Results for 'Joshua B. Grubbs'

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  1. Moral Grandstanding in Public Discourse: Status-Seeking Motives as a Potential Explanatory Mechanism in Predicting Conflict.Joshua B. Grubbs, Brandon Warmke, Justin Tosi, A. Shanti James & W. Keith Campbell - 2019 - PLoS ONE 14 (10).
    Public discourse is often caustic and conflict-filled. This trend seems to be particularly evident when the content of such discourse is around moral issues (broadly defined) and when the discourse occurs on social media. Several explanatory mechanisms for such conflict have been explored in recent psychological and social-science literatures. The present work sought to examine a potentially novel explanatory mechanism defined in philosophical literature: Moral Grandstanding. According to philosophical accounts, Moral Grandstanding is the use of moral talk to seek social (...)
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  2. Moral Grandstanding and Political Polarization: A Multi-Study Consideration.Joshua B. Grubbs, Brandon Warmke, Justin Tosi & A. Shanti James - 2020 - Journal of Research in Personality 88.
    The present work posits that social motives, particularly status seeking in the form of moral grandstanding, are likely at least partially to blame for elevated levels of affective polarization and ideological extremism in the U.S. In Study 1, results from both undergraduates (N = 981; Mean age = 19.4; SD = 2.1; 69.7% women) and a cross-section of U.S. adults matched to 2010 census norms (N = 1,063; Mean age = 48.20, SD = 16.38; 49.8% women) indicated that prestige-motived grandstanding (...)
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  3.  93
    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.  82
    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.  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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  6.  45
    Word Learning as Bayesian Inference.Fei Xu & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):245-272.
  7.  91
    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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  8.  8
    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.
  9. 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.
  10.  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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  11.  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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  12.  6
    Some Specifics About Generalization.Joshua B. Tenenbaum & Thomas L. Griffiths - 2001 - Behavioral and Brain Sciences 24 (4):762-778.
  13.  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.
  14.  8
    Structured Statistical Models of Inductive Reasoning.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (1):20-58.
  15.  27
    The Learnability of Abstract Syntactic Principles.Amy Perfors, Joshua B. Tenenbaum & Terry Regier - 2011 - Cognition 118 (3):306-338.
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  16.  20
    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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  17.  23
    Theory-Based Causal Induction.Thomas L. Griffiths & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (4):661-716.
  18.  45
    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.
  19.  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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  20.  26
    From Mere Coincidences to Meaningful Discoveries.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - Cognition 103 (2):180-226.
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  21.  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.
  22.  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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  23.  37
    Sleep Deprivation and Sustained Attention Performance: Integrating Mathematical and Cognitive Modeling.Glenn Gunzelmann, Joshua B. Gross, Kevin A. Gluck & David F. Dinges - 2009 - Cognitive Science 33 (5):880-910.
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  24.  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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  25.  49
    Probabilistic Models of Cognition: Where Next?Nick Chater, Joshua B. Tenenbaum & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):292-293.
  26.  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.
  27.  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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  28.  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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  29.  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.
  30.  24
    A Probabilistic Model of Theory Formation.Charles Kemp, Joshua B. Tenenbaum, Sourabh Niyogi & Thomas L. Griffiths - 2010 - Cognition 114 (2):165-196.
  31.  4
    “Structured Statistical Models of Inductive Reasoning”: Correction.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (2):461-461.
  32.  6
    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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  33.  53
    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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  34.  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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  35.  64
    Action Understanding as Inverse Planning.Chris L. Baker, Rebecca Saxe & Joshua B. Tenenbaum - 2009 - Cognition 113 (3):329-349.
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  36.  15
    Dissociating Intuitive Physics From Intuitive Psychology: Evidence From Williams Syndrome.Frederik S. Kamps, Joshua B. Julian, Peter Battaglia, Barbara Landau, Nancy Kanwisher & Daniel D. Dilks - 2017 - Cognition 168:146-153.
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  37. 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.
  38.  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 (...)
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  39.  4
    Rectilinear Edge Selectivity Is Insufficient to Explain the Category Selectivity of the Parahippocampal Place Area.Peter B. Bryan, Joshua B. Julian & Russell A. Epstein - 2016 - Frontiers in Human Neuroscience 10.
  40.  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.
  41.  85
    Why Blame?Mehmet Gurdal, Joshua B. Miller & Aldo Rustichini - unknown
    We provide experimental evidence that subjects blame others based on events they are not responsible for. In our experiment an agent chooses between a lottery and a safe asset; payment from the chosen option goes to a principal who then decides how much to allocate between the agent and a third party. We observe widespread blame: regardless of their choice, agents are blamed by principals for the outcome of the lottery, an event they are not responsible for. We provide an (...)
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  42.  19
    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.
  43. 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.
  44.  10
    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.
  45.  66
    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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  46. 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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  47.  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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  48.  17
    Topics in Semantic Representation.Thomas L. Griffiths, Mark Steyvers & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):211-244.
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  49.  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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  50.  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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