Characterizing the dynamics of mental representations: the temporal generalization method

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.

Journal

Title: Trends in Cognitive Sciences

Abbreviated Title: Trends in Cognitive Sciences

Publisher: Elsevier BV

ISSN (Print): 13646613

Publication date Created: April 2014

Publication date (Print): April 2014

Volume: 18

Issue: 4

Pages: 203-210

Article

DOI: 10.1016/j.tics.2014.01.002

PubMed ID: 24593982

Copyright statement: © 2014

License:

https://www.elsevier.com/tdm/userlicense/1.0/

Product

Self URI (article page): https://linkinghub.elsevier.com/retrieve/pii/S1364661314000199