The Unreliable Influence of Noise Normalization on the Reliability of Neural Dissimilarity

Representational similarity analysis (RSA) is increasingly part of the standard analytic toolkit in neuroimaging. Core to RSA is the measuring of neural dissimilarity between the response patterns for different conditions to construct neural representational dissimilarity matrices (RDMs). It has been proposed that noise normalizing these patterns, and using crossvalidated distances as a dissimilarity measure, is superior for characterizing the structure of neural RDMs. This assessment has been motivated by improvement in within-subject neural dissimilarity after noise normalization. However, between-subject reliability is more directly related to determining the amount of explainable variance, and the evaluation of observed effect sizes when they are correlated with behavioral or model RDMs. Across three datasets we did not find that noise normalization consistently boosts within-subject reliability, between-subject reliability or correlations with behavioral or model RDMs. Overall, our results provide equivocal support for the utility of noise normalization to RSA.