Abstract This paper proposes a unified framework describing human perception as a balance–feedback system operating within the Universal Balance–Feedback Framework (UBFF). While physical reality is fundamentally atomic and probabilistic, perception presents stable forms, continuous surfaces, and discrete colors. This transformation emerges from continuous feedback between external physical systems and internal neural processes. A concrete mathematical formulation is introduced — grounded in a weighted linear model with defined parameters — in which perception minimizes both representational error and computational complexity under a dual-objective optimization. Stability is analyzed via a Lyapunov condition on the error dynamics. The framework is explicitly differentiated from Friston's free-energy principle, with which it shares structural parallels but diverges in ontological grounding and scope. Applications are demonstrated across medicine, economics, and education with quantitative illustration. Experimental designs for empirical validation are proposed with defined variables and statistical procedures.