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Poster Session B: Wednesday, August 13, 1:00 – 4:00 pm, de Brug & E‑Hall

An Equivalence Between Representational Similarity Analysis and Centered Kernel Alignment

Alex H Williams¹; ¹New York University

Presenter: Alex H Williams

Centered kernel alignment (CKA) and representational similarity analysis (RSA) of dissimilarity matrices are two popular methods for comparing neural systems in terms of representational geometry. Although they follow a conceptually similar approach, typical implementations of CKA and RSA tend to result in numerically different outcomes. Here, we show that these approaches become equivalent after incorporating a mean-centering step into RSA. This equivalence holds for both linear and nonlinear variants of these methods. By unifying these measures, this paper hopes to simplify a complex and fragmented literature on this subject.

Topic Area: Methods & Computational Tools

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