Abstract
One fundamental question is what makes two brain states similar. For example, what makes the activity in visual cortex elicited from viewing a robin similar to a sparrow? A common assumption, such as in Representation Similarity Analysis of fMRI data, is that neural similarity is described by Pearson correlation. However, any number of other similarity measures could instead hold, including Minkowski and Mahalanobis measures. The choice of measure is laden with mathematical, theoretical, neural computational assumptions that impact data interpretation. Here, we evaluated which of several competing similarity measures best capture neural similarity. The technique uses a classifier to assess the information present in a brain region and the similarity measure that best corresponds to the classifier’s confusion matrix is preferred. Across two published fMRI datasets, we found the preferred neural similarity measures were common across brain regions, but differed across tasks. Moreover, Pearson correlation was consistently surpassed by alternatives.
