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      Individual empathy levels affect gradual intonation-meaning mapping: The case of biased questions in Salerno Italian

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          Abstract

          The paper investigates the interplay between intonational cues and individual variability in the perceptual assessment of speakers’ epistemic bias in Salerno Italian yes-no questions. We present a perception experiment in which we manipulated pitch span within the nuclear configuration (both nuclear accent and boundary tone) to predict degree of perceived positive bias (i.e., expected positive answer) to yes-no question stimuli. Our results show that a wider pitch span within the nuclear region predicts a higher degree of perceived positive bias, while negative bias is predicted by narrow pitch span. Crucially, though, two interacting sources of listener variability were uncovered, i.e., prolonged exposure to a non-native dialect as well as degree of empathy (i.e., Empathy Quotient, EQ). Exposure to non-native phonological systems was found to affect the way pitch span is mapped onto perceived epistemic bias, through category interference, though mediated by EQ levels. Specifically, high-empathy listeners were more affected by degree of non-native dialect exposure. EQ scores were hence found to have an effect on gradual span manipulation by interacting with the dialect exposure effect. These results advance our understanding of the intonation-meaning mapping by taking into account both the impact of gradual phonetic cues on meaning processing as well as uncovering sources of cognitive variability at the perceiver’s level.

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          Simultaneous inference in general parametric models.

          Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R add-on package multcomp, which provides a convenient interface to the general approach adopted here. Copyright 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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            Random effects structure for confirmatory hypothesis testing: Keep it maximal.

            Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F 1 and F 2 tests, and in many cases, even worse than F 1 alone. Maximal LMEMs should be the 'gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.
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              OpenSesame: An open-source, graphical experiment builder for the social sciences

              In the present article, we introduce OpenSesame, a graphical experiment builder for the social sciences. OpenSesame is free, open-source, and cross-platform. It features a comprehensive and intuitive graphical user interface and supports Python scripting for complex tasks. Additional functionality, such as support for eyetrackers, input devices, and video playback, is available through plug-ins. OpenSesame can be used in combination with existing software for creating experiments.
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                Author and article information

                Contributors
                Journal
                1868-6354
                Laboratory Phonology: Journal of the Association for Laboratory Phonology
                Ubiquity Press
                1868-6354
                18 September 2020
                2020
                : 11
                : 1
                : 12
                Affiliations
                [1 ]Department of Humanities, University of Salerno, IT
                [2 ]Aix Marseille University, CNRS, Aix-en-Provence, FR
                [3 ]Department of Linguistics and Cognitive Science Center (RuCCS), New Brunswick, NJ, US
                Article
                10.5334/labphon.238
                dddc734f-94f5-4d2f-80dc-8076c6bdd094
                Copyright: © 2020 The Author(s)

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 October 2019
                : 08 August 2020
                Categories
                Journal article

                Applied linguistics,General linguistics,Linguistics & Semiotics
                Salerno Italian,Empathy Quotient,epistemic bias,Question intonation,pitch span,dialect contact

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