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      Bilingualism and language similarity modify the neural mechanisms of selective attention

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          Abstract

          Learning and using multiple languages places major demands on our neurocognitive system, which can impact the way the brain processes information. Here we investigated how early bilingualism influences the neural mechanisms of auditory selective attention, and whether this is further affected by the typological similarity between languages. We tested the neural encoding of continuous attended speech in early balanced bilinguals of typologically similar (Dutch-English) and dissimilar languages (Spanish-English) and compared them to results from English monolinguals we reported earlier. In a dichotic listening paradigm, participants attended to a narrative in their native language while ignoring different types of interference in the other ear. The results revealed that bilingualism modulates the neural mechanisms of selective attention even in the absence of consistent behavioural differences between monolinguals and bilinguals. They also suggested that typological similarity between languages helps fine-tune this modulation, reflecting life-long experiences with resolving competition between more or less similar candidates. The effects were consistent over the time-course of the narrative and suggest that learning a second language at an early age triggers neuroplastic adaptation of the attentional processing system.

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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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            The Psychophysics Toolbox

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              Nonparametric statistical testing of EEG- and MEG-data.

              In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons problem (MCP) and it allows to incorporate biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test. The paper is written for two audiences: (1) empirical neuroscientists looking for the most appropriate data analysis method, and (2) methodologists interested in the theoretical concepts behind nonparametric statistical tests. For the empirical neuroscientist, a large part of the paper is written in a tutorial-like fashion, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect. And for the methodologist, it is explained why the nonparametric test is formally correct. This means that we formulate a null hypothesis (identical probability distribution in the different experimental conditions) and show that the nonparametric test controls the false alarm rate under this null hypothesis.
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                Author and article information

                Contributors
                ako26@cam.ac.uk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                3 June 2019
                3 June 2019
                2019
                : 9
                : 8204
                Affiliations
                [1 ]ISNI 0000000121885934, GRID grid.5335.0, Department of Psychology, , University of Cambridge, ; Downing Street, Cambridge, CB2 3EB UK
                [2 ]ISNI 0000000121885934, GRID grid.5335.0, Department of Computer Science and Technology, , University of Cambridge, ; 15 JJ Thomson Ave, Cambridge, CB3 0FD UK
                [3 ]ISNI 0000000121885934, GRID grid.5335.0, Department of Theoretical and Applied Linguistics, , University of Cambridge, ; Sidgwick Avenue, Cambridge, CB3 9DA UK
                Author information
                http://orcid.org/0000-0001-5501-8628
                Article
                44782
                10.1038/s41598-019-44782-3
                6547874
                31160645
                414495a9-e687-42db-8b75-9a2637b547f3
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 February 2019
                : 21 May 2019
                Funding
                Funded by: Cambridge Language Sciences / Isaac Newton Trust
                Funded by: Cambridge Language Sciences / Isaac Newton Trust; Experimental Psychology Society
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                language,human behaviour
                Uncategorized
                language, human behaviour

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