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      Normal congruency sequence effects in psychopathology: A behavioral and electrophysiological examination using a confound‐minimized design

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          Abstract

          Clinical studies of adaptive control emphasize the role disruptions in control play in psychopathology. However, many studies used confound‐laden designs and examined only one type of psychopathology. Recent studies of event‐related potentials (ERPs) suggest that robust congruency sequence effects (CSEs)—a popular index of adaptive control—appear in confound‐minimized designs. Thus, the present study sought to determine whether a confound‐minimized CSE paradigm could identify adaptive control dysfunction in people with major depressive disorder (MDD), generalized anxiety disorder (GAD), and obsessive‐compulsive disorder (OCD). We predicted that participants with MDD and GAD would show smaller ERP CSEs and that participants with OCD would show larger ERP CSEs than healthy controls. Data from 44 people with GAD, 51 people with MDD, 31 people with OCD, and 56 healthy comparison participants revealed normal CSEs as indexed by response times (RTs) and ERPs in the psychopathology groups. Moreover, psychiatric symptoms did not moderate these CSEs. Finally, we observed a strong mean–variance relationship in RT CSEs, such that participants with stronger post‐recruitment of control in mean RT scores showed the most consistent post‐conflict responses (i.e., the least intraindividual variability). These findings suggest that prior findings from confound‐laden tasks indicating altered CSEs in psychopathology stem from processes that are unrelated to adaptive control. Future research should employ experimental designs that isolate these processes to advance our understanding of abnormal CSEs in psychopathology.

          Abstract

          This study investigates adaptive control dysfunction in people with major depressive disorder (MDD), generalized anxiety disorder (GAD), and obsessive‐compulsive disorder (OCD) and healthy comparison participants using a confound‐minimized congruency sequence effects (CSEs) paradigm. The present findings show normal CSEs across groups, suggesting that altered CSEs in psychopathology may stem from unrelated processes. The study emphasizes the need to use confound‐minimized CSE designs to isolate control‐related processes to understand abnormal CSEs in psychopathology.

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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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            brms: An R Package for Bayesian Multilevel Models Using Stan

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              The Psychophysics Toolbox

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Psychophysiology
                Psychophysiology
                Wiley
                0048-5772
                1469-8986
                January 2024
                September 05 2023
                January 2024
                : 61
                : 1
                Affiliations
                [1 ] Department of Psychology University of South Florida Tampa Florida USA
                [2 ] Department of Psychology University of California – Davis Davis California USA
                [3 ] Department of Psychology Brigham Young University Provo Utah USA
                [4 ] Department of Psychology University of Michigan Ann Arbor Michigan USA
                [5 ] Neuroscience Center Brigham Young University Provo Utah USA
                Article
                10.1111/psyp.14426
                37668221
                84e8fcbb-e801-4254-a2e2-79215fb99993
                © 2024

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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