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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

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