Parsing a cognitive task into a sequence of operations is a central problem in cognitive
neuroscience. We argue that a major advance is now possible owing to the application
of pattern classifiers to time-resolved recordings of brain activity [electroencephalography
(EEG), magnetoencephalography (MEG), or intracranial recordings]. By testing at which
moment a specific mental content becomes decodable in brain activity, we can characterize
the time course of cognitive codes. Most importantly, the manner in which the trained
classifiers generalize across time, and from one experimental condition to another,
sheds light on the temporal organization of information-processing stages. A repertoire
of canonical dynamical patterns is observed across various experiments and brain regions.
This method thus provides a novel way to understand how mental representations are
manipulated and transformed.
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