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      Left posterior temporal cortex is sensitive to syntax within conceptually matched Arabic expressions

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          Abstract

          During language comprehension, the brain processes not only word meanings, but also the grammatical structure—the “syntax”—that strings words into phrases and sentences. Yet the neural basis of syntax remains contentious, partly due to the elusiveness of experimental designs that vary structure independently of meaning-related variables. Here, we exploit Arabic’s grammatical properties, which enable such a design. We collected magnetoencephalography (MEG) data while participants read the same noun-adjective expressions with zero, one, or two contiguously-written definite articles (e.g., ‘ chair purple’; ‘ the-chair purple’; ‘ the-chair the-purple’), representing equivalent concepts, but with different levels of syntactic complexity (respectively, indefinite phrases: ‘ a purple chair’; sentences: ‘ The chair is purple.’; definite phrases: ‘ the purple chair’). We expected regions processing syntax to respond differently to simple versus complex structures. Single-word controls (‘ chair’/‘ purple’) addressed definiteness-based accounts. In noun-adjective expressions, syntactic complexity only modulated activity in the left posterior temporal lobe (LPTL), ~ 300 ms after each word’s onset: indefinite phrases induced more MEG-measured positive activity. The effects disappeared in single-word tokens, ruling out non-syntactic interpretations. In contrast, left anterior temporal lobe (LATL) activation was driven by meaning. Overall, the results support models implicating the LPTL in structure building and the LATL in early stages of conceptual combination.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            FreeSurfer.

            FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source. Copyright © 2012 Elsevier Inc. All rights reserved.
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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
                matar@nyu.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                30 March 2021
                30 March 2021
                2021
                : 11
                : 7181
                Affiliations
                [1 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, Department of Psychology, , New York University, ; New York, NY USA
                [2 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, Department of Linguistics, , New York University, ; New York, NY USA
                [3 ]GRID grid.440573.1, NYUAD Research Institute, , New York University Abu Dhabi, ; Abu Dhabi, UAE
                Article
                86474
                10.1038/s41598-021-86474-x
                8010046
                33785801
                b179b1e8-6853-44d9-a7c2-828a67df15eb
                © The Author(s) 2021

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 September 2020
                : 8 March 2021
                Funding
                Funded by: NYUAD Research Institute
                Award ID: G1001
                Categories
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                © The Author(s) 2021

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                language,reading
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                language, reading

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