Roop, B. W. | Search bioRxiv

bioRxiv2022

Stimulus Whitening Improves the Efficiency of Reverse Correlation

Human perception depends upon internal representations of the environment that help to organize the raw information available from the senses by acting as reference patterns. Internal representations are widely characterized using reverse correlation, a method capable of producing unconstrained estimates of the representation itself, all on the basis of simple responses to random stimuli. Despite its advantages, reverse correlation is often infeasible to apply because the number of stimulus-response trials needed to provide an accurate estimate is typically very large. Prior approaches have aimed to overcome this sampling inefficiency by incorporating prior knowledge of the representation, which biases the estimate and ultimately limits the essential power of reverse correlation. The present approach, however, improves efficiency via stimulus whitening, a statistical procedure that decorrelates stimuli, making them less redundant, and commensurately more favorable for efficient estimation of an arbitrary target. We provide a mathematical justification for whitening, and demonstrate in simulation that whitening provides greater than 85% improvement in efficiency for a given estimation accuracy, and also a two- to five-fold increase in accuracy for a given sample size. Improving the efficiency of reverse correlation may enable a broader scope of investigations into individual variability and potential universality of perceptual mechanisms.

neuroscience↗

bioRxiv2021

A Compressive Sensing Approach to Inferring Cognitive Representations with Reverse Correlation

Uncovering high-level cognitive representations of categories such as faces is an elusive goal that has been frequently sought using reverse correlation, a technique employed in fields ranging from neurophysiology to cognitive psychology. In reverse correlation, subjects are asked to make perceptual judgments (e.g., "do you see a face?") about richly varying stimuli (e.g., white noise), and observed responses are then regressed against stimuli to yield reconstructions of the underlying cognitive representation. However, many thousands of stimulus-response pairs are frequently required, which severely limits the breadth of studies that are feasible using this powerful method. Techniques that are currently employed to improve efficiency, such as filtering the reconstruction, nevertheless bias the outcome. Here, we show that an advanced signal processing technique for improving sampling efficiency - compressive sensing - is directly compatible with reverse correlation. A trio of simulations are performed to demonstrate that compressive sensing can reduce the required stimulus-response pairs by up to 90% without biasing the reconstruction or can retrospectively improve the accuracy of the reconstructions on existing data. This work concludes by outlining the potential of compressive sensing to improve representation reconstruction throughout the field of neuroscience and beyond. Significance StatementUncovering cognitive representations is an elusive goal that is increasingly pursued using the reverse correlation method, wherein human subjects make judgments about vague 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. Here we show that this methodological barrier can be overcome using compressive sensing, an advanced signal processing technique designed to improve sampling efficiency. Three sets of 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. This work concludes by outlining the potential of compressive sensing to improve representation reconstruction throughout the field of neuroscience and beyond.

neuroscience↗