Neural computational models are essential research methods for in-depth analysis of neural information encoding processes. In recent years, the normalization model has emerged as a novel neural computational approach and has found wide application in cognitive activities. The model posits that the response of neurons sensitive to a stimulus is suppressed by other neurons, with the computation involving a ratio between the response of an individual neuron and the summed activity of a neuron pool. While initially proposed to elucidate neuronal responses in the primary visual cortex, the normalization model has found extensive use in the realms of sensory perception and attention, yet its application to higher cognitive activities has been limited. Future studies exploring the potential of normalization models in more advanced cognitive domains would be a valuable avenue for research.