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In the present experiment we investigated the impact of cognitive load on feedback evaluation by a learning system within medial-frontal cortex. Participants completed a task in which they had to use feedback to learn to accurately estimate the duration of one second. In two experimental conditions, we manipulated the cognitive load of the feedback stimuli (low load versus high load). Our results revealed that the amplitude of the feedback error-related negatvity (fERN), a component of the event-related brain potential (ERP) thought to index a learning system within medial-frontal, was reduced in the high load condition. Further, an analysis of the behavioural data revealed that in the high load condition participants made less effective adjustments to their estimates following error feedback. Taken together, our data suggest that the functional efficacy of the medial-frontal learning system is reduced as the cognitive load of feeback signals increase. Moreover, our data indicate that the effect of increased cognitive load is to increase the trial-to-trial temporal variability of feedback stimulus evaluation.
Pubmed ID: 22099261
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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
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