A Guide to Representational Similarity Analysis for Social Neuroscience. Issue 11 (2nd January 2020)

• Record Type: Journal Article
• Authors: Popal, Haroon; Wang, Yin; Olson, Ingrid R
• Is Part Of: Social cognitive and affective neuroscience. Volume 14:Issue 11(2019)
• Page Start: 1243
• Page End: 1253
• Publication Date: 2020-01-02
• Subjects: representational similarity analysis -- social neuroscience -- fMRI -- multivariate pattern analysis; Neurosciences -- Periodicals; Cognitive neuroscience -- Periodicals; Neuropsychology -- Periodicals; 612.8205
• DOI: 10.1093/scan/nsz099
• Languages: English
• ISSNs: 1749-5016

Abstract

Abstract: Representational similarity analysis (RSA) is a computational technique that uses pairwise comparisons of stimuli to reveal their representation in higher-order space. In the context of neuroimaging, mass-univariate analyses and other multivariate analyses can provide information on what and where information is represented but have limitations in their ability to address how information is represented. Social neuroscience is a field that can particularly benefit from incorporating RSA techniques to explore hypotheses regarding the representation of multidimensional data, how representations can predict behavior, how representations differ between groups and how multimodal data can be compared to inform theories. The goal of this paper is to provide a practical as well as theoretical guide to implementing RSA in social neuroscience studies.