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Coarticulation of Handshape in Sign Language of the Netherlands: A Corpus Study

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      Abstract

      This article investigates the articulation of the thumb in flat handshapes (B handshapes) in Sign Language of the Netherlands. On the basis of phonological models of handshape, the hypothesis was generated that the thumb state is variable and will undergo coarticulatory influences of neighboring signs. This hypothesis was tested by investigating thumb articulation in signs with B handshapes that occur frequently in the Corpus NGT. Manual transcriptions were made of the thumb state in two dimensions and of the spreading of the fingers in a total of 728 tokens of 14 sign types, and likewise for the signs on the left and right of these targets, as produced by 61 signers. Linear mixed-effects regression (LME4) analyses showed a significant prediction of the thumb state in the target sign based on the thumb state in the preceding as well as following neighboring sign. Moreover, the degree of spreading of the other fingers in the target sign also influenced the position of the thumb. We conclude that there is evidence for phonological models of handshapes in sign languages that argue that not all fingers are relevant in all signs. Phonological feature specifications can single out specific fingers as the articulators, leaving other fingers unspecified. We thus argue that the standard term ‘handshape’ is in fact a misnomer, as it is typically not the shape of the whole hand that is specified in the lexicon.

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      Mixed-effects modeling with crossed random effects for subjects and items

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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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          Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology.

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            Author and article information

            Affiliations
            Centre for Language Studies, Radboud University, Nijmegen, NL
            Windesheim University of Applied Sciences, Zwolle, NL
            Contributors
            Journal
            1868-6354
            Laboratory Phonology
            Ubiquity Press
            1868-6354
            28 April 2017
            : 8
            : 1
            10.5334/labphon.45
            Copyright: © 2017 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/.

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