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.