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Reverse Correlation of Natural Statistics for Ecologically-Relevant Characterization of Human Perceptual Templates.

Lorenzo Landolfi,Peter Neri

Published 2025 in Journal of Neurophysiology

ABSTRACT

Psychophysical reverse correlation is an established technique for retrieving 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 relevant when ecological validity 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 retrieval of human perceptual templates under ecologically valid 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.

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