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Efficient and inefficient visual search are characterized by the difference in the time required to find the target. Efficient "popout" search time is relatively unaffected by increases in the number of search items, whereas inefficient "non-popout" search time requires more time increases in duration. Electrophysiological investigations of the neural correlates of visual search have revealed a component of the event-related potential (ERP) known as the N2pc. The N2pc is thought to reflect the orienting of attention to the target during visual search. If the N2pc is more closely associated in time with the moment of target selection than the onset of the search display, it may be predicted that the N2pc would be delayed or more temporally variable during inefficient compared to efficient visual search. In the present study, we contrasted efficient "popout" search with inefficient "non-popout" search to investigate the hypothesis that the N2pc is temporally associated with the moment of search completion, and this would be reflected as a delayed or attenuated N2pc during non-popout search compared to popout. In Experiment 1, we observed a robust N2pc during popout search, but not during non-popout search. However, sorting trials by the N2pc latency recovered the N2pc during non-popout search. In Experiment 2 we replicated this observation and demonstrated that popout and non-popout search reflected differences in the temporal variability of the N2pc latency. Further investigation using time-spectral analysis suggests that evoked posterior-contralateral theta (4-6 Hz) underlies the N2pc during both popout and non-popout search.
Pubmed ID: 22743179 RIS Download
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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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