Estimation method for higher-order judgment process with psychophysical reverse correlation

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Reverse correlation (RC) is known as a powerful method for probing internal computations in the brain, and it has been used in physiological researches. Psychophysical studies have also employed the RC analysis for understanding the computations underlying observers' judgments, but its application has been limited to the experiments with simple tasks and stimuli. This is because RC is a variation of white noise analysis, and it strongly restricts experimental stimuli. The present study proposes a method that enables us to explore nonlinear computations underlying psychophysical decision processes with arbitrary stimuli. This method provides unbiased estimates of internal computations without any restrictions on stimulus distributions. Numerical and psychophysical experiments confirmed the validity of the proposed method. This method could contribute to unveil the nature of higher-order cognitive functions which is desirable to present structured stimuli rather than simple noise input.