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      The role of vowel length and glottalization in German learners’ perception of the English coda stop voicing contrast

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          Abstract

          In German, the voicing contrast in word-final stops is neutralized towards the voiceless sound. We tested how German learners of English use in perception two phonetic cues to this contrast in English: the duration of the vowel preceding the stop and the partial glottalization of this vowel. While a longer vowel cues the voiced sound of the contrast, glottalization enhances the voiceless sound, which should be ‘easy’ for learners as word-finally it is the default in German. We asked whether cueing the ‘easy’ sound would nevertheless affect learners’ word identification. Learners categorized two English minimal pairs along vowel duration continua with either a fully modal vowel or the last 25% of the vowel glottalized. Learners gave more voiced-stop responses as vowel duration increased. They also used glottalization by giving fewer voiced-stop responses for the glottalized continua. A second experiment demonstrated that the glottalization was not merely perceived as a change in the vowel+closure duration ratio. When the glottalized portion of the vowels was set to silence learners gave even fewer voiced-stop responses than in the glottalized condition. Results suggest that learners can use a phonetic cue to a second language sound contrast even if it enhances the familiar ‘easy’ sound.

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          Fitting Linear Mixed-Effects Models Using lme4

          Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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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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              PsychoPy—Psychophysics software in Python

              The vast majority of studies into visual processing are conducted using computer display technology. The current paper describes a new free suite of software tools designed to make this task easier, using the latest advances in hardware and software. PsychoPy is a platform-independent experimental control system written in the Python interpreted language using entirely free libraries. PsychoPy scripts are designed to be extremely easy to read and write, while retaining complete power for the user to customize the stimuli and environment. Tools are provided within the package to allow everything from stimulus presentation and response collection (from a wide range of devices) to simple data analysis such as psychometric function fitting. Most importantly, PsychoPy is highly extensible and the whole system can evolve via user contributions. If a user wants to add support for a particular stimulus, analysis or hardware device they can look at the code for existing examples, modify them and submit the modifications back into the package so that the whole community benefits.
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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
                24 October 2019
                2019
                : 10
                : 1
                : 18
                Affiliations
                [1 ]Institute of Phonetics and Speech Processing, Ludwig Maximilians University Munich, DE
                [2 ]Acoustics Research Institute, Austrian Academy of Sciences, Vienna, AT
                [3 ]Centre for Language Sciences, Department of Linguistics, Macquarie University, Sydney, AU
                Author information
                http://orcid.org/0000-0002-1400-5473
                Article
                10.5334/labphon.176
                8046e94d-eddf-4995-b833-19bb994800fb
                Copyright: © 2019 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
                : 22 October 2018
                : 08 October 2019
                Categories
                Journal article

                Applied linguistics,General linguistics,Linguistics & Semiotics
                acoustic cues,glottalization,speech perception,second language learning,word-final stop voicing

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