010 - Philosophy of Science 77 (2):172-200.
    Hierarchical Bayesian models (HBMs) provide an account of Bayesian inference in a hierarchically structured hypothesis space. Scientific theories are plausibly regarded as organized into hierarchies in many cases, with higher levels sometimes called ‘paradigms’ and lower levels encoding more specific or concrete hypotheses. Therefore, HBMs provide a useful model for scientific theory change, showing how higher‐level theory change may be driven by the impact of evidence on lower levels. HBMs capture features described in the Kuhnian tradition, particularly the idea that (...)
    Direct download (6 more)  
     
    Export citation  
     
    My bibliography   13 citations  
  12.  20
    The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology.Julian Jara-Ettinger, Hyowon Gweon, Laura E. Schulz & Joshua B. Tenenbaum - 2016 - Trends in Cognitive Sciences 20 (8):589-604.
  13.  4
    Word Learning as Bayesian Inference.Fei Xu & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):245-272.
    Direct download  
     
    Export citation  
     
    My bibliography   17 citations  
  14. Topics in Semantic Representation.Thomas L. Griffiths, Mark Steyvers & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):211-244.
    Direct download  
     
    Export citation  
     
    My bibliography   17 citations  
  15.  8
    Going Beyond the Evidence: Abstract Laws and Preschoolers’ Responses to Anomalous Data.Laura E. Schulz, Noah D. Goodman, Joshua B. Tenenbaum & Adrianna C. Jenkins - 2008 - Cognition 109 (2):211-223.
  16. Theory-Based Causal Induction.Thomas L. Griffiths & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (4):661-716.
    Direct download (2 more)  
     
    Export citation  
     
    My bibliography   11 citations  
  17.  39
    Learning to Learn Causal Models.Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum - 2010 - 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 (...)
    Direct download (9 more)  
     
    Export citation  
     
    My bibliography   9 citations  
  18. Children’s Understanding of the Costs and Rewards Underlying Rational Action.Julian Jara-Ettinger, Hyowon Gweon, Joshua B. Tenenbaum & Laura E. Schulz - 2015 - Cognition 140:14-23.
    Direct download  
     
    Export citation  
     
    My bibliography   3 citations  
  19.  14
    Three Ideal Observer Models for Rule Learning in Simple Languages.Michael C. Frank & Joshua B. Tenenbaum - 2011 - Cognition 120 (3):360-371.
    Direct download (4 more)  
     
    Export citation  
     
    My bibliography   7 citations  
  20.  12
    A Probabilistic Model of Theory Formation.Charles Kemp, Joshua B. Tenenbaum, Sourabh Niyogi & Thomas L. Griffiths - 2010 - Cognition 114 (2):165-196.
  21.  26
    Bootstrapping in a Language of Thought: A Formal Model of Numerical Concept Learning.Steven T. Piantadosi, Joshua B. Tenenbaum & Noah D. Goodman - 2012 - Cognition 123 (2):199-217.
  22.  2
    Structured Statistical Models of Inductive Reasoning.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (1):20-58.
    Direct download  
     
    Export citation  
     
    My bibliography   7 citations  
  23.  13
    The Imaginary Fundamentalists: The Unshocking Truth About Bayesian Cognitive Science.Nick Chater, Noah Goodman, Thomas L. Griffiths, Charles Kemp, Mike Oaksford & Joshua B. Tenenbaum - 2011 - 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.
    Direct download (3 more)  
     
    Export citation  
     
    My bibliography   5 citations  
  24.  17
    Intuitive Theories as Grammars for Causal Inference.Joshua B. Tenenbaum, Thomas L. Griffiths & Sourabh Niyogi - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. pp. 301--322.
    Direct download (2 more)  
     
    Export citation  
     
    My bibliography   8 citations  
  25.  29
    Two Proposals for Causal Grammars.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. pp. 323--345.
    Direct download (2 more)  
     
    Export citation  
     
    My bibliography   7 citations  
  26.  23
    Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults.Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum & Alison Gopnik - 2011 - 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 (...)
    Direct download (6 more)  
     
    Export citation  
     
    My bibliography   4 citations  
  27.  8
    A Probabilistic Model of Cross-Categorization.Patrick Shafto, Charles Kemp, Vikash Mansinghka & Joshua B. Tenenbaum - 2011 - Cognition 120 (1):1-25.
  28.  22
    Probabilistic Models of Cognition: Where Next?Nick Chater, Joshua B. Tenenbaum & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):292-293.
    Direct download (3 more)  
     
    Export citation  
     
    My bibliography   6 citations  
  29.  12
    From Mere Coincidences to Meaningful Discoveries.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - Cognition 103 (2):180-226.
    Direct download (4 more)  
     
    Export citation  
     
    My bibliography   6 citations  
  30.  8
    Inductive Reasoning About Causally Transmitted Properties.Patrick Shafto, Charles Kemp, Elizabeth Baraff Bonawitz, John D. Coley & Joshua B. Tenenbaum - 2008 - Cognition 109 (2):175-192.
  31.  7
    Cause and Intent: Social Reasoning in Causal Learning.Noah D. Goodman, Chris L. Baker & Joshua B. Tenenbaum - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 2759--2764.
    Direct download  
     
