Recent behavioral studies showed that prior knowledge can directly influence visual perception. In the current work,we offer an explanation of the observed findings based on the adaptive resonance theory (ART). The ART neural network wasdesigned to solve the problem of catastrophic forgetting during learning in non-stationary environment. In the ART, stability oflearning is achieved by matching bottom-up sensory signals with top-down expectations. Resonant state that corresponds withconscious perception develops in the network when the bottom-up and top-down signals are closely aligned. On the other hand,mismatch produces global reset signal that clears the traces of erroneous top-down expectations. Therefore, prior knowledge caninfluence conscious perception only when it already closely matches with sensory signals. We performed computer simulationswith real-time implementation of the ART circuit that confirm our analysis. Simulations also showed how observed behavioralfindings arise from response bias.