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      Characterizing the dynamics of mental representations: the temporal generalization method

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      Trends in Cognitive Sciences

      Elsevier BV

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          Abstract

          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. Copyright © 2014 Elsevier Ltd. All rights reserved.

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          Author and article information

          Journal
          Trends in Cognitive Sciences
          Trends in Cognitive Sciences
          Elsevier BV
          13646613
          April 2014
          April 2014
          : 18
          : 4
          : 203-210
          Article
          10.1016/j.tics.2014.01.002
          24593982
          © 2014

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