Reconstruction of Partial Dissimilarity Matrices for Cognitive Neuroscience

TL;DR

This paper presents a fast, geometric inference-based algorithm for accurately reconstructing incomplete representational dissimilarity matrices in cognitive neuroscience, outperforming existing methods in efficiency and transparency.

Contribution

A novel, simple geometric algorithm for filling missing entries in dissimilarity matrices, offering a computationally efficient and transparent alternative to neural network imputation.

Findings

Effective across various datasets and sparsity levels

Robust to different matrix sizes and missing data patterns

Available as open-source Python and MATLAB tools

Abstract

In cognitive neuroscience research, Representational Dissimilarity Matrices (RDMs) are often incomplete because pairwise similarity judgments cannot always be exhaustively collected as the number of pairs rapidly increases with the number of conditions. Existing methods to fill these missing values, such as deep neural network imputation, are powerful but computationally demanding and relatively opaque. We introduce a simple algorithm based on geometric inference that fills missing dissimilarity matrix entries using known distances. We use tests on publicly available empirical cognitive neuroscience datasets, as well as simulations, to demonstrate the method's effectiveness and robustness across varying sparsity and matrix sizes. We have made this geometric reconstruction algorithm, implemented in Python and MATLAB, publicly available. This method provides a fast and accurate solution for completing partial dissimilarity matrices in the cognitive neurosciences.