A compressive sensing approach for inferring cognitive representations with reverse correlation

Uncovering cognitive representations is an elusive goal that is increasingly pursued using the reverse correlation method, wherein human subjects make judgments about ambiguous stimuli. Employing reverse correlation often entails collecting thousands of stimulus-response pairs, which severely limits the breadth of studies that are feasible using the method. Current techniques to improve efficiency bias the outcome. Here we show that this methodological barrier can be diminished using compressive sensing, an advanced signal processing technique designed to improve sampling efficiency. Simulations are performed to demonstrate that compressive sensing can improve the accuracy of reconstructed cognitive representations and dramatically reduce the required number of stimulus-response pairs. Additionally, compressive sensing is used on human subject data from a previous reverse correlation study, demonstrating a dramatic improvement in reconstruction quality. This work concludes by outlining the potential of compressive sensing to improve representation reconstruction throughout the fields of psychology, neuroscience, and beyond.