Functional Network Constructed by Psychological Vectors: A Prospective Framework for Machine Consciousness

Abstract: While contemporary AI has advanced rapidly in perception, reasoning, and control, it still lacks a unified computational framework for intrinsic motivation, value judgment, holistic cognition, and adaptive mental mechanisms. This paper originally proposes and formally defines the novel concept of Functional Network (FN) —a computational architecture that takes psychological vectors as its fundamental representational units, operates with mental functions as its core, and employs computer technology to systematically express advanced psychological phenomena including perception, cognition, motivation, emotion, value, decision-making, and consciousness. The Functional Network shifts from "simulating brain structures" to "implementing mental functions." Building on psychological vectors, this paper integrates psychological energy, motivation, attention, value preferences, and adaptive behaviors into high-dimensional vectors and their interactive fields, constructing an engineering-feasible, iteratively developable architecture. Through systematic analysis of theoretical core, system architecture, feasibility, MVP roadmap, and future development, this paper demonstrates that under current conditions of neural networks, representation learning, RL, and computing power, the Functional Network possesses logical self-consistency, physical feasibility, and engineering implementability. Following a pragmatic "validate mechanisms first" approach, this paper proposes a phased MVP scheme and provides a forward-looking perspective on the path from Functional Networks to machine consciousness. Keywords: psychological vector; Functional Network; value vector; dual-channel cognition; meta-learning; Minimum Viable Product