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 of its inefficiency—a very large number of stimulus–response trials are required in order to obtain an accurate estimate. Here, we show that an important source of this inefficiency is small, yet nontrivial, correlations that occur by chance between randomly generated stimuli. We demonstrate in simulation that whitening stimuli to remove such correlations before eliciting responses provides greater than 85% improvement in efficiency for a given estimation quality, as well as a two- to fivefold increase in quality for a given sample size. Moreover, unlike conventional approaches, whitening improves the efficiency of reverse correlation without introducing bias into the estimate, or requiring prior knowledge of the target internal representation. Improving the efficiency of reverse correlation with whitening may enable a broader scope of investigations into the individual variability and potential universality of perceptual mechanisms.