Beyond mind-reading: multi-voxel pattern analysis of fMRI data.

by: KA Norman, SM Polyn, GJ Detre, JV Haxby

Trends Cogn Sci, Vol. 10, No. 9. (September 2006), pp. 424-430.

View FullText article

DOI, Pubmed, Hubmed, ScienceDirect

Reviews [Write a review of this article]

There are no reviews of this article

Find related articles from these CiteULike users

oyen, librain, dianeoyen, brian, j_hsieh, dmd, Scis0000002, ilyajune (group), Foresight (group), schulz

Find related articles with these CiteULike tags

bound, case2, categorification, categorifications, classifications, coding, cognitive-dynamics, cognitive-space, cognitive-states, conceptualizations, distributed, distributed-representations, fmri, mental-space, metholodogy, mind-reading, mvpa, neural-codes, neurodynamics, neuro-semantics, no-tag, quals, review, svm, unified-concept-theory

Abstract

A key challenge for cognitive neuroscience is determining how mental representations map onto patterns of neural activity. Recently, researchers have started to address this question by applying sophisticated pattern-classification algorithms to distributed (multi-voxel) patterns of functional MRI data, with the goal of decoding the information that is represented in the subject's brain at a particular point in time. This multi-voxel pattern analysis (MVPA) approach has led to several impressive feats of mind reading. More importantly, MVPA methods constitute a useful new tool for advancing our understanding of neural information processing. We review how researchers are using MVPA methods to characterize neural coding and information processing in domains ranging from visual perception to memory search.

@article{citeulike:848369, abstract = {A key challenge for cognitive neuroscience is determining how mental representations map onto patterns of neural activity. Recently, researchers have started to address this question by applying sophisticated pattern-classification algorithms to distributed (multi-voxel) patterns of functional MRI data, with the goal of decoding the information that is represented in the subject's brain at a particular point in time. This multi-voxel pattern analysis (MVPA) approach has led to several impressive feats of mind reading. More importantly, MVPA methods constitute a useful new tool for advancing our understanding of neural information processing. We review how researchers are using MVPA methods to characterize neural coding and information processing in domains ranging from visual perception to memory search.}, address = {Department of Psychology, Princeton University, Green Hall, Washington Road, Princeton, NJ 08540, USA.}, author = {Norman, K. A. and Polyn, S. M. and Detre, G. J. and Haxby, J. V. }, citeulike-article-id = {848369}, doi = {10.1016/j.tics.2006.07.005}, issn = {1364-6613}, journal = {Trends Cogn Sci}, month = {September}, number = {9}, pages = {424--430}, posted-at = {2008-03-31 21:13:59}, priority = {2}, title = {Beyond mind-reading: multi-voxel pattern analysis of fMRI data.}, url = {http://dx.doi.org/10.1016/j.tics.2006.07.005}, volume = {10}, year = {2006} }

RIS record

TY - JOUR ID - citeulike:848369 L3 - citeulike-article-id:848369 TI - Beyond mind-reading: multi-voxel pattern analysis of fMRI data. JF - Trends Cogn Sci VL - 10 IS - 9 SP - 424 EP - 430 AD - Department of Psychology, Princeton University, Green Hall, Washington Road, Princeton, NJ 08540, USA. SN - 1364-6613 N2 - A key challenge for cognitive neuroscience is determining how mental representations map onto patterns of neural activity. Recently, researchers have started to address this question by applying sophisticated pattern-classification algorithms to distributed (multi-voxel) patterns of functional MRI data, with the goal of decoding the information that is represented in the subject's brain at a particular point in time. This multi-voxel pattern analysis (MVPA) approach has led to several impressive feats of mind reading. More importantly, MVPA methods constitute a useful new tool for advancing our understanding of neural information processing. We review how researchers are using MVPA methods to characterize neural coding and information processing in domains ranging from visual perception to memory search. AU - Norman, KA AU - Polyn, SM AU - Detre, GJ AU - Haxby, JV PY - 2006/09// UR - http://dx.doi.org/10.1016/j.tics.2006.07.005 ER -

RIS BibTeX