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Previous studies suggest that deaf readers use phonological information of words when it is explicitly demanded by the task itself. However, whether phonological encoding is automatic remains controversial. The present experiment examined whether adult congenitally deaf readers show evidence of automatic use of phonological information during visual word recognition. In an ERP masked priming lexical decision experiment, deaf participants responded to target words preceded by a pseudohomophone (koral - CORAL) or an orthographic control prime (toral - CORAL). Responses were faster for the pseudohomophone than for the orthographic control condition. The N250 and N400 amplitudes were reduced for the pseudohomophone when compared to the orthographic control condition. Furthermore, the magnitude of both the behavioral and the ERP pseudohomophone effects in deaf readers was similar to that of a group of well-matched hearing controls. These findings reveal that phonological encoding is available to deaf readers from the early stages of visual word recognition. Finally, the pattern of correlations of phonological priming with reading ability suggested that the amount of sub-lexical use of phonological information could be a main contributor to reading ability for hearing but not for deaf readers.
Pubmed ID: 28987908
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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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