80 found
Order:
  1.  49
    Building Machines That Learn and Think Like People.Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum & Samuel J. Gershman - 2017 - Behavioral and Brain Sciences 40.
    No categories
    Direct download (6 more)  
     
    Export citation  
     
    Bookmark   49 citations  
  2. 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.
  3.  44
    Word Learning as Bayesian Inference.Fei Xu & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):245-272.
  4.  88
    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.
  5. 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.
  6.  64
    Action Understanding as Inverse Planning.Chris L. Baker, Rebecca Saxe & Joshua B. Tenenbaum - 2009 - Cognition 113 (3):329-349.
    No categories
    Direct download (6 more)  
     
    Export citation  
     
    Bookmark   64 citations  
  7.  62
    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 (...)
    No categories
    Direct download (12 more)  
     
    Export citation  
     
    Bookmark   34 citations  
  8.  16
    Topics in Semantic Representation.Thomas L. Griffiths, Mark Steyvers & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):211-244.
    Direct download  
     
    Export citation  
     
    Bookmark   83 citations  
  9.  77
    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 (6 more)  
     
    Export citation  
     
    Bookmark   94 citations  
  10.  23
    Theory-Based Causal Induction.Thomas L. Griffiths & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (4):661-716.
  11.  33
    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  
     
    Bookmark   76 citations  
  12.  32
    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.  27
    The Learnability of Abstract Syntactic Principles.Amy Perfors, Joshua B. Tenenbaum & Terry Regier - 2011 - Cognition 118 (3):306-338.
    Direct download (4 more)  
     
    Export citation  
     
    Bookmark   40 citations  
  14. The Structure and Dynamics of Scientific Theories: A Hierarchical Bayesian Perspective.Leah Henderson, Noah D. Goodman, Joshua B. Tenenbaum & James F. Woodward - 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  
     
    Bookmark   30 citations  
  15.  24
    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.
    No categories
    Direct download (11 more)  
     
    Export citation  
     
    Bookmark   62 citations  
  16.  17
    The Logical Primitives of Thought: Empirical Foundations for Compositional Cognitive Models.Steven T. Piantadosi, Joshua B. Tenenbaum & Noah D. Goodman - 2016 - Psychological Review 123 (4):392-424.
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   18 citations  
  17.  61
    Modeling Human Performance in Statistical Word Segmentation.Michael C. Frank, Sharon Goldwater, Thomas L. Griffiths & Joshua B. Tenenbaum - 2010 - Cognition 117 (2):107-125.
    No categories
    Direct download (5 more)  
     
    Export citation  
     
    Bookmark   30 citations  
  18.  8
    Structured Statistical Models of Inductive Reasoning.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (1):20-58.
  19.  53
    A Critical Period for Second Language Acquisition: Evidence From 2/3 Million English Speakers.Joshua K. Hartshorne, Joshua B. Tenenbaum & Steven Pinker - 2018 - Cognition 177:263-277.
    No categories
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark   6 citations  
  20.  8
    A Rational Analysis of Rule-Based Concept Learning.Noah D. Goodman, Joshua B. Tenenbaum, Jacob Feldman & Thomas L. Griffiths - 2008 - Cognitive Science 32 (1):108-154.
  21.  43
    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.  8
    Bayesian Collective Learning Emerges From Heuristic Social Learning.P. M. Krafft, Erez Shmueli, Thomas L. Griffiths, Joshua B. Tenenbaum & Alex “Sandy” Pentland - 2021 - Cognition 212:104469.
  23.  5
    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 (7 more)  
     
    Export citation  
     
    Bookmark   25 citations  
  24.  28
    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 (1):1093-1096.
    No categories
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark   26 citations  
  25.  69
    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  
     
    Bookmark   15 citations  
  26.  18
    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.
  27.  17
    Mind Games: Game Engines as an Architecture for Intuitive Physics.Tomer D. Ullman, Elizabeth Spelke, Peter Battaglia & Joshua B. Tenenbaum - 2017 - Trends in Cognitive Sciences 21 (9):649-665.
  28.  33
    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 (5 more)  
     
