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      EEG alpha and pupil diameter reflect endogenous auditory attention switching and listening effort


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          Everyday environments often contain distracting competing talkers and background noise, requiring listeners to focus their attention on one acoustic source and reject others. During this auditory attention task, listeners may naturally interrupt their sustained attention and switch attended sources. The effort required to perform this attention switch has not been well studied in the context of competing continuous speech. In this work, we developed two variants of endogenous attention switching and a sustained attention control. We characterized these three experimental conditions under the context of decoding auditory attention, while simultaneously evaluating listening effort and neural markers of spatial‐audio cues. A least‐squares, electroencephalography (EEG)‐based, attention decoding algorithm was implemented across all conditions. It achieved an accuracy of 69.4% and 64.0% when computed over nonoverlapping 10 and 5‐s correlation windows, respectively. Both decoders illustrated smooth transitions in the attended talker prediction through switches at approximately half of the analysis window size (e.g., the mean lag taken across the two switch conditions was 2.2 s when the 5‐s correlation window was used). Expended listening effort, as measured by simultaneous EEG and pupillometry, was also a strong indicator of whether the listeners sustained attention or performed an endogenous attention switch (peak pupil diameter measure [ p = 0 . 034 ] and minimum parietal alpha power measure [ p = 0 . 016 ]). We additionally found evidence of talker spatial cues in the form of centrotemporal alpha power lateralization ( p = 0 . 0428 ). These results suggest that listener effort and spatial cues may be promising features to pursue in a decoding context, in addition to speech‐based features.


          We implemented two variants of attention switching during a “cocktail party” listening task that consisted of two simultaneously played audiobooks. Attention switching was studied under the context of decoding auditory attention, while simultaneously evaluating listening effort and neural markers of spatial‐audio cues. Pupil and alpha measures of effort indicate that listeners expend more effort in the at‐will switch condition than in a sustained‐attention control condition.

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          Most cited references58

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

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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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              When to use the Bonferroni correction.

              The Bonferroni correction adjusts probability (p) values because of the increased risk of a type I error when making multiple statistical tests. The routine use of this test has been criticised as deleterious to sound statistical judgment, testing the wrong hypothesis, and reducing the chance of a type I error but at the expense of a type II error; yet it remains popular in ophthalmic research. The purpose of this article was to survey the use of the Bonferroni correction in research articles published in three optometric journals, viz. Ophthalmic & Physiological Optics, Optometry & Vision Science, and Clinical & Experimental Optometry, and to provide advice to authors contemplating multiple testing.

                Author and article information

                Eur J Neurosci
                Eur J Neurosci
                The European Journal of Neuroscience
                John Wiley and Sons Inc. (Hoboken )
                16 February 2022
                March 2022
                : 55
                : 5 ( doiID: 10.1111/ejn.v55.5 )
                : 1262-1277
                [ 1 ] Human Health and Performance Systems MIT Lincoln Laboratory Lexington Massachusetts USA
                [ 2 ] Speech and Hearing Bioscience and Technology Harvard Medical School Boston Massachusetts USA
                Author notes
                [*] [* ] Correspondence

                Christopher J. Smalt, Human Health and Performance Systems, MIT Lincoln Laboratory, Lexington, MA 02421, USA.

                Email: Christopher.Smalt@ 123456ll.mit.edu

                © 2022 MIT Lincoln Laboratory. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                Page count
                Figures: 7, Tables: 0, Pages: 16, Words: 11604
                Funded by: National Institute of Health , doi 10.13039/501100003653;
                Award ID: T32 Trainee Grant No 5T32DC000038‐27
                Funded by: National Science Foundation , doi 10.13039/100000001;
                Award ID: GFRP Grant No. DGE1745303
                Funded by: Under Secretary of Defense for Research and Engineering
                Award ID: Air Force Contract No. FA8702‐15‐D‐0001
                Research Report
                Systems Neuroscience
                Custom metadata
                March 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.7 mode:remove_FC converted:22.07.2022

                alpha lateralization,auditory attention decoding,competing speech,event related spectral perturbation,simultaneous talkers


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