  This paper presents a distributed communication model to investigate
multistable perception, where a stimulus gives rise to multiple competing
perceptual interpretations. We formalize stable perception as consensus
achieved through components exchanging information. Our key finding is that
relationships between components influence monostable versus multistable
perceptions. When components contain substitute information about the
prediction target, stimuli display monostability. With complementary
information, multistability arises. We then analyze phenomena like order
effects and switching costs. Finally, we provide two additional perspectives.
An optimization perspective balances accuracy and communication costs, relating
stability to local optima. A Prediction market perspective highlights the
strategic behaviors of neural coordination and provides insights into phenomena
like rivalry, inhibition, and mental disorders. The two perspectives
demonstrate how relationships among components influence perception costs, and
impact competition and coordination behaviors in neural dynamics.