    Export citation  
     
    Bookmark   14 citations  
  29.  50
    Three Ideal Observer Models for Rule Learning in Simple Languages.Michael C. Frank & Joshua B. Tenenbaum - 2011 - Cognition 120 (3):360-371.
    No categories
    Direct download (5 more)  
     
    Export citation  
     
    Bookmark   12 citations  
  30.  16
    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.
    No categories
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark   9 citations  
  31.  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.
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   26 citations  
  32.  9
    The Role of Causality in Judgment Under Uncertainty.Tevye R. Krynski & Joshua B. Tenenbaum - 2007 - Journal of Experimental Psychology: General 136 (3):430-450.
  33.  8
    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 (10):785.
  34.  9
    Lucky or Clever? From Expectations to Responsibility Judgments.Tobias Gerstenberg, Tomer D. Ullman, Jonas Nagel, Max Kleiman-Weiner, David A. Lagnado & Joshua B. Tenenbaum - 2018 - Cognition 177:122-141.
    No categories
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark   4 citations  
  35.  13
    Learning a Commonsense Moral Theory.Max Kleiman-Weiner, Rebecca Saxe & Joshua B. Tenenbaum - 2017 - Cognition 167:107-123.
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   5 citations  
  36.  26
    From Mere Coincidences to Meaningful Discoveries.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - Cognition 103 (2):180-226.
    No categories
    Direct download (7 more)  
     
    Export citation  
     
    Bookmark   17 citations  
  37.  24
    A Probabilistic Model of Theory Formation.Charles Kemp, Joshua B. Tenenbaum, Sourabh Niyogi & Thomas L. Griffiths - 2010 - Cognition 114 (2):165-196.
  38.  36
    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.
  39.  4
    “Structured Statistical Models of Inductive Reasoning”: Correction.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (2):461-461.
  40.  7
    A Probabilistic Model of Visual Working Memory: Incorporating Higher Order Regularities Into Working Memory Capacity Estimates.Timothy F. Brady & Joshua B. Tenenbaum - 2013 - Psychological Review 120 (1):85-109.
  41.  5
    Graph Theoretic Analyses of Semantic Networks: Small Worlds in Semantic Networks.Mark Steyvers & Joshua B. Tenenbaum - 2005 - Cognitive Science 29 (1):41-78.
    No categories
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark   25 citations  
  42.  63
    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 (13 more)  
     
    Export citation  
     
    Bookmark   9 citations  
  43.  27
    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  
     
    Bookmark   14 citations  
  44.  14
    The Emergence of Organizing Structure in Conceptual Representation.Brenden M. Lake, Neil D. Lawrence & Joshua B. Tenenbaum - 2018 - Cognitive Science 42 (S3):809-832.
    Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form—where form could be a tree, ring, chain, grid, etc.. Although this approach can learn intuitive organizations, including a tree for animals and a ring for the color circle, it assumes a strong inductive bias that considers only these particular forms, and each form is explicitly provided as initial knowledge. Here we (...)
    No categories
    Direct download (6 more)  
     
    Export citation  
     
    Bookmark   3 citations  
  45.  48
    Probabilistic Models of Cognition: Where Next?Nick Chater, Joshua B. Tenenbaum & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):292-293.
  46.  33
    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  
     
    Bookmark   12 citations  
  47.  5
    A Theory of Learning to Infer.Ishita Dasgupta, Eric Schulz, Joshua B. Tenenbaum & Samuel J. Gershman - 2020 - Psychological Review 127 (3):412-441.
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   3 citations  
  48.  23
    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.
  49.  16
    A Probabilistic Model of Cross-Categorization.Patrick Shafto, Charles Kemp, Vikash Mansinghka & Joshua B. Tenenbaum - 2011 - Cognition 120 (1):1-25.
  50.  6
    The Child as Hacker.Joshua S. Rule, Joshua B. Tenenbaum & Steven T. Piantadosi - 2020 - Trends in Cognitive Sciences 24 (11):900-915.
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   1 citation  
1 — 50 / 80