Three Virtues of Similarity-based Multivariate Pattern Analysis : An example from the human object vision pathway

We present an fMRI investigation of object representation in the human ventral vision pathway highlighting three aspects of similarity analysis that make it especially useful for illuminating the representational content underlying neural activation patterns. First, similarity structures allow for an abstract depiction of representational content in a given brain region. This is demonstrated using hierarchical clustering and multidimensional scaling (MDS) of the dissimilarity matrices defined by our stimulus categories—female and male human faces, dog faces, monkey faces, chairs, shoes, and houses. For example, in ventral temporal (VT) cortex the similarity space was neatly divided into face and non-face regions. Within the face region of the MDS space, male and female human faces were closest to each other, and dog faces were closer to human faces than monkey faces. Within the non-face region of the abstract space, the smaller objects—shoes and chairs—were closer to each other than they were to houses. Second, similarity structures are independent of the data source. Dissimilarities among stimulus categories can be derived from behavioral measures, from stimulus models, or from neural activity patterns in different brain regions and different subjects. The similarity structures from these diverse sources all have the same dimensionality. This source independence allowed for the direct comparison of similarity structures across subjects (N = 16) and across three brain regions representing early–, mid–, and late–stages of the object vision pathway. Finally, similarity structures can change shape in well-ordered ways as the source of the dissimilarities changes—helping to illuminate how representational content is transformed along a neural pathway. By comparing similarity spaces from three regions along the ventral visual pathway, we demonstrate how the similarity structure transforms from an organization based on lowlevel visual features—as reflected by patterns in early visual cortex—to a more categorical representation in late object vision cortex with intermediate organization at the middle stage.