@inproceedings{
acosta2023evaluation,
title={Evaluation of Representational Similarity Scores Across Human Visual Cortex},
author={Francisco Acosta and Colin Conwell and Sophia Sanborn and David A. Klindt and Nina Miolane},
booktitle={UniReps:  the First Workshop on Unifying Representations in Neural Models},
year={2023},
url={https://openreview.net/forum?id=LhV3Ex8fky}
}
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.
Evaluation of Representational Similarity Scores Across Human Visual Cortex
Francisco Acosta Colin Conwell Sophia Sanborn David A. Klindt Nina Miolane