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  1.  54
    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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  2.  31
    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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  3.  11
    The Probabilistic Analysis of Language Acquisition: Theoretical, Computational, and Experimental Analysis.Anne S. Hsu, Nick Chater & Paul M. B. Vitányi - 2011 - Cognition 120 (3):380-390.
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  4.  50
    The Logical Problem of Language Acquisition: A Probabilistic Perspective.Anne S. Hsu & Nick Chater - 2010 - Cognitive Science 34 (6):972-1016.
    Natural language is full of patterns that appear to fit with general linguistic rules but are ungrammatical. There has been much debate over how children acquire these “linguistic restrictions,” and whether innate language knowledge is needed. Recently, it has been shown that restrictions in language can be learned asymptotically via probabilistic inference using the minimum description length (MDL) principle. Here, we extend the MDL approach to give a simple and practical methodology for estimating how much linguistic data are required to (...)
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  5.  67
    Language Learning From Positive Evidence, Reconsidered: A Simplicity-Based Approach.Anne S. Hsu, Nick Chater & Paul Vitányi - 2013 - Topics in Cognitive Science 5 (1):35-55.
    Children learn their native language by exposure to their linguistic and communicative environment, but apparently without requiring that their mistakes be corrected. Such learning from “positive evidence” has been viewed as raising “logical” problems for language acquisition. In particular, without correction, how is the child to recover from conjecturing an over-general grammar, which will be consistent with any sentence that the child hears? There have been many proposals concerning how this “logical problem” can be dissolved. In this study, we review (...)
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  6.  18
    Exploring Human Cognition Using Large Image Databases.Thomas L. Griffiths, Joshua T. Abbott & Anne S. Hsu - 2016 - Topics in Cognitive Science 8 (3):569-588.
    Most cognitive psychology experiments evaluate models of human cognition using a relatively small, well-controlled set of stimuli. This approach stands in contrast to current work in neuroscience, perception, and computer vision, which have begun to focus on using large databases of natural images. We argue that natural images provide a powerful tool for characterizing the statistical environment in which people operate, for better evaluating psychological theories, and for bringing the insights of cognitive science closer to real applications. We discuss how (...)
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  7.  37
    When Absence of Evidence Is Evidence of Absence: Rational Inferences From Absent Data.Anne S. Hsu, Andy Horng, Thomas L. Griffiths & Nick Chater - 2017 - Cognitive Science 41 (S5):1155-1167.
    Identifying patterns in the world requires noticing not only unusual occurrences, but also unusual absences. We examined how people learn from absences, manipulating the extent to which an absence is expected. People can make two types of inferences from the absence of an event: either the event is possible but has not yet occurred, or the event never occurs. A rational analysis using Bayesian inference predicts that inferences from absent data should depend on how much the absence is expected to (...)
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