A Compressive Sensing Approach to Inferring Cognitive Representations with Reverse Correlation

Abstract

Uncovering cognitive representations is an elusive goal that is increasingly pursued using the reverse correlation method. Employing reverse correlation often entails collecting thousands of stimulus-response pairs from human subjects, a burdensome task that limits the feasibility of many such studies. This methodological barrier can potentially be overcome using recent advances in signal processing designed to improve sampling efficiency, specifically compressive sensing. Here, compressive sensing is shown to be directly compatible with reverse correlation, and a trio of simulations are performed to demonstrate that compressive sensing can improve the accuracy of reconstructed representations while dramatically reducing the required number of samples. This work concludes by outlining the potential of compressive sensing to improve representation reconstruction throughout the field of neuroscience and beyond. Perceptual representations are uncovered more efficiently than ever before, removing a major barrier to cognitive studies.