    Export citation  
     
    My bibliography   4 citations  
  32. Learning a Theory of Causality.Noah D. Goodman, Tomer D. Ullman & Joshua B. Tenenbaum - 2011 - Psychological Review 118 (1):110-119.
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    My bibliography   3 citations  
  33. The Role of Causality in Judgment Under Uncertainty.Tevye R. Krynski & Joshua B. Tenenbaum - 2007 - Journal of Experimental Psychology: General 136 (3):430-450.
  34.  20
    Learning Causal Schemata.Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum - 2007 - In McNamara D. S. & Trafton J. G. (eds.), Proceedings of the 29th Annual Cognitive Science Society. Cognitive Science Society. pp. 389--394.
    Direct download  
     
    Export citation  
     
    My bibliography   4 citations  
  35. Informative Communication in Word Production and Word Learning.Michael C. Frank, Noah D. Goodman, Peter Lai & Joshua B. Tenenbaum - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
     
    Export citation  
     
    My bibliography   3 citations  
  36.  4
    Inferring the Intentional States of Autonomous Virtual Agents.Peter C. Pantelis, Chris L. Baker, Steven A. Cholewiak, Kevin Sanik, Ari Weinstein, Chia-Chien Wu, Joshua B. Tenenbaum & Jacob Feldman - 2014 - Cognition 130 (3):360-379.
  37.  12
    Encoding Higher-Order Structure in Visual Working Memory: A Probabilistic Model.Timothy F. Brady & Joshua B. Tenenbaum - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 411--416.
    Direct download  
     
    Export citation  
     
    My bibliography   2 citations  
  38.  2
    Corrigendum to “Three Ideal Observer Models for Rule Learning in Simple Languages” [Cognition 120 360–371].Michael C. Frank & Joshua B. Tenenbaum - 2014 - Cognition 132 (3):501.
    Direct download (2 more)  
     
    Export citation  
     
    My bibliography   1 citation  
  39.  17
    How Tall is Tall? Compositionality, Statistics, and Gradable Adjectives.Lauren A. Schmidt, Noah D. Goodman, David Barner & Joshua B. Tenenbaum - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
    Direct download  
     
    Export citation  
     
    My bibliography   2 citations  
  40.  14
    Subject Index to Volume 29.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 - Cognitive Science 29:1093-1096.
    Direct download  
     
    Export citation  
     
    My bibliography   3 citations  
  41.  24
    Subjective Probability in a Nutshell.Nick Chater, Joshua B. Tenenbaum & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):287-291.
    Direct download  
     
    Export citation  
     
    My bibliography   1 citation  
  42. 1. Not a Sure Thing: Fitness, Probability, and Causation Not a Sure Thing: Fitness, Probability, and Causation (Pp. 147-171). [REVIEW]Denis M. Walsh, Leah Henderson, Noah D. Goodman, Joshua B. Tenenbaum, James F. Woodward, Hannes Leitgeb, Richard Pettigrew, Brad Weslake & John Kulvicki - 2010 - Philosophy of Science 77 (2).
  43.  3
    Some Specifics About Generalization.Joshua B. Tenenbaum & Thomas L. Griffiths - 2001 - Behavioral and Brain Sciences 24 (4):762-778.
  44.  19
    Structured Models of Semantic Cognition.Charles Kemp & Joshua B. Tenenbaum - 2008 - 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.
    Direct download (7 more)  
     
    Export citation  
     
    My bibliography   1 citation  
  45.  9
    Questions for Future Research.Joshua B. Tenenbaum, Thomas L. Griffiths & Charles Kemp - 2006 - Trends in Cognitive Sciences 10 (7):309-318.
    Direct download  
     
    Export citation  
     
    My bibliography   1 citation  
  46. “Structured Statistical Models of Inductive Reasoning”: Correction.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (2):461-461.
    Direct download  
     
    Export citation  
     
    My bibliography   1 citation  
  47.  15
    Dynamical Causal Learning.David Danks, Thomas L. Griffiths & Joshua B. Tenenbaum - unknown
    Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets, 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.
    Direct download (2 more)  
     
    Export citation  
     
    My bibliography   1 citation  
  48.  27
    Discovering Syntactic Hierarchies.Virginia Savova, Daniel Roy, Lauren Schmidt & Joshua B. Tenenbaum - unknown
    Direct download  
     
    Export citation  
     
    My bibliography  
  49.  18
    Rational Statistical Inference: A Critical Component for Word Learning.Fei Xu & Joshua B. Tenenbaum - 2001 - 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.
    Direct download (6 more)  
     
    Export citation  
     
    My bibliography   1 citation  
  50. Randomness and Coincidences: Reconciling Intuition and Probability Theory.Thomas L. Griffiths & Joshua B. Tenenbaum - unknown
    No categories
    Direct download  
     
    Export citation  
     
    My bibliography   1 citation  
1 — 50 / 57