12 research outputs found

    Sticky central limit theorems on open books

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    Given a probability distribution on an open book (a metric space obtained by gluing a disjoint union of copies of a half-space along their boundary hyperplanes), we define a precise concept of when the Fr\'{e}chet mean (barycenter) is sticky. This nonclassical phenomenon is quantified by a law of large numbers (LLN) stating that the empirical mean eventually almost surely lies on the (codimension 11 and hence measure 00) spine that is the glued hyperplane, and a central limit theorem (CLT) stating that the limiting distribution is Gaussian and supported on the spine. We also state versions of the LLN and CLT for the cases where the mean is nonsticky (i.e., not lying on the spine) and partly sticky (i.e., is, on the spine but not sticky).Comment: Published in at http://dx.doi.org/10.1214/12-AAP899 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Categorization Reduces the Effect of Context On Hedonic Preference

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    When moderately hedonically positive test stimuli are presented following better-liked context stimuli, preferences between the test stimuli are reduced. This reduction in preference, hedonic condensation, occurs in settings that also produce negative hedonic contrast-the phenomenon in which moderately hedonically positive test stimuli seem less positive when they follow better-liked context stimuli. Subjects who were instructed that the context and test stimuli were from different categories exhibited less hedonic condensation. Those categories have smaller hedonic ranges than does the full stimulus set. The increase in preference magnitude with reduction in size of the hedonic range is predicted by Parducci\u27s (1995) range-frequency model
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