Evaluation of Representational Similarity Scores Across Human Visual Cortex

Francisco Acosta, Colin Conwell, Sophia Sanborn, David A. Klindt, Nina Miolane

Keywords: Representational Dissimilarity, RSA, CKA, Shape Metrics, Riemannian Distance, Visual Cortex

TL;DR: We evaluate different representational dissimilarity measures by relating their geometry to the anatomical geometry of visual cortex

Abstract: We investigate several popular methods for quantifying the similarity between neural representations applied to a large-scale fMRI dataset of human ventral visual cortex. We focus on representational geometry as a framework for comparing various functionally-defined high-level regions of interest (ROIs) in the ventral stream. We benchmark Representational Similarity Analysis, Centered Kernel Alignment, and Generalized Shape Metrics. We explore how well the geometry implied by pairwise representational dissimilarity scores produced by each method matches the 2D anatomical geometry of visual cortex. Our results suggest that while these methods yield similar outcomes, Shape Metrics provide distances between representations whose relation to the anatomical geometry is most invariant across subjects. Our work establishes a criterion with which to compare methods for quantifying representational similarity with implications for studying the anatomical organization of high-level ventral visual cortex.

Track: Extended Abstract Track

Submission Number: 88