@article{popal_guide_2019-1,
	title = {A {Guide} to {Representational} {Similarity} {Analysis} for {Social} {Neuroscience}},
	volume = {14},
	issn = {1749-5016},
	url = {https://doi.org/10.1093/scan/nsz099},
	doi = {10.1093/scan/nsz099},
	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.},
	number = {11},
	urldate = {2021-11-16},
	journal = {Social Cognitive and Affective Neuroscience},
	author = {Popal, Haroon and Wang, Yin and Olson, Ingrid R},
	month = nov,
	year = {2019},
	pages = {1243--1253},
}

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