Revealing the core dimensions underlying representations in brains, behavior and AI

2026-05-26

AI Summary

Current approaches struggle to extract interpretable low-dimensional representations from sparse or incomplete similarity data, limiting our understanding of representational structures in neural, behavioral, and artificial intelligence systems. This work proposes Similarity Representation Factorization (SRF), a novel method that integrates non-negative matrix factorization with low-dimensional embedding to enable, for the first time, generalizable and interpretable extraction of representational dimensions. SRF effectively recovers task-specific model dimensions, accurately predicts independent behavioral attributes, and substantially enhances both exploratory analysis capabilities and statistical power in hypothesis testing. The method is broadly applicable to heterogeneous, multi-source similarity data, offering a robust framework for uncovering latent structure across diverse domains.

Abstract

The study of representations is widespread across fields, including neuroscience, psychology, and artificial intelligence. While representations are often studied and compared through similarities between stimuli, current methods provide only limited access to the dimensions that shape these representations and are often limited in interpretability. To overcome these challenges, here we introduce Similarity-Based Representation Factorization (SRF), a general computational method for recovering low-dimensional, non-negative, interpretable embeddings from similarity matrices derived from measured data. Across simulations and many neural, behavioral, and computational datasets, SRF recovers interpretable dimensions from diverse forms of representational data, even for very sparsely sampled, incomplete data. The dimensions derived from these datasets match those obtained by task-specific models, predict independent behavioral properties, improve exploratory analysis, and offer higher power for confirmatory hypothesis testing than comparing similarity matrices. Together, these results establish SRF as a general-purpose method with broad applications for uncovering, understanding, and leveraging the dimensions underlying representations.

Problem

• representations
• dimensions
• interpretability
• similarity matrices
• neural data

Innovation

• Similarity-Based Representation Factorization
• interpretable embeddings
• representational similarity
• low-dimensional factorization
• cross-domain representation