Searching across hundreds of databases
Violations of outcome expectancies have been proposed to account for error-related brain activity in the medial prefrontal cortex. The present study investigated whether early error monitoring processes are sensitive only to the expectancy of errors, or whether these processes also evaluate the significance of errors. To this end, we considered the error-related negativity (Ne/ERN), an electrophysiological marker of early error monitoring, in a modified flanker task in which errors could occur because participants responded to the flankers instead of the target (flanker error) or because a response unrelated to the stimulus was given (nonflanker error). By manipulating the onset of the flankers relative to the target, we manipulated two variables: the probability (and thus the expectancy) of flanker errors and the proportion of significant attention errors among each error type. Contrary to the predictions of outcome expectancy accounts, we found that the Ne/ERN was larger for flanker errors than for nonflanker errors only in the condition in which flanker errors were particularly frequent. Consistent with the error significance account, however, Ne/ERN amplitude mirrored the estimated proportion of significant attention errors as estimated by multinomial modeling. These results provide support for the idea that early performance monitoring as reflected by the Ne/ERN involves an evaluation of error significance.
Pubmed ID: 26481402 RIS Download
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Interactive 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
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
scicrunch.org
