Results for 'Josh B. Tenenbaum'

1000+ found
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  1.  10
    Computational Ethics.Edmond Awad, Sydney Levine, Michael Anderson, Susan Leigh Anderson, Vincent Conitzer, M. J. Crockett, Jim A. C. Everett, Theodoros Evgeniou, Alison Gopnik, Julian C. Jamison, Tae Wan Kim, S. Matthew Liao, Michelle N. Meyer, John Mikhail, Kweku Opoku-Agyemang, Jana Schaich Borg, Juliana Schroeder, Walter Sinnott-Armstrong, Marija Slavkovik & Josh B. Tenenbaum - 2022 - Trends in Cognitive Sciences 26 (5):388-405.
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  2.  49
    Word Learning as Bayesian Inference.Fei Xu & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):245-272.
  3.  99
    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.
  4.  85
    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 (...)
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  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.  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.
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  7.  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.
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  8.  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.
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  9.  27
    The Learnability of Abstract Syntactic Principles.Amy Perfors, Joshua B. Tenenbaum & Terry Regier - 2011 - Cognition 118 (3):306-338.
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  10.  21
    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.
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  11.  6
    A Theory of Learning to Infer.Ishita Dasgupta, Eric Schulz, Joshua B. Tenenbaum & Samuel J. Gershman - 2020 - Psychological Review 127 (3):412-441.
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  12.  46
    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.
  13. A-Theory for B-Theorists.Josh Parsons - 2002 - Philosophical Quarterly 52 (206):1-20.
    The debate between A-theory and B-theory in the philosophy of time is a persistent one. It is not always clear, however, what the terms of this debate are. A-theorists are often lumped with a miscellaneous collection of heterodox doctrines: the view that only the present exists, that time flows relentlessly, or that presentness is a property (Williams 1996); that time passes, tense is unanalysable, or that earlier than and later than are defined in terms of pastness, presentness, and futurity (Bigelow (...)
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  14.  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.
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  15.  10
    The Role of Causality in Judgment Under Uncertainty.Tevye R. Krynski & Joshua B. Tenenbaum - 2007 - Journal of Experimental Psychology: General 136 (3):430-450.
  16.  8
    Structured Statistical Models of Inductive Reasoning.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (1):20-58.
  17.  23
    Theory-Based Causal Induction.Thomas L. Griffiths & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (4):661-716.
  18.  8
    Graph Theoretic Analyses of Semantic Networks: Small Worlds in Semantic Networks.Mark Steyvers & Joshua B. Tenenbaum - 2005 - Cognitive Science 29 (1):41-78.
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  19.  50
    Three Ideal Observer Models for Rule Learning in Simple Languages.Michael C. Frank & Joshua B. Tenenbaum - 2011 - Cognition 120 (3):360-371.
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  20.  9
    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.  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.
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  22.  6
    The Child as Hacker.Joshua S. Rule, Joshua B. Tenenbaum & Steven T. Piantadosi - 2020 - Trends in Cognitive Sciences 24 (11):900-915.
  23.  65
    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.
    Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...)
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  24.  49
    Probabilistic Models of Cognition: Where Next?Nick Chater, Joshua B. Tenenbaum & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):292-293.
  25.  24
    A Probabilistic Model of Theory Formation.Charles Kemp, Joshua B. Tenenbaum, Sourabh Niyogi & Thomas L. Griffiths - 2010 - Cognition 114 (2):165-196.
  26.  8
    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.
  27.  4
    “Structured Statistical Models of Inductive Reasoning”: Correction.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (2):461-461.
  28.  27
    From Mere Coincidences to Meaningful Discoveries.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - Cognition 103 (2):180-226.
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  29.  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.
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  30.  7
    Inferring Mass in Complex Scenes by Mental Simulation.Jessica B. Hamrick, Peter W. Battaglia, Thomas L. Griffiths & Joshua B. Tenenbaum - 2016 - Cognition 157:61-76.
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  31.  6
    Some Specifics About Generalization.Joshua B. Tenenbaum & Thomas L. Griffiths - 2001 - Behavioral and Brain Sciences 24 (4):762-778.
  32.  63
    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 (...)
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  33. Probabilistic Models of Cognition: Where Next.N. Carter, J. B. Tenenbaum & A. Yuille - 2006 - Trends in Cognitive Sciences 10 (7):292-293.
     
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  34. 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.
  35.  18
    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.
  36. 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 (...)
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  37.  34
    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.
  38. Beyond Boolean Logic: Exploring Representation Languages for Learning Complex Concepts.Steven T. Piantadosi, Joshua B. Tenenbaum & Noah D. Goodman - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 859--864.
  39.  3
    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 ‘para- digms’ 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 (...)
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  40.  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.
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  41.  65
    Action Understanding as Inverse Planning.Chris L. Baker, Rebecca Saxe & Joshua B. Tenenbaum - 2009 - Cognition 113 (3):329-349.
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  42.  12
    Predicting the Future as Bayesian Inference: People Combine Prior Knowledge with Observations When Estimating Duration and Extent.Thomas L. Griffiths & Joshua B. Tenenbaum - 2011 - Journal of Experimental Psychology: General 140 (4):725-743.
  43.  22
    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.
  44.  11
    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.
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  45.  6
    Online Developmental Science to Foster Innovation, Access, and Impact.Mark Sheskin, Kimberly Scott, Candice M. Mills, Elika Bergelson, Elizabeth Bonawitz, Elizabeth S. Spelke, Li Fei-Fei, Frank C. Keil, Hyowon Gweon, Joshua B. Tenenbaum, Julian Jara-Ettinger, Karen E. Adolph, Marjorie Rhodes, Michael C. Frank, Samuel A. Mehr & Laura Schulz - 2020 - Trends in Cognitive Sciences 24 (9):675-678.
  46.  19
    Questions for Future Research.Joshua B. Tenenbaum, Thomas L. Griffiths & Charles Kemp - 2006 - Trends in Cognitive Sciences 10 (7):309-318.
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  47.  18
    Topics in Semantic Representation.Thomas L. Griffiths, Mark Steyvers & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):211-244.
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  48.  47
    Concepts Are Not Beliefs, but Having Concepts is Having Beliefs.Fei Xu, Joshua B. Tenenbaum & Cristina M. Sorrentino - 1998 - 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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  49.  7
    Randomness and Coincidences: Reconciling Intuition and Probability Theory.Thomas L. Griffiths & Joshua B. Tenenbaum - unknown
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  50.  40
    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.
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