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Memories for daily events require that individuals integrate initial fragile traces of events over time. Recent evidence suggests that reward anticipation enhances memory performance and amplifies frontal theta activity for remembered items vs. forgotten items. However, little is known about how incidental rewards after item presentation retrospectively modulate memory and the neural basis of this processing. Here, we used EEG combined with an incidental memory task to study how incidental reward association biased the post-encoding process. In the anticipatory stage, participants saw photos in win, loss and neutral contexts. Each photo was presented in a color frame that indicated the incentive condition (win vs. loss vs. neutral) and participants were asked to make a binary choice to predict whether the photo was associated with the left/right button. Feedback was presented to indicate arbitrary correctness and monetary outcomes. Recognition memory was tested after a short delay. During the encoding phase, left central-parietal theta power predicted subsequent memory performance in the win context. The post-encoding theta power at right central-frontal and central-parietal sites predicted later memory performance only in the win context. The size of frontal post-encoding related theta activity in the win context was correlated with the discriminate accuracy of the test stimulus. Our results suggest that post-encoding theta activity is closely linked to reward-based associative learning, providing evidence of a potential post-encoding mechanism of information binding.
Pubmed ID: 30630040
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Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
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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