Shannon entropy in visual perception predicts priming effects

When a system observes a visual signal under uncertainty, it weights a number of values probabilistically, where the maximum likelihood estimation (MLE) of the distribution corresponds to the best guess of the system regarding the true value of the observed stimulus. Perceptual uncertainty can therefore be assessed as the amount of variability in a Gaussian observation which is logically expected to increase in density as a function of time (number of exposures) and stimulus intensity (Norwich, 1977), thereby making the stimulus representation more accurate. Similar to how noise is modeled in stimulus representations during memory tasks, the amount of uncertainty in the observation can also simultaneously depend on whether the stimulus is perceived as a target or a distractor in a psychophysical task. We show that a framework incorporating both sources of noise can explain priming effects where the interference between two signals in a trial is given by the Kullback-Leibler (KL) divergence of their observations, with the initial stimulus treated as a reference distribution. Our model predicted response times in a perceptual shape discrimination task including items embedded in spatial noise that could unpredictably appear as a target or as a distractor. These results show that processing times of the second stimulus can be convincingly modeled as the statistical distance between the noise distributions of two consecutive stimuli.