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Action execution is prone to errors and, while engaged in interaction, our brain is tuned to detect deviations from what one expects from other's action. Prior research has shown that Event-Related-Potentials (ERPs) are specifically modulated by the observation of action mistakes interfering with goal achievement. However, in complex and modular actions, embedded motor errors do not necessarily produce an immediate effect on the global goal. Here we dissociate embedded motor goals from global action goals by asking subjects to observe familiar but untrained knotting actions. During knotting an embedded motor error (i.e. the rope is inserted top-down instead of bottom-up during the formation of a loop) while not producing any immediate mistake, may strongly affect the final result. We found that embedded errors elicit in the observer specific early fronto-central negativity (120-180 ms). In a second experiment, we online administered exicitatory transcranial magnetic stimulation (TMS) over central (C3) or occipital (Oz) scalp locations, at the timing of the ERP components observed in the first experiment. C3 stimulation produced a significant improvement in embedded error discrimination performance. These results show that sensorimotor areas are instrumental in the early detection of embedded motor errors. We conclude that others' embedded errors provide fundamental cues which, inserted within a complex hierarchical action plan, might be used by the observer to anticipate whether an action will eventually fail.
Pubmed ID: 31614184
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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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