विधियों की तुलना करें

	बहुचर पैटर्न विश्लेषण	प्रतिनिधित्व समानता विश्लेषण
क्षेत्र	तंत्रिका प्रतिबिंबन	तंत्रिका प्रतिबिंबन
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2001	2008
प्रवर्तक	James V. Haxby	Nikolaus Kriegeskorte
प्रकार	fMRI pattern classification pipeline	fMRI similarity structure comparison
मौलिक स्रोत	Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430.	Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis—connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 4.
उपनाम	MVPA, brain decoding, pattern classification	RSA, representational geometry, similarity structure analysis
संबंधित	3	3
सारांश	Multivariate Pattern Analysis (MVPA) is a machine learning approach to fMRI that decodes cognitive states, stimuli, or behavior from whole-brain spatial patterns of neural activity. Pioneered by Haxby and colleagues in 2001, MVPA treats fMRI as a classification problem: can a trained decoder predict what a person is perceiving or thinking based solely on their brain activity pattern?	Representational Similarity Analysis (RSA) is a framework for comparing representational geometry across brain regions, computational models, and behavioral measures. Introduced by Kriegeskorte and colleagues in 2008, RSA measures how similarly a brain region represents different stimuli or concepts by examining pairwise similarity structure rather than absolute activity patterns.