Typically, in task-switching contexts individuals are slower and less accurate when repeating a task in mixed blocks compared to single-task blocks (mixing cost) and when switching to a new task compared to repeating a previous one (switch cost). Previous research has shown that distinct electrophysiological correlates underlie these two phenomena. However, this evidence is not a consistent result. The goal of this study was to better characterize differences between the control processes involved in mixing and switch costs. To this aim, we examined event-related potentials (ERPs) evoked during a cued task-switching experiment. In order to minimize the confounding effects of cognitive demands unrelated to task-switching, we asked participants to shift between two simple tasks (a letter identity task and a letter position task). The mixing cost was defined, in terms of ERPs, by contrasting repeat and single-task trials, whereas the ERP switch cost was obtained from the comparison of switch and repeat trials. Cue-locked ERPs showed that the mixing cost was mediated by two sustained components, an early posterior positivity and a late anterior negativity. On the other hand, the switch cost was associated with two early phasic positive components, one principally distributed over centro-parietal sites and the other located over left posterior sites. In target-locked ERPs the mixing cost was expressed by a frontal positivity, whereas the switch cost was expressed by a reduced parietal P3b. Overall, the results extend previous findings by providing elucidating ERP evidence on distinct proactive and reactive control processes involved in mixing and switch costs.
Pubmed ID: 27238463 RIS Download
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Software as collaborative, open source application dedicated to analysis of brain recordings: MEG, EEG, fNIRS, ECoG, depth electrodes and animal invasive neurophysiology. User-Friendly Application for MEG/EEG Analysis.
View all literature mentionsInteractive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. First developed on Matlab 5.3 under Linux, EEGLAB runs on Matlab v5 and higher under Linux, Unix, Windows, and Mac OS X (Matlab 7+ recommended). EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users'' transition from GUI-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics, both at the level of individual EEGLAB ''datasets'' and/or across a collection of datasets brought together in an EEGLAB ''studyset.'' For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG data. For creative research programmers and methods developers, EEGLAB offers an extensible, open-source platform through which they can share new methods with the world research community by publishing EEGLAB ''plug-in'' functions that appear automatically in the EEGLAB menu of users who download them. For example, novel EEGLAB plug-ins might be built and released to ''pick peaks'' in ERP or time/frequency results, or to perform specialized import/export, data visualization, or inverse source modeling of EEG, MEG, and/or ECOG data. EEGLAB Features * Graphic user interface * Multiformat data importing * High-density data scrolling * Defined EEG data structure * Open source plug-in facility * Interactive plotting functions * Semi-automated artifact removal * ICA & time/frequency transforms * Many advanced plug-in toolboxes * Event & channel location handling * Forward/inverse head/source modeling
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