Klindt, David, Acosta, Francisco, Conwell, Colin, Sanborn, Sophia, Miolane, Nina (December 2023) Evaluation of Representational Similarity Scores Across Human Visual Cortex. In: UniReps: the First Workshop on Unifying Representations in Neural Models.
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | organs, tissues, organelles, cell types and functions organs, tissues, organelles, cell types and functions > tissues types and functions organs, tissues, organelles, cell types and functions > tissues types and functions > visual cortex |
| CSHL Authors: | |
| Communities: | CSHL labs > Klindt lab |
| SWORD Depositor: | CSHL Elements |
| Depositing User: | CSHL Elements |
| Date: | 18 December 2023 |
| Date Deposited: | 11 Apr 2024 16:03 |
| Last Modified: | 11 Apr 2024 16:03 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/41507 |
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