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      ADVANCED SECOND LANGUAGE LEARNERS’ PERCEPTION OF LEXICAL TONE CONTRASTS

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          Abstract

          It is commonly believed that second language (L2) acquisition of lexical tones presents a major challenge for learners from nontonal language backgrounds. This belief is somewhat at odds with research that consistently shows beginning learners making quick gains through focused tone training, as well as research showing advanced learners achieving near-native performance in tone identification tasks. However, other long-term difficulties related to L2 tone perception may persist, given the additional demands of word recognition and the effects of context. In the current study, we used behavioral and event-related potential (ERP) experiments to test whether perception of Mandarin tones is difficult for advanced L2 learners in isolated syllables, disyllabic words in isolation, and disyllabic words in sentences. Stimuli were more naturalistic and challenging than in previous research. While L2 learners excelled at tone identification in isolated syllables, they performed with very low accuracy in rejecting disyllabic tonal nonwords in isolation and in sentences. We also report ERP data from critical mismatching words in sentences; while L2 listeners showed no significant differences in responses in any condition, trends were not inconsistent with the overall pattern in behavioral results of less sensitivity to tone mismatches than to semantic or segmental mismatches. We interpret these results as evidence that Mandarin tones are in fact difficult for advanced L2 learners. However, the difficulty is not due primarily to an inability to perceive tones phonetically, but instead is driven by the need to process tones lexically, especially in multisyllable words.

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          Fitting Linear Mixed-Effects Models Usinglme4

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            EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

            We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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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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                Author and article information

                Journal
                Studies in Second Language Acquisition
                Stud Second Lang Acquis
                Cambridge University Press (CUP)
                0272-2631
                1470-1545
                March 2019
                May 15 2018
                March 2019
                : 41
                : 1
                : 59-86
                Article
                10.1017/S0272263117000444
                eb7cfa5f-4e71-4ec4-a044-de09156192c4
                © 2019

                https://www.cambridge.org/core/terms

                https://www.cambridge.org/core/terms

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