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

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 d...

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Bibliographic Details
Published in:Trends in cognitive sciences Vol. 10; no. 9; pp. 424 - 430
Main Authors: Norman, Kenneth A., Polyn, Sean M., Detre, Greg J., Haxby, James V.
Format: Journal Article
Language:English
Published: London Elsevier Ltd 01.09.2006
Elsevier
Subjects:
Algorithms
Artificial Intelligence
Biological and medical sciences
Brain - physiology
Cognition - physiology
Discrimination Learning - physiology
Electroencephalography
Fundamental and applied biological sciences. Psychology
Humans
Image Processing, Computer-Assisted
Language
Magnetic Resonance Imaging
Memory - physiology
Mental Processes - physiology
Multivariate Analysis
Orientation - physiology
Pattern Recognition, Automated
Pattern Recognition, Visual - physiology
Production and perception of written language
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Signal Processing, Computer-Assisted
Visual Perception - physiology
Human
Data analysis
Language
Methodology
Mental representation
Cognition
Nuclear magnetic resonance imaging
Functional imaging
ISSN:1364-6613, 1879-307X
Online Access:Get full text
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Summary: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.
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ISSN:1364-6613
1879-307X
DOI:10.1016/j.tics.2006.07.005