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      Brain Inhibitory Mechanisms Are Involved in the Processing of Sentential Negation, Regardless of Its Content. Evidence From EEG Theta and Beta Rhythms

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          Abstract

          The two-step process account of negation understanding posits an initial representation of the negated events, followed by a representation of the actual state of events. On the other hand, behavioral and neurophysiological studies provided evidence that linguistic negation suppresses or reduces the activation of the negated events, contributing to shift attention to the actual state of events. However, the specific mechanism of this suppression is poorly known. Recently, based on the brain organization principle of neural reuse ( Anderson, 2010), it has been proposed that understanding linguistic negation partially relies upon the neurophysiological mechanisms of response inhibition. Specifically, it was reported that negated action-related sentences modulate EEG signatures of response inhibition ( de Vega et al., 2016; Beltrán et al., 2018). In the current EEG study, we ponder whether the reusing of response inhibition processes by negation is constrained to action-related contents or consists of a more general-purpose mechanism. To this end, we employed the same dual-task paradigm as in our prior study—a Go/NoGo task embedded into a sentence comprehension task—but this time including both action and non-action sentences. The results confirmed that the increase of theta power elicited by NoGo trials was modulated by negative sentences, compared to their affirmative counterparts, and this polarity effect was statistically similar for both action- and non-action-related sentences. Thus, a general-purpose inhibitory control mechanism, rather than one specific for action language, is likely operating in the comprehension of sentential negation to produce the transition between alternative representations.

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          Most cited references 63

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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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            FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

            This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.
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              Inhibition and the right inferior frontal cortex: one decade on.

              In our TICS Review in 2004, we proposed that a sector of the right inferior frontal cortex (rIFC) in humans is critical for inhibiting response tendencies. Here we survey new evidence, discuss ongoing controversies, and provide an updated theory. We propose that the rIFC (along with one or more fronto-basal-ganglia networks) is best characterized as a brake. This brake can be turned on in different modes (totally, to outright suppress a response; or partially, to pause), and in different contexts (externally, by stop or salient signals; or internally, by goals). We affirm inhibition as a central component of executive control that relies upon the rIFC and associated networks, and explain why rIFC disruption could generally underpin response control disorders. Copyright © 2014 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                08 August 2019
                2019
                : 10
                Affiliations
                1Instituto Universitario de Neurociencia , Universidad de La Laguna, San Cristóbal de La Laguna, Spain
                2Departamento de Psicología Cognitiva , Universidad de La Laguna, San Cristóbal de La Laguna, Spain
                3Centro Asociado de La Laguna , Universidad Nacional de Educación a Distancia, Madrid, Spain
                4Facultad de Psicología , Universidad Europea de Canarias, Santa Cruz de Tenerife, Spain
                Author notes

                Edited by: Katharina Spalek, Humboldt University of Berlin, Germany

                Reviewed by: Jana Lüdtke, Freie Universität Berlin, Germany; Francesca Delogu, Saarland University, Germany

                *Correspondence: Manuel de Vega, mdevega@ 123456ull.edu.es

                This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2019.01782
                6694754
                Copyright © 2019 Beltrán, Morera, García-Marco and de Vega.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 3, Tables: 3, Equations: 0, References: 64, Pages: 14, Words: 0
                Funding
                Funded by: Ministerio de Economía, Industria y Competitividad, Gobierno de España 10.13039/501100010198
                Categories
                Psychology
                Original Research

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