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      Functional but not obligatory link between microsaccades and neural modulation by covert spatial attention

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      1 , , 2 , 3 , 1 , 3 ,
      Nature Communications
      Nature Publishing Group UK
      Attention, Human behaviour

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          Abstract

          Covert spatial attention is associated with spatial modulation of neural activity as well as with directional biases in fixational eye movements known as microsaccades. We studied how these two ‘fingerprints’ of attention are interrelated in humans. We investigated spatial modulation of 8-12 Hz EEG alpha activity and microsaccades when attention is directed internally within the spatial layout of visual working memory. Consistent with a common origin, spatial modulations of alpha activity and microsaccades co-vary: alpha lateralisation is stronger in trials with microsaccades toward versus away from the memorised location of the to-be-attended item and occurs earlier in trials with earlier microsaccades toward this item. Critically, however, trials without attention-driven microsaccades nevertheless show clear spatial modulation of alpha activity – comparable to trials with attention-driven microsaccades. Thus, directional biases in microsaccades correlate with neural signatures of spatial attention, but they are not necessary for neural modulation by spatial attention to be manifest.

          Abstract

          Covert visual spatial attention modulates neural activity and biases the direction of small eye movements, known as microsaccades. Here, the authors show that these two markers co-vary, but that microsaccades are not necessary for neural modulation by attention to occur.

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          Most cited references51

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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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            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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              Visual attention: the past 25 years.

              This review focuses on covert attention and how it alters early vision. I explain why attention is considered a selective process, the constructs of covert attention, spatial endogenous and exogenous attention, and feature-based attention. I explain how in the last 25 years research on attention has characterized the effects of covert attention on spatial filters and how attention influences the selection of stimuli of interest. This review includes the effects of spatial attention on discriminability and appearance in tasks mediated by contrast sensitivity and spatial resolution; the effects of feature-based attention on basic visual processes, and a comparison of the effects of spatial and feature-based attention. The emphasis of this review is on psychophysical studies, but relevant electrophysiological and neuroimaging studies and models regarding how and where neuronal responses are modulated are also discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                b.liu@vu.nl
                freek.van.ede@vu.nl
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                17 June 2022
                17 June 2022
                2022
                : 13
                : 3503
                Affiliations
                [1 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Institute for Brain and Behavior Amsterdam, Department of Experimental and Applied Psychology, , Vrije Universiteit Amsterdam, ; Amsterdam, The Netherlands
                [2 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Department of Experimental Psychology, , University of Oxford, ; Oxford, UK
                [3 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, , University of Oxford, ; Oxford, UK
                Author information
                http://orcid.org/0000-0002-7857-4456
                http://orcid.org/0000-0001-5762-2802
                http://orcid.org/0000-0002-7434-1751
                Article
                31217
                10.1038/s41467-022-31217-3
                9205986
                35715471
                78827d17-8ac4-4ef7-89c4-dcf7544a90df
                © The Author(s) 2022

                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
                : 16 November 2021
                : 8 June 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 850636
                Award ID: 850636
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010269, Wellcome Trust (Wellcome);
                Award ID: 220020448
                Award Recipient :
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                © The Author(s) 2022

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                attention,human behaviour
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                attention, human behaviour

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