विधियों की तुलना करें

	प्रतिनिधित्व समानता विश्लेषण	बहुचर पैटर्न विश्लेषण
क्षेत्र	तंत्रिका प्रतिबिंबन	तंत्रिका प्रतिबिंबन
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2008	2001
प्रवर्तक	Nikolaus Kriegeskorte	James V. Haxby
प्रकार	fMRI similarity structure comparison	fMRI pattern classification pipeline
मौलिक स्रोत	Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis—connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 4.	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.
उपनाम	RSA, representational geometry, similarity structure analysis	MVPA, brain decoding, pattern classification
संबंधित	3	3
सारांश	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.	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?