Results for 'Joshua B. Palmatier'

1000+ found
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  1.  65
    M-Zeroids: Structure and Categorical Equivalence.Joshua B. Palmatier & Fernando Guzman - 2012 - Studia Logica 100 (5):975-1000.
    In this note we develop a method for constructing finite totally-ordered m-zeroids and prove that there exists a categorical equivalence between the category of finite, totally-ordered m-zeroids and the category of pseudo Łukasiewicz-like implicators.
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  2.  79
    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.  71
    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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  4.  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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  5.  38
    Word Learning as Bayesian Inference.Fei Xu & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):245-272.
  6.  58
    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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  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.  23
    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.  6
    Some Specifics About Generalization.Joshua B. Tenenbaum & Thomas L. Griffiths - 2001 - Behavioral and Brain Sciences 24 (4):762-778.
  10.  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.
  11.  4
    The Child as Hacker.Joshua S. Rule, Joshua B. Tenenbaum & Steven T. Piantadosi - 2020 - Trends in Cognitive Sciences 24 (11):900-915.
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  12.  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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  13.  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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  14.  5
    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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  15.  16
    A Probabilistic Model of Cross-Categorization.Patrick Shafto, Charles Kemp, Vikash Mansinghka & Joshua B. Tenenbaum - 2011 - Cognition 120 (1):1-25.
  16.  24
    Rational Inference of Beliefs and Desires From Emotional Expressions.Yang Wu, Chris L. Baker, Joshua B. Tenenbaum & Laura E. Schulz - 2018 - Cognitive Science 42 (3):850-884.
    We investigated people's ability to infer others’ mental states from their emotional reactions, manipulating whether agents wanted, expected, and caused an outcome. Participants recovered agents’ desires throughout. When the agent observed, but did not cause the outcome, participants’ ability to recover the agent's beliefs depended on the evidence they got. When the agent caused the event, participants’ judgments also depended on the probability of the action ; when actions were improbable given the mental states, people failed to recover the agent's (...)
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  17.  90
    Defending Opioid Treatment Agreements: Disclosure, Not Promises.Joshua B. Rager & Peter H. Schwartz - 2017 - Hastings Center Report 47 (3):24-33.
    In order to receive controlled pain medications for chronic non-oncologic pain, patients often must sign a “narcotic contract” or “opioid treatment agreement” in which they promise not to give pills to others, use illegal drugs, or seek controlled medications from health care providers. In addition, they must agree to use the medication as prescribed and to come to the clinic for drug testing and pill counts. Patients acknowledge that if they violate the opioid treatment agreement, they may no longer receive (...)
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  18.  43
    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.
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  19.  44
    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.
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  20. 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.
  21.  57
    Action Understanding as Inverse Planning.Chris L. Baker, Rebecca Saxe & Joshua B. Tenenbaum - 2009 - Cognition 113 (3):329-349.
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  22.  15
    Topics in Semantic Representation.Thomas L. Griffiths, Mark Steyvers & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):211-244.
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  23.  7
    Decreased Modulation of EEG Oscillations in High-Functioning Autism During a Motor Control Task.Joshua B. Ewen, Balaji M. Lakshmanan, Ajay S. Pillai, Danielle McAuliffe, Carrie Nettles, Mark Hallett, Nathan E. Crone & Stewart H. Mostofsky - 2016 - Frontiers in Human Neuroscience 10.
  24.  22
    Theory-Based Causal Induction.Thomas L. Griffiths & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (4):661-716.
  25. 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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  26.  8
    Structured Statistical Models of Inductive Reasoning.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (1):20-58.
  27.  14
    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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  28.  51
    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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  29.  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.
  30.  41
    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.
  31.  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.
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  32.  5
    Learning a Theory of Causality.Noah D. Goodman, Tomer D. Ullman & Joshua B. Tenenbaum - 2011 - Psychological Review 118 (1):110-119.
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  33.  68
    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 (...)
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  34.  48
    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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  35.  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.
  36.  4
    “Structured Statistical Models of Inductive Reasoning”: Correction.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (2):461-461.
  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.  6
    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.
  39.  12
    Learning a Commonsense Moral Theory.Max Kleiman-Weiner, Rebecca Saxe & Joshua B. Tenenbaum - 2017 - Cognition 167:107-123.
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  40.  23
    From Mere Coincidences to Meaningful Discoveries.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - Cognition 103 (2):180-226.
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  41.  32
    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.
  42.  61
    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 (...)
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  43.  48
    Probabilistic Models of Cognition: Where Next?Nick Chater, Joshua B. Tenenbaum & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):292-293.
  44.  13
    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 (...)
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  45.  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.
  46. 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.
     
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  47.  26
    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.
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  48.  34
    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.
  49.  3
    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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  50.  6
    How the Brain’s Navigation System Shapes Our Visual Experience.Matthias Nau, Joshua B. Julian & Christian F. Doeller - 2018 - Trends in Cognitive Sciences 22 (9):810-825.
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