A Computational Model of Anti-Bayesian Sensory Integration in the Size-Weight Illusion

Abstract

We propose a computational model for anti-Bayesian sensory integration of human behavioral actions and perception in the size–weight illusion (SWI). The SWI refers to the fact that people judge the smaller of two equally weighted objects to heavier when lifted. Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics. However, the SWI cannot be explained on the basis of Bayesian integration, and the nervous system is thought to use two entirely different mechanisms to integrate prior expectations with current sensory information about object weight. Our proposed model is defined as a state estimator, combining a Kalman filter and a H^∞ filter. As a result, the model not only predicted the anti-Bayesian estimation of the weight but also the Bayesian estimation of the motor behavior. Therefore, we hypothesize that the SWI is realized by a H^∞ filter and a Kalman filter.