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This paper discusses the visual perception for natural communication between a partner robot and a human. The prediction is very important to reduce the computational cost and to extract the perceptual information for the natural communication with a human in the future. Therefore we propose a prediction-based control of visual perception based on spiking neurons. The proposed method is composed of four layers: the input layer, clustering layer, prediction layer, and perceptual module selection layer. Next, we propose a competitive learning method to perform the clustering of human behavior patterns. Furthermore, the robot select perceptual modules used in the next perception according to the predicted perceptual mode. The results of prediction are evaluated based on the Gaussian membership function. Furthermore, we show experimental results of the communication between a partner robot and a human based on our proposal method.