Reverse correlation of natural statistics for ecologically relevant characterization of human perceptual templates.

Abstract

Psychophysical reverse correlation is an established technique for characterizing perceptual templates. Its application is best suited to a scenario in which 1) the human observer operates as a template matcher, and 2) the perceptual system is probed using radially symmetric noise, such as Gaussian white noise. When both conditions apply, the resulting estimate of the perceptual template directly reflects the actual template engaged by observers. However, when either condition fails, template estimates can be highly distorted to the point of becoming uninterpretable. This limitation is particularly pertinent when ecological relevance is under consideration because natural signals are clearly nothing like white noise. Template distortions associated with natural statistics may be corrected using a number of methods, many of which have been tested in single neurons, but none of which has been tested in human observers. We studied the applicability (or lack thereof) of five such methods to multiple experimental conditions under which the human visual system approaches a template matcher to different degrees of approximation. We find that methods based on minimizing/maximizing loss/information, such as logistic regression and maximally informative dimensions, outperform other approaches under the conditions of our experiments, and therefore represent promising tools for the characterization of human perceptual templates under ecologically relevant conditions. However, we also identify plausible scenarios under which those same approaches produce misleading outcomes, urging caution when interpreting results from those and related methods.NEW & NOTEWORTHY Reverse correlation is the method of choice for estimating neuronal/perceptual receptive fields, however, its applicability to natural behavior is hampered by the highly structured statistics of natural scenes. Although contemporary techniques for incorporating natural statistics have proven successful in neuronal settings, their applicability to psychophysical settings is unknown. We demonstrate that those techniques are indeed applicable to human observers, but with some important caveats that, if ignored, may lead to gross misinterpretations of the perceptual process.