Difference Between Rsa And Mvp Classification Analysis And...

1.1 What is representational similarity analysis? Representational similarity analysis (RSA) is an analysis framework builds on a rich psychological and mathematical literature, in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs. RDM is the representational dissimilarity matrix, which contains a cell for each pair of experimental conditions. Each cell is a number reflecting the dissimilarity between the activity patterns associated with the two conditions. The core of the of RSA is to use RDM as a signature of the representations in brain regions and computational models (Kriegeskorte, Mur, Bandettini, 2008). 1.2 The differences between RSA and MVP classification analysis and the new information that can be obtained from representational similarity analysis that is not revealed by MVP classification or univariate analysis. RSA is a particular versatile version of MVPA. It goes beyond testing of information in regional response patterns and enables researchers to handle condition-rich experiments without predefined stimulus categories, to test conceptual and computational models, and to relate representations between humans and monkeys, and even to related across different types of brain activity measurements. 1.2.1 Major differences: 1) MVP classification analysis focus on the representations of the brain associated with experimental