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      Task Difficulty Regulates How Conscious and Unconscious Monetary Rewards Boost the Performance of Working Memory: An Event-Related Potential Study

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          Abstract

          The performance of working memory can be improved by the corresponding high-value vs. low-value rewards consciously or unconsciously. However, whether conscious and unconscious monetary rewards boosting the performance of working memory is regulated by the difficulty level of working memory task is unknown. In this study, a novel paradigm that consists of a reward-priming procedure and N-back task with differing levels of difficulty was designed to inspect this complex process. In particular, both high-value and low-value coins were presented consciously or unconsciously as the reward cues, followed by the N-back task, during which electroencephalogram signals were recorded. It was discovered that the high-value reward elicited larger event-related potential (ERP) component P3 along the parietal area (reflecting the working memory load) as compared to the low-value reward for the less difficult 1-back task, no matter whether the reward was unconsciously or consciously presented. In contrast, this is not the case for the more difficult 2-back task, in which the difference in P3 amplitude between the high-value and low-value rewards was not significant for the unconscious reward case, yet manifested significance for the conscious reward processing. Interestingly, the results of the behavioral analysis also exhibited very similar patterns as ERP patterns. Therefore, this study demonstrated that the difficulty level of a task can modulate the influence of unconscious reward on the performance of working memory.

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          Most cited references31

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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.
            • Record: found
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            Is Open Access

            ERPLAB: an open-source toolbox for the analysis of event-related potentials

            ERPLAB toolbox is a freely available, open-source toolbox for processing and analyzing event-related potential (ERP) data in the MATLAB environment. ERPLAB is closely integrated with EEGLAB, a popular open-source toolbox that provides many EEG preprocessing steps and an excellent user interface design. ERPLAB adds to EEGLAB’s EEG processing functions, providing additional tools for filtering, artifact detection, re-referencing, and sorting of events, among others. ERPLAB also provides robust tools for averaging EEG segments together to create averaged ERPs, for creating difference waves and other recombinations of ERP waveforms through algebraic expressions, for filtering and re-referencing the averaged ERPs, for plotting ERP waveforms and scalp maps, and for quantifying several types of amplitudes and latencies. ERPLAB’s tools can be accessed either from an easy-to-learn graphical user interface or from MATLAB scripts, and a command history function makes it easy for users with no programming experience to write scripts. Consequently, ERPLAB provides both ease of use and virtually unlimited power and flexibility, making it appropriate for the analysis of both simple and complex ERP experiments. Several forms of documentation are available, including a detailed user’s guide, a step-by-step tutorial, a scripting guide, and a set of video-based demonstrations.
              • Record: found
              • Abstract: not found
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              Working memory.

                Author and article information

                Contributors
                Journal
                Front Syst Neurosci
                Front Syst Neurosci
                Front. Syst. Neurosci.
                Frontiers in Systems Neuroscience
                Frontiers Media S.A.
                1662-5137
                13 January 2022
                2021
                : 15
                : 716961
                Affiliations
                [1] 1Faculty of Health Sciences, University of Macau , Taipa, Macao SAR, China
                [2] 2Centre for Cognitive and Brain Science, University of Macau , Shanghai, Macao SAR, China
                [3] 3Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence , Taipa, Macao SAR, China
                [4] 4Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University , Xi’an, China
                [5] 5Faculty of Arts and Humanities, University of Macau , Taipa, Macao SAR, China
                [6] 6Faculty of Social Sciences, University of Macau , Taipa, Macau SAR, China
                [7] 7Faculty of Education, University of Macau , Taipa, Macau SAR, China
                Author notes

                Edited by: James Joseph Chrobak, University of Connecticut, United States

                Reviewed by: Mingzhou Ding, University of Florida, United States; Yingchun Zhang, University of Houston, United States

                *Correspondence: Zhen Yuan, zhenyuan@ 123456um.edu.mo
                Article
                10.3389/fnsys.2021.716961
                8802761
                ddf65769-185c-4b01-9c7b-1729047796e1
                Copyright © 2022 Xu, Qi, Duan, Zhang, Akioma, Gao, Wu and Yuan.

                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.

                History
                : 29 May 2021
                : 29 October 2021
                Page count
                Figures: 5, Tables: 2, Equations: 2, References: 32, Pages: 10, Words: 6265
                Categories
                Systems Neuroscience
                Original Research

                Neurosciences
                n-back task,working memory,p3,monetary reward,task difficulty level
                Neurosciences
                n-back task, working memory, p3, monetary reward, task difficulty level

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