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  1. Using Bayes to get the most out of non-significant results.Zoltan Dienes - 2014 - Frontiers in Psychology 5:85883.
    No scientific conclusion follows automatically from a statistically non-significant result, yet people routinely use non-significant results to guide conclusions about the status of theories (or the effectiveness of practices). To know whether a non-significant result counts against a theory, or if it just indicates data insensitivity, researchers must use one of: power, intervals (such as confidence or credibility intervals), or else an indicator of the relative evidence for one theory over another, such as a Bayes factor. I argue Bayes factors (...)
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  • The rationality of informal argumentation: A Bayesian approach to reasoning fallacies.Ulrike Hahn & Mike Oaksford - 2007 - Psychological Review 114 (3):704-732.
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  • Rational Irrationality: Modeling Climate Change Belief Polarization Using Bayesian Networks.John Cook & Stephan Lewandowsky - 2016 - Topics in Cognitive Science 8 (1):160-179.
    Belief polarization is said to occur when two people respond to the same evidence by updating their beliefs in opposite directions. This response is considered to be “irrational” because it involves contrary updating, a form of belief updating that appears to violate normatively optimal responding, as for example dictated by Bayes' theorem. In light of much evidence that people are capable of normatively optimal behavior, belief polarization presents a puzzling exception. We show that Bayesian networks, or Bayes nets, can simulate (...)
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  • The Appeal to Expert Opinion: Quantitative Support for a Bayesian Network Approach.Adam J. L. Harris, Ulrike Hahn, Jens K. Madsen & Anne S. Hsu - 2016 - Cognitive Science 40 (6):1496-1533.
    The appeal to expert opinion is an argument form that uses the verdict of an expert to support a position or hypothesis. A previous scheme-based treatment of the argument form is formalized within a Bayesian network that is able to capture the critical aspects of the argument form, including the central considerations of the expert's expertise and trustworthiness. We propose this as an appropriate normative framework for the argument form, enabling the development and testing of quantitative predictions as to how (...)
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  • Belief polarization is not always irrational.Alan Jern, Kai-min K. Chang & Charles Kemp - 2014 - Psychological Review 121 (2):206-224.
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  • Argument Content and Argument Source: An Exploration.Ulrike Hahn, Adam J. L. Harris & Adam Corner - 2009 - Informal Logic 29 (4):337-367.
    Argumentation is pervasive in everyday life. Understanding what makes a strong argument is therefore of both theoretical and practical interest. One factor that seems intuitively important to the strength of an argument is the reliability of the source providing it. Whilst traditional approaches to argument evaluation are silent on this issue, the Bayesian approach to argumentation (Hahn & Oaksford, 2007) is able to capture important aspects of source reliability. In particular, the Bayesian approach predicts that argument content and source reliability (...)
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  • Because Hitler did it! Quantitative tests of Bayesian argumentation using ad hominem.Adam J. L. Harris, Anne S. Hsu & Jens K. Madsen - 2012 - Thinking and Reasoning 18 (3):311 - 343.
    Bayesian probability has recently been proposed as a normative theory of argumentation. In this article, we provide a Bayesian formalisation of the ad Hitlerum argument, as a special case of the ad hominem argument. Across three experiments, we demonstrate that people's evaluation of the argument is sensitive to probabilistic factors deemed relevant on a Bayesian formalisation. Moreover, we provide the first parameter-free quantitative evidence in favour of the Bayesian approach to argumentation. Quantitative Bayesian prescriptions were derived from participants' stated subjective (...)
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  • Legal idioms: a framework for evidential reasoning.David A. Lagnado, Norman Fenton & Martin Neil - 2013 - Argument and Computation 4 (1):46 - 63.
    (2013). Legal idioms: a framework for evidential reasoning. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 46-63. doi: 10.1080/19462166.2012.682656.
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  • A Normative Theory of Argument Strength.Ulrike Hahn & Mike Oaksford - 2006 - Informal Logic 26 (1):1-24.
    In this article, we argue for the general importance of normative theories of argument strength. We also provide some evidence based on our recent work on the fallacies as to why Bayesian probability might, in fact, be able to supply such an account. In the remainder of the article we discuss the general characteristics that make a specifically Bayesian approach desirable, and critically evaluate putative flaws of Bayesian probability that have been raised in the argumentation literature.
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  • Dependencies in evidential reports: The case for informational advantages.Toby D. Pilditch, Ulrike Hahn, Norman Fenton & David Lagnado - 2020 - Cognition 204 (C):104343.
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  • Rational argument, rational inference.Ulrike Hahn, Adam J. L. Harris & Mike Oaksford - 2012 - Argument and Computation 4 (1):21 - 35.
    (2013). Rational argument, rational inference. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 21-35. doi: 10.1080/19462166.2012.689327.
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